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New Publications are available now online for this publication.
Please follow the links to view the publication.Fractional derivative filter for image contrast enhancement with order prediction
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0432
Fractional derivative based techniques have been proposed for preprocessing of digital images. Although these techniques address the texture enhancement and other issues to a certain extent, none of them have proposed a method of determining the fractional order adaptively. In this paper, we propose a Grunwald-Letnikov derivative based fractional derivative mask for image contrast enhancement. The proposed mask is multidirectional thus enhancing the image in several directions in one pass. The regularisation based prediction network learns from the training set of images and determines the fractional order based on the statistics of the image at hand. Also the blur reduction is achieved in a controlled fashion as the fractional order is predicted according to the desired blur improvement. Experimental results with the comparative blur metric show the effectiveness of the proposed novel filter on a wide range of images. (6 pages)Epidural needle length measurement by video processing
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0446
This paper presents a novel image processing algorithm to measure the length and depth of an epidural needle during insertion. A wireless camera is used which transmits video during insertion to a host computer. The computer contains the image processing algorithm to detect the visible needle in the image and measures the length. The measurement is done by HSV background removal, colour comparison and using RGB histograms to locate 10mm markings on the Tuohy needle shaft. The visible length is then subtracted from the known length of the needle to calculate the depth of the needle tip. The camera can be placed in the operating theatre up to one meter away from the needle insertion site. The purpose of measuring needle depth in real time is to precisely place the needle in the epidural space. (6 pages)Automatic identification of wildlife using local binary patterns
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0454
Recognition of individuals is necessary for accurate estimation of wildlife population dynamics for effective management and conservation. Identifying individual wildlife by their distinctive body marks is one of the least invasive methods available. Although widely practiced, this method is mostly manual where newly captured images are compared with those in the library of previously captured images. The ability to do so automatically using computer vision techniques can improve speed and accuracy, facilitate on-field matching, and so on. This paper reports the results of using a texture based image feature descriptor, the Local Binary Patterns (LBP), for the automatic identification of an important endangered species - The Great Crested Newt (GCN). The proposed approach is tested on a database of newts' distinctive belly images which are treated as a source of biometric information. Results indicate that when both appearance and spatial information of newt belly patterns are encoded into a composite LBP feature vector, the discriminating power of the system can improve significantly. (6 pages)Illumination robust face representation based on intrinsic geometrical information
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0437
The ability to recognize people is a key element for improving naturalistic human-robot and human-computer interaction systems. In this paper, we propose a binary non-subsampled contourlet transform (B-NSCT) based illumination robust face representation. Faces are transformed into multi-scale and multi-directional contour information where the intrinsic geometrical structures are used for characterising facial texture. Experiments on the Yale B and CMU PIE databases illustrate that B-NSCT is highly insensitive to illumination variation. (6 pages)An improved enhancement of degraded binary text document images using morphological and single scale retinex operations
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0420
This paper proposes a novel enhancement algorithm for degraded binary document images captured using a low end cell phone camera. The algorithm starts with colour to grey scale conversion followed by contrast stretching. Then single scale retinex enhancement is performed to increase the contrast, reduce global variance and improve the background uniformity of the image. Otsu's binarisation method is then used to dichotomize the image and finally, morphological dilation is performed to preserve stroke connectivity of the document characters by bridging any gaps resulting from the thresholding process. Computer simulation experiments have been used to demonstrate the effectiveness of the proposed algorithm. The results show a significant improvement over the Otsu's method in terms shadow removal, document legibility and optical character recognition. The results also compare well with the state of the art Sauvola's method results. (6 pages)Stereoscopic 3D visual attention model considering comfortable viewing
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0445
Stereoscopic 3D images have depth perception as one of characteristics differ from conventional 2D images. Therefore, the conventional 2D oriented visual attention model cannot be directly applied to stereoscopic 3D images. In addition, visual fatigue is considered as another 3D related characteristics when viewing stereoscopic 3D images. In this paper, we propose a novel 3D visual attention model that combines bottom-up and top-down methods which considers comfortable viewing. The proposed model is based on 2D oriented features extracted from the image: intensity, colour, local orientation information, and the detection of human representation. Then, this model is combined with depth information obtained by considering the comfortable viewing. The experimental results show that the proposed model increase the simulation efficiency of visual characteristics of human visual system. (5 pages)Geolocation: maps, measurements and methods
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0404
The paper presents the geolocation is the art of innovative combination of properties of nature and sensor technology. The particle filter provides a general method for geolocation. Geolocation fingerprinting is a general concept for fitting a measurement (along a trajectory) to a geographical information system. Geolocation is equal to map plus measurement plus method. (48 pages)High-capacity colour image watermarking using multi-dimensional Fourier transforms and semi-random LDPC codes
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0456
In this paper, we propose a colour image watermarking scheme based on the Spatio-Chromatic Fourier Transform (SCFT) with spread-spectrum signaling enhanced by error correction using semi-random low density parity check (SRLDPC) codes. The SCFT transform enables efficient use of the embedding properties of the complex Fourier representations without incurring additional computational complexity. The watermark detection is based on a statistical maximum likelihood approach using a Weibull distribution known to be well-suited for modelling the SCFT coefficients. The proposed embedding scheme is image-adaptive, and provides control over the watermark embedding strength according to the local properties of the SCFT representation of the host image. The efficiency and data hiding capacity of the proposed watermark embedding scheme are found to be greatly enhanced by the use of SR-LDPC codes. Simulation results and comparisons with colour-component Discrete Fourier Transform (DFT)-based schemes demonstrate the increased robustness of the proposed LDPC-coded, colour image watermarking algorithm against standard attacks including additive white Gaussian noise and JPEG compression. (5 pages)Robust image watermarking using two dimensional Walsh coding
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0435
This paper deals with a new blind watermarking technique using two dimensional Walsh coding. The aim of using the 2D Walsh coding is to improve the robustness of the algorithm. The watermark which is a hand written signature was encoded by using 2D Walsh functions then it was embedded in the low frequency coefficients of the discrete cosine transform of the host image. The new algorithm is blind and does not require the original image to extract the watermark and cause minimal distortion to the host image. The robustness of the algorithm was assessed against various Stirmark attacks such as JPEG compression, noise, and some filtering operations. The extent of the improvements is related to the scaling factor. (5 pages)Preliminary evaluation of multispectral iris imagery
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0450
The unique patterns of complex texture that are visible in iris images captured under near-infrared illumination are highly stable and can be used for high confidence biometric recognition. Similar patterns are visible in lighter colored irises under visible light. This paper presents an analysis of a database of iris images captured using multispectral illumination, from 405 nm to 1070 nm in wavelength. The analysis is based on matching performance using a Daugman-based commercial implementation of the iris recognition algorithm. We find that illumination wavelength has a very significant effect on iris recognition performance. (5 pages)Novel fingerprint segmentation with entropy-Li MCET using log-normal distribution
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0455
Fingerprint recognition is an important biometric application. This process consists of several phases including fingerprint segmentation. This paper proposes a new method for fingerprint segmentation using a modified Iterative Minimum Cross Entropy Thresholding (MCET) method. The main idea is to model fingerprint images as a mixture of two Log-normal distributions. The proposed method was applied on bi-modal fingerprint images and promising experimental results were obtained. Evaluation of the resulting segmented fingerprint images shows that the proposed method yields better estimation of the optimal threshold than does the same MCET method with Gamma and Gaussian distributions. (6 pages)Dyadic wavelets and dct based blind copy-move image forgery detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0439
This paper proposes a blind method of copy move image forgery detection using dyadic wavelet transform (DyWT) and discrete cosine transform (DCT). An input image is decomposed using DyWT to approximation (LL) subbands and detail (HH) subbands. DCT is then applied to overlapping blocks in LL and HH subbands, and Euclidean distances between the blocks are calculated using DCT coefficients. Decision is made based on similarity of the blocks in LL subband and dissimilarity of the blocks in HH subband. The proposed method is evaluated with images of different sizes, different compression qualities, and with or without rotation before pasting. Experimental results show that the method performs better in all cases than two other multiresolution based methods. (6 pages)Multi-resolution, perceptual and compressive sampling based image codec
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0462
Direct application of compressive sampling in coding wavelet high frequency coefficients of an image, is unpleasantly deteriorating the quality of the reconstructed image. This is due to an error introduced by many high frequency coefficients that have small but nonzero values. In this paper, a novel multi-resolution image coding scheme using compressive sampling and perceptual weights is presented that significantly improves the quality of the reconstructed images by setting the coefficients with small values to zero using two different hard thresholding operators. The proposed codec applies a wavelet transform on the input image and decorrelates the image into its frequency subbands. Baseband coefficients are lossless coded to preserve their visually important information. High frequency subbands' coefficients are hard threshold to improve and also to control their sparsity. Perceptual-weights for different wavelet subbands are calculated and used to adjust threshold values for different subbands. Compressive sampling algorithm is used to generate measurements for each resulting sparse subband. Measurements for each subband are then cast to an integer and arithmetic coded. In the decoder side, the Basis Pursuit method is used to recover the coefficients. Empirical values for the observation factor for best coding performance of the codec, using standard test images, were first determined. The performance of the codec was assessed using standard test images. Results show that the application of perceptual weights in regulating threshold values significantly improves the coding performance of the codec. (4 pages)Distortion constrained robustness scalable watermarking
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0434
The embedding distortion and the robustness to quality scalable image coding are two complementary watermarking requirements. This work proposes a novel concept of scalable image watermarking to generate a distortion-constrained robustness scalable watermarked image code stream which consists of hierarchically nested joint distortion-robustness coding atoms. The code stream is generated using a new wavelet domain binary tree guided rules-based blind watermarking algorithm. The code stream can be truncated at any distortion-robustness atom level to generate the watermarked image with the desired distortion-robustness requirements. A universal blind extractor is capable of extracting watermark data from the watermarked images. The simulation results verify the feasibility of the proposed concept, its applications and its improved robustness to quality scalable content adaptation (JPEG 2000). (6 pages)A combined image approach to compression of volumetric data using delaunay tetrahedralization
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0461
We present a method for lossy compression of three dimensional gray scale images that is based on a 3D linear spline approximation to the image. We have extended an approach that has previously been successfully applied in two dimensions. In our method, we first select significant points in the data, and use them to create a 3D tetrahedralization. The tetrahedrons of the tetrahedralization are used as cells for a linear interpolation spline that gives an approximation of the original image. The compression is done by storing the positions of the vertices of the tetrahedralization and the values there instead of the value of the approximation at each grid point. We introduce a novel concept of using a smoothed version of the original image to improve the quality of the approximating spline. To increase the efficiency of the algorithm, we combine it with a refinement/decimation technique. We compare our compression technique to JPG2000 3D. We show that our algorithm performs similarly to, and in some cases even outperforms it, for high compression ratios. Our approach gives images that have significantly different properties than ones created using wavelets, and have the potential of being more suitable for some applications. In addition, this type of compression is particularly suitable for visualization. (6 pages)Mass detection in digital mammograms using gabor filter bank
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0465
Digital Mammograms are currently the most effective imaging modality for early detection of breast cancer but the number of false negatives and false positives is high. Mass is one type of breast lesion and the detection of masses is highly challenged problem. Almost all methods that have been proposed so far suffer from high number of false positives and false negatives. In this paper, a method for detecting true masses is presented, especially, for the reduction of false positives and false negatives. The key idea of the proposal is the use of Gabor filter banks for extracting the most representative and discriminative local spatial textural properties of masses that are present in mammograms at different orientations and scales. The system is evaluated on 512 (256 normal+256 true mass) regions of interests (ROIs) extracted from digital mammograms of DDSM database. We performed experiments with Gabor filter banks having different numbers of orientations and scales to find the best parameter setting. Using a powerful feature selection technique and support vector machines (SVM) with 10-fold cross validation, we report to achieve Az = 0.995±0.011, the area under ROC. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods, which are based on texture description, and the difference is statistically significant. (6 pages)Face recognition using kernel collaborative representation and multiscale local binary patterns
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0457
Collaborative Representation with regularized least square (CRC-RLS) is state-of-the-art face recognition method that exploits the role of collaboration between classes in representing the query sample. However, this method views the image as a point in a feature space, and the performance can be degraded when the cropped face image is misaligned and/or the lighting conditions change. Histogram-based features, such as Local Binary Patterns (LBP) have gained reputation as powerful and attractive texture descriptors showing excellent results in terms of accuracy in face recognition. In this paper, LBP features are introduced in CRC-RLS to confront these problems such as illumination. In addition, motivated by the recent success of non-linear approaches, a new kernel-based nonlinear regularized least square classifier with collaborative representation (KCRC-RLS) is proposed in this paper. The proposed system is evaluated on two benchmarks: ORL and Extended Yale B. The results indicate a significant increase in the performance when compared with state-of-the-art face recognition methods. (4 pages)Expression classification of 3D faces using local deformations
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0452
One of the most important challenges for face recognition algorithms is dealing with large variability due to facial expression. This paper presents an approach for the expression classification of 3D face scans. The proposed method is based on modelling local deformations which are calculated as the surface change between a neutral face and a face with expression. These deformations are used to train a multiclass/multi-feature LDA classifier. On an unseen face local deformations are calculated automatically using a face with neutral expression as a reference. It is shown that the results obtained are comparable with other similar approaches with the advantage that there is not manual intervention is required for the classification process. (6 pages)Semi automated segmentation of chromosomes in metaphase cells
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0463
Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. Since the birth of the automated karyotyping systems by the aid of computers, building a fully automated chromosome analysis system has been an ultimate goal. Along with many other challenges, accurate segmentation of the chromosomes has been a major challenge especially due to the non rigid nature of the chromosomes. The earlier reported approaches for the segmentation have limited success as they are sensitive to scale variation, experimented only on gray images, unable to segment the clusters and the highly bent chromosomes. This work, describes an effective approach of segmentation of chromosomes in Metaphase images using Random Walker Algorithm [RWA] which is yet unexplored and not reported in the literature. The efforts are also done to compare the results with traditional methods so as to prove the efficiency of the implemented RWA algorithm. The algorithm is tested on publically available database and has shown encouraging and acceptable results. (6 pages)Selectively filtering image features using a percentage occupancy hit-or-miss transform
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0427
The Hit-or-Miss Transform (HMT) is a well known morphological transform which can be used for template matching and other applications. Recent developments in this area include extensions of the HMT which have employed a variety of techniques in order to improve the noise robustness of the transform. Rank order filters feature heavily in these approaches, and recently, a novel design tool, known as a PO plot, has been introduced. This tool can be used to determine the optimum rank parameter when using these extensions of the HMT to locate features in noisy data. In this paper, the properties of the PO plot are exploited in such a way that an extension of the HMT, known as a POHMT, can be used as a discriminatory filter which selectively marks or discards features in an image. This paper summarises the POHMT, and the PO plot, before demonstrating how these can be used to implement a discriminatory filter. This filter is then shown to produce promising results when applied to the problem of selectively detecting dice in images. (6 pages)Discriminative training of patch-based models using joint boosting for occupant classification
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0449
This paper presents a vision-based occupant classification method which is essential for developing a system that can intelligently decide when to turn on airbags based on vehicle occupancy. To circumvent intra-class variance, this work considers the empty class as a reference and describes the occupant class by using appearance difference. Context contrast histogram is used to represent the patch appearance. Each class is modelled using a set of locally representative parts called patches that alleviate the mis-classification problem resulting from severe lighting change. The selection and estimating the parameters of the patches are learned through joint boosting by minimizing training error. Experimental results from many videos from a camera deployed on a moving platform demonstrate the effectiveness of the proposed approach. (4 pages)Colour texture segmentation using evidence gathering
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0441
A new approach to colour-texture segmentation is presented which uses Local Binary Pattern data and a new colour quantisation scheme based on hue and saturation to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class. Tests on remotely sensed images demonstrate the superiority of the colour-texture algorithm compared to the established JSEG algorithm; a notable advantage of the new approach is the absence of over-segmentation. The VisTex database is used to compare the colour-texture algorithm with alternative methods, including its grey-scale equivalent, for the segmentation of colour texture images; providing good results with smooth texture boundaries and low noise within texture segments. (6 pages)Low-complexity lossy image coding through a near-optimal general embedded quantizer
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0428
Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common scheme to do so is to use a uniform scalar deadzone quantizer (USDQ) together with a bitplane coding (BPC) strategy. This scheme is convenient, but does not allow major variations. This paper uses the recently introduced general embedded quantizer (GEQ) to design a multi-stage quantization scheme that can be introduced in the core of modern image coding systems. Experimental results carried out in the framework of JPEG2000 indicate that the proposed scheme achieves same coding performance as that of USDQ+BPC while requiring fewer quantization stages, which reduces the computational costs of codecs without penalizing their performance. (6 pages)Total electron content mapping using global navigation satellite systems
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0390
We present the results of application high-orbital (GPS/GLONASS) and low-orbital (Parus/TRANSIT) satellite navigation systems to the study of different-scale irregularities in the ionospheric total electron content (TEC), such as troughs, crests of equatorial anomaly, traveling ionospheric disturbances and so on. We also present the results of the comparison of the global ionospheric maps (GIM) of vertical TEC, which are now widely used in ionospheric research, with the results of low- and high-orbital radio tomographic ionospheric imaging and with the data of UV spectral imaging form GUVI instrument (Global Ultraviolet Imager). The data from low-orbital radiotomography systems in Russia (Moscow-Svalbard) and Alaska (Arctic Village-Cordova) were involved in the comparison as well as the data of the IGS (International GNSS Service) network. The comparisons cover the time interval from 2003 to 2008, which includes both geomagnetically quiet and disturbed periods. We also demonstrate the possibilities of GPS/GLONASS TEC studies in connection with solar flares and artificial ionospheric heating. (5 pages)A new mutual information based similarity measure for medical image registration
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0424
Medical image registration (IR) is the systematic process of aligning spate images, often involving different modalities with common reference framework, so complementary information can be combined and compared. This paper presents a new similarity measure which uses Expectation Maximization for Principal Component Analysis allied with mutual information (EMPCA-MI) for medical IR. The new measure has been analysed on multimodal, three band magnetic resonance images (MRI) T1, T2 and PD weighted, in the presence of both intensity non-uniformities (INU) and noise. Both quantitative and qualitative experimental results clearly demonstrate both improved robustness and lower computational complexity of the new EMPCA-MI paradigm compared with existing MI-based similarity measures, for various MRI test datasets. (6 pages)Skin detection using color processing mechanism inspired by the visual system
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0459
Skin detection is an important procedure for face detection, hand gesture analysis, and objectionable image filtering. In this paper, a skin detection algorithm is proposed according to the colour processing mechanism inspired by the visual system. In the algorithm different types of neurons are used to deal with skin colour and texture. Inspired by receptive fields of rod, cone and orientation selectivity neurons in the visual system, a neural network is proposed to identify skin area in an image. The network contains three types of cones, one type of rod and four types of orientation neurons. Three cones are in response to red, green and blue lights respectively. The rods are in response to brightness. Orientation neurons are used to detect textures of skin. Through their specific receptive fields, the network can be trained to identify skin areas. The experimental results show that the proposed algorithm is comparable to current skin segmentation algorithms. (5 pages)Normalized cuts and watersheds for image segmentation
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0440
In this paper problem of image segmentation is considered. Specifically, normalized graph cut algorithm is regarded. In its source version the Ncut approach is computationally complex and time consuming, what decreases possibilities of its application in practical applications of machine vision. The segmentation approach proposed in this paper overcomes these limitations by incorporating watershed transform and normalized cuts. Results of the proposed method are presented, compared with results of the original normalized cut method and discussed. (6 pages)Validating the neutrosophic approach of MRI denoising based on structural similarity
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0419
This paper focuses on validating the proposed Neutrosophic Set (NS) approach of Magnetic Resonance Image (MRI) denoising based on structural similarity such as Structural Similarity Index (SSIM) and Quality Index based on Local Variance (QILV). The Neutrosophic Set approach of median filter is used to reduce the Rician noise in MR image. The experiments have conducted on real MR image with Rician noise added. The visual and the diagnostic quality of the denoised image is well preserved. The performance of this filter is compared with median filter and non local mean filter (NLM). (6 pages)Closed form spherical omnidirectional image unwrapping
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0443
This paper proposed a novel method of image unwrapping for spherical omnidirectional images acquired through a non-single viewpoint omnidirectional sensor. The proposed method comprises of three key steps, i.e. 1) calibrate the camera to obtain parameters describing the spherical omnidirectional sensor, 2) map world points onto mirror points and, subsequently, onto image points, and 3) set up the projection plane for the final image unwrapping. Depending on the projection plane selected, the algorithm is able to produce either the cylindrical panoramic, the cubloid panoramic, or the ground plane view using closed form mapping equations derived herein. The motivation for developing this technique is to address the complexity in using non-single viewpoint omnidirectional sensor; and ultimately promotes its adoption in robotics. (5 pages)Feasibility of iris identification algorithm optimization by fractional template matching
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0453
The human iris exhibits random and unique textural patterns that allow for identification with high accuracy. These patterns are evident in near-infrared (NIR) imagery, even for very dark irises. Iris templates are created from NIR iris imagery with the Ridge Energy Direction (RED) recognition algorithm and subsequently matched to measure performance. In this paper we investigate the feasibility of algorithm optimization by performing an initial fractional template comparison to eliminate high Hamming distance (HD) score matches followed by a full template re-comparison or iterative higher fractional template comparison on remaining templates. Recognition performance for different fractional template areas is analyzed with a view towards substantial improvement of algorithm identification time performance. The feasibility of identification time reduction by 60% or more is reported both ICE and Bath data sets. (5 pages)Optimal configuration strategies for iris recognition processing
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0451
Although biometric systems are now widely used in a variety of scenarios, optimising practical systems can still be challenging because this can involve balancing a number of very different factors across a number of processing stages. In iris recognition, a typical processing chain leading from raw data capture to an identity decision requires several clearly identifiable modules, but within this chain it is generally accepted that the segmentation stage and the recognition module are particularly critical in determining overall performance. This paper investigates the extent to which overall performance can be optimised with respect to scenario constraints and requirements by an appropriate balance of processing power allocated between these two most important steps, and how enhanced processing at one step can compensate for less sophistication at another. The work reported thus provides some insight into practical options for system implementation which can take account of constraints in, for example, error rate performance, processing speed, resource allocation, and so on. (6 pages)Advanced video camera identification using conditional probability features
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0426
Today, the misuse of digital data especially images and videos become crucial with the existence of sophisticated high-tech equipment and it is available at relatively low cost. Illegal recording of movie in cinema has caused losses of millions of dollars a year. Law enforcement agencies are keen to find ways to counter illegal video recording. Current research into camera identification techniques is attracting a significant amount of attention. The main objective is to identify the camera equipment used to record digital image or video based on the data source obtained. In this paper, we propose a video camera identification technique based on the Conditional Probability (CP) Features. Specifically we focus on its performance for identification of video sources using cameras of different models. In our experiments, we demonstrate that the CP Features are able to correctly match the test video frames with their source with classification accuracy is approximately 97.2%. These findings provide a good indication that CP Features are suitable for digital video forensics. (5 pages)Digital image ownership verification based on spatial correlation of colors
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0448
In this paper, a spatial domain digital image copyright protection scheme based on Visual Cryptography (VC) and Spatial Correlation of Colors (SCC) is proposed. A binary feature matrix, extracted from the spatial correlation of host image, is used to split the watermark into two noisy binary images called shares. One of them is generated during watermark embedding phase and is registered with a trusted third party. The other is extracted during watermark extraction phase. Both these shares are combined to recover hidden watermark. When compared to the related works, the proposed scheme reduces the probability of false positives; reduces the size of shares and improves the quality of extracted watermark. Experimental results prove that the scheme is also robust to wide range of attacks. (5 pages)Geometric feature based age classification using facial images
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0438
This paper presents the use of geometric feature based models for age group determination of facial color images. This process consists of two main stages: geometric feature extraction, analysis and age group classification. The feature extraction was performed with the correct understanding of the effect of age on facial anthropometry. The age differentiation capability of the features is evaluated using three different classifiers, namely, neural network classifier, support vector classifier, normal densities-based linear classifier. The facial face images are categorized to five major age groups. To show the effectiveness and accuracy of the proposed feature extraction, experiments are conducted on two publically available databases namely FGNET and IFDB. The results show that the success rate of classification is around 90%. (5 pages)Segmentation of sputum cell image for early lung cancer detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0433
Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. To that end, computer-aided diagnosis system using images of sputum stained smears has been an attractive approach due to its practicality, low cost, and invasiveness. In this context, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with an other competitive technique via a series of experimentation conducted with a data set of 88 images. (6 pages)MRI mammogram image classification using ID3 algorithm
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0464
Breast cancer is one of the most common forms of cancer in women. In order to reduce the death rate , early detection of cancerous regions in mammogram images is needed. The existing system is not so accurate and it is time consuming. The Proposed system is mainly used for automatic segmentation of the mammogram images and classify them as benign,malignant or normal based on the decision tree ID3 algorithm. A hybrid method of data mining technique is used to predict the texture features which play a vital role in classification. The sensitivity, the specificity, positive prediction value and negative prediction value of the proposed algorithm accounts to 93.45% , 99.95%,94% and 98.5% which rates very high when compared to the existing algorithms. The size and the stages of the tumor is detected using the ellipsoid volume formula which is calculated over the segmented region. (5 pages)A genetic based generic filter for image impulse noise reduction
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0421
The captured images usually are influenced by impulse noise therefore removing impulse noise is one of most important pre-processing phases in many applications. In this paper, we have proposed a genetic based method to remove impulse noise. This method proposes a composite filter which is a combination of several standard filters to reduce the noise effect. The experimental results showed the proposed method could efficiently restore degraded image while it is approximately stable to noise ratio increment. (5 pages)Image segmentation based on semi-greedy region merging
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0431
Region merging algorithms are known to be fast when the merge criteria are relatively loose but very slow when extended schemes are applied. However, since region merging is greedy and looks only at local information, it is susceptible to suboptimal merge pathways as well as to outliers. This paper presents a fast and effective region merging scheme using a semi-greedy merging criterion and an adaptive threshold (SGAT) to control segmentation resolution. In quantitative analysis on standard benchmarks data, the proposed method performs the best, with respect to specific metrics as well as overall, compared to other segmentation methods. (4 pages)Learning based objective evaluation of image segmentation algorithms
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0444
Image segmentation plays an important role in a broad range of applications and many image segmentation methods have been proposed, therefore it is necessary to be able to evaluate the performance of image segmentation algorithms objectively. In this paper we present a new fuzzy metric to evaluate the accuracy of image segmentation algorithms, based on the features of each segments using neural networks. The neural network after training can distinguish the similarity or dissimilarity of each pairs of segments and finally the segmentation algorithms accuracy have been computed by novel presented metric quantitatively. Our proposed method does not require a manually-segmented reference image for comparison therefore can be used for real-time evaluation and is sensitive to both oversegmentation and under-segmentation. Experimental results were obtained for a selection of images from Berkeley segmentation data set and demonstrated that it's a proper measure for comparing image segmentation algorithms. (6 pages)Model, predict and test: towards a rigorous process for acquisition of object detection and recognition algorithms for un-manned air vehicle applications
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0423
The designers of unmanned air vehicle (UAV) mission systems are seeking to exploit advances in consumer image processing technology, to provide additional object detection and recognition capability for UAV systems. However, the two application domains are quite different, so a simple transfer of algorithms is not possible. A formal approach for selecting and developing algorithms is proposed. This involves algorithm modelling and experimental validation by the algorithm supplier, together with assessment trials in a representative system, carried out by the system designer. The objective is to build confidence in the behaviour of the algorithm under consideration, to build high fidelity models that can be used in system design studies and to address integration issues early in a development programme. A review of the open literature indicates that the necessary algorithm modelling is feasible and an example is presented to illustrate the proposed approach. (6 pages)A thresholding approach for detection of sputum cell for lung cancer early diagnosis
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0442
In this paper, we address the problem of detection and extraction of sputum cells that help in lung cancer early diagnosis. Our approach is based on thresholding classifier looking at the distribution of sputum pixels and non sputum pixels in RGB space for extracting the sputum cell from the raw sputum image. In this method the problem is viewed as a segmentation problem focusing on extraction of such sputum cells from the images whereby we want to partition the image into sputum cell regions including the nuclei, cytoplasm and the background that includes all the rest. These cells will be analyzed to check whether they are cancerous or not. In this study, we used a database of 100 sputum color images to test the thresholding classifier by comparing it with the ground truth data of extracted sputum cells and it has shown a better extraction result than previous work. Moreover, we computed a histogram for different color spaces (RGB, YCbCr, HSV, L*a*b* and XYZ) to find the best color space with low false detection rate. We used some performance criteria such as precision, specificity and accuracy to evaluate the improved thresholding classifier. (6 pages)Retinal vessel segmentation using ensemble classifier of bagged decision trees
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0458
This paper presents a new supervised method for segmentation of blood vessels in retinal images. This method uses an ensemble system of boot strapped decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological linear transformation, line strength measures and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases. Method performance on both sets of test images is better than the 2<sup xmlns="http://pub2web.metastore.ingenta.com/ns/">nd</sup> human observer and other existing methodologies available in the literature. The incurred accuracy, speed, robustness and simplicity make the algorithm a suitable tool for automated retinal image analysis. (6 pages)Robust watermarking for scalable image coding-based content adaptation
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0436
In scalable image coding-based content adaptation, such as, JPEG 2000, the quality scaling is performed by a quantization process that follows a bit plane discarding model. In this paper we propose a robust blind image watermarking algorithm by incorporating the bit plane discarding model. The new wavelet based binary tree guided rules-based watermarking algorithm is capable to retain the watermarking information for a given number of bit plane being discarded. The experimental simulations confirm the scheme's robustness against JPEG 2000 quality scalability. (6 pages)Object search using wavelet-based polar matching for aerial imagery
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0167
A method for finding known objects in aerial and satellite imagery was designed, implemented in MATLAB and tested. This method took a keypoint-based approach to describing objects. Several of the steps used in the scheme took advantage of the Dual-Tree Complex Wavelet Transform (DTCWT). These were combined with a new approach which takes the highly localised information encoded in keypoint descriptors and combines it with “global” geometrical information about the target. Many keypoints which corroborate the same approximate location as the predicted target centre can improve match confidence. The implementation of the above was evaluated both with synthetic imagery, as a means to identify and characterise factors which degrade performance, and also real imagery as a means to demonstrate its “real-world” performance. The real imagery demonstrated success in identifying a range of objects. (5 pages)Randomised forests for people detection
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0148
People detection is an important task with applications in fields such as surveillance and human computer interaction. A popular approach to this problem is to train a classifier on a data set using a particular set of features. A great deal of empirical evidence suggests that edge features are particularly discriminative for this task. In this paper we explore the use of randomised forests (sometimes referred to as randomised decision forests) for people detection. A randomised forest classifier is trained for people detection with edge orientation features. These features capture information concerning the distribution of edges with specific orientations. The classifier is trained and tested on the INRIA person data set, and some results are presented. (5 pages)Wyner-Ziv coding for distributed compressive sensing
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0141
Distributed compressive sensing (CS) is emerging as a powerful technique of distributed signal processing in various applications such as sensor networks due to its capability of simultaneous sensing and compression. However, since distributed CS is an analog technique, a fundamental open question is to find the best source coding scheme for the distributed CS samples. This paper applies nested-lattice Wyner-Ziv coding to the CS data by exploiting the correlation among the CS samples at different sensors. The proposed coder consists of CS with Toeplitz/circulant sensing matrices and practical Wyner-Ziv coding. Simulation results shows this is a fast, energy-saving system and recovers good quality image sources with low distortion and high SNR. (5 pages)People counting with re-identification using depth cameras
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0113
Low cost real-time depth cameras offer new sensors for a wide field of applications apart from the gaming world. Other active research scenarios as for example surveillance, can take advantage of the capabilities offered by this kind of sensors that integrate depth and visual information. In this paper, we present a system that operates in a novel application context for these devices, in troublesome scenarios where illumination conditions can suffer sudden changes. We focus on the people counting problem with re-identification and trajectory analysis. Automatic people counting offers different application contexts related to security, safety, energy saving or fraud control. Here we go one step further and give hints to extract useful information using depth cameras. The processing of that information allows us to analyze the individuals behavior, particularly if they go away from the typical trajectory, and the problem of re-identifying people. (6 pages)Facial blood vessels activity in drunk persons using thermal infrared
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0108
In this work, the activity of the facial blood vessels of drunk persons is studied using thermal infrared images. Nonlinear anisotropic diffusion is applied to enhance the vessels on the images and after that top-hat transformation is used for isolating them from the rest information on the face. Simple thresholding is applied to raise the more active vessels. Registration procedures are employed to easily compare the vessel activity on the face of the drunk person with that on the sober person. In drunk persons, vessels around nose and eyes as well as on the forehead become more active. This work constitutes a preliminary study, which aims at qualitative and not quantitative results. Basically, the restricted number of the 20 persons that participated in the experiment is not considered adequate for statistical inference. (5 pages)Real time car theft decline system using ARM processor
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0059
Due to the insecure environment the ratio of vehicle theft increases rapidly. Because of this is manufacturers of luxury automobiles has the responsibilities for taking steps to ensure the authorization for the owners and also inbuilt the anti theft system to prevent the car from theft. The existing system was. Car alarm techniques are used to prevent the car theft with the help of different type of sensors like pressure, tilt and shock & door sensors.Drawbacks are cost and cant used to find out the thief, it just prevents the vehicles from loss. The proposed security system for smart cars used to prevent them from loss or theft using Advanced RISC Machine (ARM) processor. It performs the real time user authentication (driver, who starts the car engine) using face recognition, using the Principle Component Analysis - Linear Discreminant Analysis (PCA LDA) algorithm. According to the comparison result (authentic or not), ARM processor triggers certain actions. If the result is not authentic means ARM produces the signal to block the car access (i.e. Produce the interrupt signal to car engine to stop its action) and inform the car owner about the unauthorized access via Multimedia Message Services (MMS) with the help of GSM modem. Also it can be extends to send the current location of the vehicle using the GPS modem as a Short Message Services (SMS) as passive method.CT-based robust statistical shape modeling for forensic craniofacial reconstruction
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0126
Estimating the facial outlook from an unidentified skull is a challenging task in forensic investigations. This paper presents the definition and implementation of a craniofacial model for computerized craniofacial reconstruction (CFR). The craniofacial model consists of a craniofacial template that is warped towards an unidentified target skull. The allowed transformations for this warping are statistically defined using a PCA-based transformation model, resulting in a linear combination of major modes of deformations. This work builds on previous work [1] in which a statistical model was constructed based on facial shape (represented as a dense set of points) variations and sparse soft tissue depths at 52 craniofacial landmarks. The main contribution of this work is the extension of the soft tissue depth measurements to a dense set of points derived from a database of head CT-images of 156 patients. Despite the limited amount of training data compared to the number of degrees of freedom, the reconstruction tests show good results for a larger part of the test data. Root mean squared error (RMSE) values between reconstruction results and ground truth data smaller than 4 mm over the total head and neck region are observed. (6 pages)