Compressed video sensing (CVS) is an application of compressed sensing theory which samples a signal below the Shannon–Nyquist rate. However, previous research about CVS has largely ignored the inter-frame correlation analysis in the measurement domain, and then is not able to remove the time redundancy. In this study, the authors consider the estimation of the measurements of a block in any possible position in a frame by introducing a correlation noise (CN) between the actual and the estimated measurements. In this work, they first establish a correlation model (CM) in the pixel domain between a block which is in a random unknown position in a frame and the adjacent non-overlapping blocks that they already have. Then, a novel measurement domain CM is presented to approximate the measurements for the random block. Lastly, they employ the CN to characterise the accuracy of the CM in the measurement domain. The simulation results show that the proposed model can make an accurate estimation to the actual measurements of an arbitrary block in a frame and that by using the proposed CN to perform motion estimation, they can improve the peak signal-to-noise ratio of the video sequences by 0.1–1.7 dB compared with the existing methods.

Fuzzy c-means (FCMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well-known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image segmentation applications. In this study, the authors propose a new algorithm to incorporate the merits of these two approaches and reveal some intrinsic relationships between them. In the authors model, the new objective function pays more attention on spatial constraints and adopts Gaussian distribution as the distance function. Thus, their model can degrade to the standard GMM as a special case. Our algorithm is fully free of the empirically pre-defined parameters that are used in traditional FCM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in their algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighbourhood to incorporate the local spatial information and intensity information. Finally, they utilise the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. The mean template is considered as a spatial constraint for collecting more image spatial information. Compared with HMRF, their method is simple, easy and fast to implement. The performance of their proposed algorithm, compared with state-of-the-art technologies including extensions of possibilistic fuzzy c-means (PFCM), GMM, FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.

Recently, genetic algorithm (GA) has been introduced as an effective method to solve the registration problem. It maintains a population of candidate solutions for the problem and evolves by iteratively applying a set of stochastic operators. Accordingly, a key question is how to reduce the population size. In this study, the authors present two techniques for reducing the population size in the GA for registration of partially overlapping point sets. Based on the trimmed iterative closest point algorithm, they introduce a growth operator into the GA. The growth operator, which is also inspired by the biological evolution, can improve the GA efficiency for registration. Furthermore, they present a technique called centre alignment to confirm the value range of all the registration parameters, which can reduce the search space and allow the well-designed GA to directly solve the registration problem. Experimental results carried out with the m-dimensional point sets illustrate its advantages over previous approaches.

In this study, a new quaternion filter for removal of Gaussian noise in colour images is presented. It is based on the quaternion representation of colour images and the optimisation of a tight bound of the quaternion mean-square error between the restored colour image and the original one, together with the essential idea of the non-local means filter. The optimal weights are obtained by using the method of Lagrange multipliers. The authors' quaternion optimal weights non-local means filter is given by the weighted means of the observed quaternion representation using the optimal weights. Experiments on commonly used images are provided to illustrate the efficiency of the proposed filter.

Diabetic retinopathy (DR) is a microvascular complication of long-term diabetes and it is the major cause of visual impairment because of changes in blood vessels of the retina. Major vision loss because of DR is highly preventable with regular screening and timely intervention at the earlier stages. The presence of exudates is one of the primitive signs of DR and the detection of these exudates is the first step in automated screening for DR. Hence, exudates detection becomes a significant diagnostic task, in which digital retinal imaging plays a vital role. In this study, the authors propose an algorithm to detect the presence of exudates automatically and this helps the ophthalmologists in the diagnosis and follow-up of DR. Exudates are normally detected by their high grey-level variations and they have used an artificial neural network to perform this task by applying colour, size, shape and texture as the features. The performance of the authors algorithm has been prospectively tested by using DIARETDB1 database and evaluated by comparing the results with the ground-truth images annotated by expert ophthalmologists. They have obtained illustrative results of mean sensitivity 96.3%, mean specificity 99.8%, using lesion-based evaluation criterion and achieved a classification accuracy of 99.7%.

Content-based image retrieval (CBIR) systems often incorporate a relevance feedback mechanism in which retrieval is adapted based on users identifying images as relevant or irrelevant. Such relevance decisions are often assumed to be category-based. However, forcing a user to decide upon category membership of an image, even when unfamiliar with a database and irrespective of context, is restrictive. An alternative is to obtain user feedback in the form of relative similarity judgments. The ability of a user to provide meaningful feedback depends on the interface that displays retrieved images and facilitates the feedback. Similarity-based 2D layouts provide context and can enable more efficient visual search. Motivated by these observations, this study describes and evaluates an interactive image browsing and retrieval approach based on relative similarity feedback obtained from 2D image layouts. It incorporates online maximal-margin learning to adapt the image similarity metric used to perform retrieval. A user starts a session by browsing a collection of images displayed in a 2D layout. He/she may choose a query image perceived to be similar to the envisioned target image. A set of images similar to the query are then returned. The user can then provide relational feedback and/or update the query image to obtain a new set of images. Algorithms for CBIR are often characterised empirically by simulating usage based on pre-defined, fixed category labels, deeming retrieved results as relevant if they share a category label with the query. In contrast, the purpose of the system in this study is to enable browsing and retrieval without predefined categories. Therefore evaluation is performed in a target-based setting by quantifying the efficiency with which target images are retrieved given initial queries.