The present study assessed the use of filters for noise reduction in ultrasound images of the common carotid artery (CCA) and brachial artery using intima–media thickness, which is a safe and non-invasive technique for determining subclinical atherosclerosis and cardiovascular risk. A new combined speckle reducing anisotropic diffusion (SRAD) filter for noise reduction is then proposed. Ultrasonic examination of both arteries was performed on 30 men (aged 40 ± 5 years). The programme was designed using MATLAB software to extract consecutive images in bit map format from the audio video interleaves. An additional programme was designed in MATLAB to apply the region of interest (ROI) to the thickness of the intima–media of the posterior walls of the arteries. Block-matching techniques were used to estimate arterial motion from ultrasound images of the B-mode CCA and brachial artery. Different noise reduction filters and Canny edge detection were carried out separately in the ROI. The programme measured mean square error (MSE) and peak signal-to-noise ratio (PSNR). The results demonstrated that the new combined SRAD filter with Canny edge detection identified the lowest value for MSE and the highest value for PSNR in 90 consecutive frames (∼3 cardiac cycles). The results indicate that MSE and PSNR were better detected by the proposed combined SRAD filter with Canny edge detection than did several commonly used filters with Canny detection for speckle suppression and preservation detail in carotid and brachial arteries ultrasound images.

A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.

In real-world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library-100, SOIL-47, SDUMLA-HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi-component identity recognition.

Visual object tracking is a challenging task due to two intractable problems: visual appearance representation and online update model. Existing approaches often operate appearance model based on hand-crafted features with discriminative feature selection. The tracking learning model is formulated as a binary classification. However, some issues remain to be addressed. First, there does not exist sufficient information for online feature selection. Second, these algorithms do not make use of structure information between object and background. In this study, the authors propose an algorithm named data driven tracker with an appearance model which exploits prior visual target representation by binary PCANet. The authors’ speed up strategy by binary operation on the convolution filters is efficient for tracking task with little performance loss. They formulate the learning model as multi-class task via online LPBoost. Their data-driven tracking (DDT) algorithm performs favourably on various challenging sequences by evaluating against state-of-the-art trackers.

The authors propose a terse texture feature, called the dominant centre-symmetric local binary pattern (DCSLBP), which has similar distinctiveness and half dimension compared against original centre-symmetric local binary pattern (CS-LBP). On the basis of DCSLBP histogram and an improved construction, a compact descriptor for local feature is presented. To assess the proposed descriptor with the state-of-the-art in performance and dimension, the authors extend it to two variants with different dimensions using the existing method. These descriptors are compared with scale-invariant feature transform (SIFT), multisupport region rotation and intensity monotonic invariant descriptor (MRRID), orthagonal combination local binary pattern (OC-LBP) in interest region matching and in the application of object recognition. The experiments demonstrate the proposed descriptor's compactness and robustness to various image transformations, especially to large illumination change.

Face recognition (FR) is one of the most effervescent fields of research with extensive applications that span numerous domains, and it stands resolutely as one of the most challenging problems in computer vision. The accuracy of FR systems is severely affected when two images under consideration for a match, vary in their scale and/or affine angles. The prevalent affine and scale invariant recognition systems have been predominantly developed only for objects, and hence in this study, the authors propose a novel approach for faces based on the affine-SIFT (ASIFT) and two-dimensional principal component analysis (2DPCA) techniques, to accomplish the formidable task of facial image recognition, invariant of scale and affine angles, i.e. the ability to simulate with enough accuracy, all the distortions caused by the differences in resolution and the variation of the camera optical axis direction. In the formulation of ASIFT-2DPCA, they investigate three different variants of 2DPCA: classical 2DPCA, quaternion 2DPCA and sparse 2DPCA to gauge as to which is more effective. The authors'experimentations will demonstrate that the proposed approach can robustly handle affine and scale variations, and hence provide better accuracy and matching performance than the state-of-the-art methodologies.

In this study, the authors propose a multi-group–multi-class domain adaptation framework to recognise events in consumer videos by leveraging a large number of web videos. The authors’ framework is extended from multi-class support vector machine by adding a novel data-dependent regulariser, which can force the event classifier to become consistent in consumer videos. To obtain web videos, they search them using several event-related keywords and refer the videos returned by one keyword search as a group. They also leverage a video representation which is the average of convolutional neural networks features of the video frames for better performance. Comprehensive experiments on the two real-world consumer video datasets demonstrate the effectiveness of their method for event recognition in consumer videos.

In computer vision, it is a challenge to compute the relationship of multiple views from scene images. The view relationship can be obtained from the fundamental matrix. Thus, it is very important to compute an accurate fundamental matrix from unevenly distributed features in complex scene images. This study proposes a robust method to estimate the fundamental matrix from corresponding images. First, the authors introduce how to find matched features from scene images efficiently. The epipolar geometry can restrict the point correspondences to the polar line, but cannot cope with the false points lying on the line. To eliminate such mismatches, the authors present an affine constraint which can also merge the uniform regions produced by mean-shift segmentation. Second, inspired by the success of random sample consensus, the authors moderately improve the weighting function based on M-estimator to increase the accuracy of the fundamental matrix estimation. Experimental results on simulated data and real images show these works are efficient for estimating fundamental matrix. The authors also evaluated the accuracy of their method on computing the external parameters of two cameras. The result shows that this method obtains comparable performance to the more sophisticated calibration method.

The quantitative analysis of the left ventricle (LV) contractile function is one of the key steps in the assessment of cardiovascular disease. Such analysis greatly depends on the accurate delineation of LV boundary from cardiac sequences. However, segmentation of the LV still remains a challenging problem due to its subtle boundary, occlusion, and image inhomogeneity. To overcome such difficulties, the authors propose a novel segmentation method by incorporating a dynamic shape constraint into the weighting function of the random walks segmentation algorithm. This approach involves iterative updates on the intermediate result to achieve the desired solution. The inclusion of a shape constraint restricts the solution space of the segmentation result to handle misleading information that may come from noise, weak boundaries and clutter, leading to increased robustness of the algorithm. The authors describe the details of the proposed method and demonstrate its effectiveness in segmenting the LV from real cardiac magnetic resonance (CMR) image sets. The experimental results demonstrate that the proposed method obtains better segmentation performance than the standard method.

This study proposes a new method for content-based image retrieval by finding an optimal classifier. The optimal classifier is achieved by a new active learning support vector machine (SVM) which combines the model selection with the active learning. The unlabelled samples close to the boundary of the SVM classifier are selected based on the feature similarity for the active learning, and the adaptive regularisation is used to select the optimal model. The combination of model selection with active learning accelerates the convergence of the classifier. The new method can improve the image retrieval accuracy and reduce the time consumption. The experimental results show that the proposed method has a better performance with fewer samples and less time consumption for image retrieval.

The aim of image retrieval systems is to automatically assess, retrieve and represent relative images-based user demand. However, the accuracy and speed of image retrieval are still an interesting topic of many researches. In this study, a new method based on sparse representation and iterative discrete wavelet transform has been proposed. To evaluate the applicability of the proposed feature-based sparse representation for image retrieval technique, the precision at percent recall and average normalised modified retrieval rank are used as quantitative metrics to compare different methods. The experimental results show that the proposed method provides better performance in comparison with other methods.