IET Image Processing
Print ISSN
1751-9659
Online ISSN 1751-9667
Online ISSN 1751-9667
The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications.
This publication was previously known as IEE Proceedings - Vision, Image and Signal Processing 1994-2006. ISSN 1350-245X. more..
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Latest content
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Framework for image retrieval using machine learning and statistical similarity matching techniques
- Author(s): Majid Fakheri; Tohid Sedghi; Mahrokh G. Shayesteh; Mehdi Chehel Amirani
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p.
1
–11
(11)
The aim of this study is to take advantage of both shape and texture properties of image to improve the performance of image indexing and retrieval algorithm. Further, a framework for partitioning image into non-overlapping tiles of different sizes, which results in higher retrieval efficiency, is presented. In the new approach, the image is divided into different regions (tiles). Then, the energy and standard deviation of Hartley transform coefficients of each tile, which serve as the local descriptors of texture, are extracted as sub-features. Next, invariant moments of edge image are used to record the shape features. The shape features and combination of sub-features of texture provide a robust feature set for image retrieval. The most similar highest priority (MSHP) principle is used for matching of textural features and Canberra distance is utilised for shape features matching. The retrieved image is the image which has less MSHP and Canberra distance from the query image. The proposed method is evaluated on three different image sets, which contain about 17 000 images. The experimental results indicate that the proposed method achieves higher retrieval accuracy than several previously presented schemes, whereas the computational complexity and processing time of the new method are less than those of other approaches.
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Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure?
- Author(s): Alain Horé; Djemel Ziou
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p.
12
–24
(13)
In this study, the authors analyse two well-known image quality metrics, peak-signal-to-noise ratio (PSNR) as well as structural similarity index measure (SSIM), and the authors derive an analytical relationship between them which works for some kinds of common image degradations such as Gaussian blur, additive Gaussian noise, Jpeg and Jpeg2000 compressions. The analytical relationship brings more clarity on the interpretation of PSNR and SSIM values, explains some differences found between these quality measures in the literature and confirms some experimental observations regarding these measures. A series of tests realised on images from the Kodak database give a better understanding of the performance of SSIM and PSNR in assessing image quality.
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Wavelet statistical texture features-based segmentation and classification of brain computed tomography images
- Author(s): A. Padma Nanthagopal; R. Sukanesh
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p.
25
–32
(8)
A computer software system is designed for segmentation and classification of benign and malignant tumour slices in brain computed tomography images. In this study, the authors present a method to select both dominant run length and co-occurrence texture features of wavelet approximation tumour region of each slice to be segmented by a support vector machine (SVM). Two-dimensional discrete wavelet decomposition is performed on the tumour image to remove the noise. The images considered for this study belong to 208 tumour slices. Seventeen features are extracted and six features are selected using Student's t-test. This study constructed the SVM and probabilistic neural network (PNN) classifiers with the selected features. The classification accuracy of both classifiers are evaluated using the k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and segmentation error. The proposed system provides some newly found texture features have an important contribution in classifying tumour slices efficiently and accurately. The experimental results show that the proposed SVM classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.
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Transmission of JPEG2000 images over frequency-selective channels with unequal power allocation
- Author(s): Moein Shayegannia; Atousa Hajshirmohammadi; Sami Muhaidat; Mahin Torki
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p.
33
–41
(9)
In this study, transmission of JPEG2000 images using an unequal power allocation (UPA) scheme and orthogonal frequency division multiplexing (OFDM) over block-fading frequency-selective channels is presented. A distortion model is provided to evaluate the contribution of each coding pass (CP) in the construction of the received image. The optimisation algorithm exploits the hierarchical structure of the JPEG2000 images and uses the distortion model along with the channel state information for allocating optimal values of power for each CP to minimise the end-to-end distortion. Furthermore, the actual total power consumed for transmission is measured and compared with the total power initially assigned. For the purpose of simulations, the authors set the number of OFDM subcarriers to be 16, the length of the cyclic prefixes equal to the channel memory length and analyse the quality of the received image in a 2-tap and 3-tap frequency-selective channel, with and without our proposed UPA technique in an OFDM system. The results show an improvement of up to 10.5 dB in the decoded image quality when the UPA scheme is used. In addition, our system manages to maintain similar quality for the received image in a multi-tap channel scenario.
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Fast and reliable iris segmentation algorithm
- Author(s): Abduljalil Radman; Kasmiran Jumari; Nasharuddin Zainal
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p.
42
–49
(8)
Design a fast and reliable iris segmentation algorithm for less constrained iris images is essential to build a robust iris recognition system. Daugman's integrodifferential operator (IDO) is one of powerful iris segmentation mechanisms, but in contrast consumes a large portion of the computational time for localising the rough position of the iris centre and eyelid boundaries. To address this problem, a fast iris segmentation algorithm is proposed. First, the circular Gabor filter is adopted to find the rough position of the pupil centre. Second, the iris and pupil circles are localised using the IDO taken into account that the real centres of the iris and pupil are in the small area around the rough position of the pupil centre. Third, the upper and lower eyelid boundaries are extracted using the live-wire technique. Experimental results demonstrate that the proposed iris segmentation algorithm significantly minimises the required time to segment the iris without affecting the segmentation accuracy. Moreover, the comparison results with state-of-the-art iris segmentation algorithms show the superiority of the proposed algorithm in terms of segmentation accuracy and recognition performance. The challenging UBIRIS.v1 iris image database is utilised to evaluate the performance of the proposed algorithm.
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Uniform depth region-based registration between colour channels and its application to single camera-based multifocusing
- Author(s): Jinhee Lee; Joonki Paik
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p.
50
–60
(11)
This study presents a spatially varying image registration method based on regions of the same depth. The proposed registration method uses phase correlation matching to measure colour shifting vectors (CSVs) between colour channels in a pre-specified region of the same distance to the camera, and aligns colour channels of the corresponding region according to the CSV. The authors also present the foreground region detection method by using binary edge labelling and analysis of histograms of channel-shifting features. The major contribution of this study is 2-fold: (i) the proposed method can be considered as a region-wise approximated version of fully non-rigid registration, which is widely used in the medical imaging area, and (ii) it can compensate misalignment between red (R), green (G) and blue (B) colour channels caused by refraction and chromatic aberration of a multiple colour-filtered aperture (MCA) camera, which has been proposed as a single camera-based multifocusing system. Among various applications of non-rigid image registration, the proposed region-based registration method is particularly suitable for multifocusing images acquired by an MCA camera. In depth analysis of each step of the proposed algorithm is provided with experimental results, and its application to the MCA camera is also provided to realise efficient depth estimation and highly accurate multifocusing functions using a single camera. Without using joint histogram or geometric transformation, the proposed region-adaptive approach successfully approximates the fully non-rigid registration with significantly reduced amount of computation.
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Optimised image retargeting using aesthetic-based cropping and scaling
- Author(s): Yun Liang; Zhuo Su; Chuntao Wang; Dong Wang; Xiaonan Luo
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p.
61
–69
(9)
Image retargeting is a critical technique in displaying images on devices with different resolutions. This study presents a new image retargeting algorithm based on aesthetic-based cropping and scaling. A composite measurement is first constructed under the guidelines of composition aesthetics in photographing. An aesthetic-based cropping is proposed to yield an optimal candidate retargeted image with maximum aesthetic value computed via a constructed composite measurement. The optimal candidate is uniformly scaled to obtain the retargeted image of target size. Some subjective and objective assessments demonstrate that the proposed scheme significantly improves the aesthetics of retargeted images while preserving the important objects. It also achieves better performance in terms of aesthetics than a number of conventional image retargeting approaches.
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Boosted subunits: a framework for recognising sign language from videos
- Author(s): Junwei Han; George Awad; Alistair Sutherland
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p.
70
–80
(11)
This study addresses the problem of vision-based sign language recognition, which is to translate signs to English. The authors propose a fully automatic system that starts with breaking up signs into manageable subunits. A variety of spatiotemporal descriptors are extracted to form a feature vector for each subunit. Based on the obtained features, subunits are clustered to yield codebooks. A boosting algorithm is then applied to learn a subset of weak classifiers representing discriminative combinations of features and subunits, and to combine them into a strong classifier for each sign. A joint learning strategy is also adopted to share subunits across sign classes, which leads to a more efficient classification. Experimental results on real-world hand gesture videos demonstrate the proposed approach is promising to build an effective and scalable system.
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Whitening central projection descriptor for affine-invariant shape description
- Author(s): Rushi Lan; Jianwei Yang; Yong Jiang; Colin Fyfe; Zhan Song
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p.
81
–91
(11)
A novel descriptor, referred to as the whitening central projection predictor (WCPD), is developed for affine-invariant shape description. The proposed descriptor is based on central projection transform (CPT) and whitening transform (WT). Dislike contour-based or region-based approaches, an object is first converted to a closed curve by CPT, which is called the general curve (GC). The derived GC not only keeps the affine transform information, but also is very robust to noise. Then WT is performed to the GC with the purpose that the affine transformation is simplified to a rotation only. Finally, Fourier descriptors are employed to remove the rotation, and WCPD is obtained. One advantage of using WCPD for affine-invariant description lies in that it is applicable to objects consisting of several components. Furthermore, the approach used on the GC is contour-based, and is of small computational complexity. Several experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method has a powerful discrimination ability, and is more robust to noise.
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Improved algorithm for detecting zero-quantised discrete cosine transform coefficients in H.264/AVC (revised version)
- Author(s): Li-Li Wang; Wan-Chi Siu
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p.
92
–97
(6)
In this study, an efficient approach for detecting zero-quantised discrete cosine transform (DCT) coefficients for video coding is developed. Compared with conventional detection methods of zero-quantised DCT coefficients used in H.264/AVC, the proposed algorithm has two major features. First, a new classification of patterns for DCT, quantisation, inverse quantisation and inverse discrete cosine transform processes is proposed. By taking a zigzag scanning order into the classification, the quantised DCT coefficients can be coded efficiently. Second, the thresholds for detecting zero-quantised DCT coefficients are determined by combining the Gaussian distribution with a theoretical analysis of the DCT and quantisation in H.264/AVC. Experimental results show that the proposed algorithm achieves an average timesaving of more than 40% compared with the algorithm in reference software JM12.2 of H.264/AVC. When compared with other algorithms in the literature, it also gives the best performance in terms of both rate-distortion and time saving.
