The authors propose a novel and efficient method for single image dehazing. To accelerate the transmission estimation process, a block-to-pixel interpolation method is used for fine dark channel computation, in which the block-level dark channel is first computed, and then the fine pixel-level dark channel is obtained by a weighted voting of the block-level dark channel to preserve edges and smooth out texture noise. This technique can be used for a direct transmission map generation without a computationally expensive refinement step. Since the dark channel prior (DCP) is not valid in bright (sky) regions, they propose an adaptive DCP modelled by a Gaussian curve that produces a more natural recovered image of the sky and other bright regions. In addition, a scaling method for transmission map computation is proposed to further accelerate the dehazing method. Through experiments, they show that the proposed adaptive block-to-pixel technique is about 30 times faster and produces improved recovered images than the well-known state-of-the-art DCP approach.

With the development of high-speed, high-resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer-aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer-aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use.

Precise localisation of an optic disk (OD) in the retinal images is one of the most important problems in the ophthalmic image processing. Although a considerable progress has been made towards a computerised solution of the problem, the numerical algorithms often fail on retinal images characterised by poor quality. Therefore, the authors propose a new method suitable for low-quality images based on exploiting the convergence of the blood vessels to the OD. The novelty of the proposed techniques includes clustering the vessels endowed with a novel correction procedure and the vessel transform (VT) which measures the distance to the main clusters. The algorithm is integrated into the scale-space (SS) analysis to detect the boundary of the OD. The integrated method is called SS algorithm with VT (SSVT). SSVT has been tested on retinal images from two databases with fair and poor images against the fuzzy convergence (FC) method and a modification of the circular transform proposed by Lu. The absolute improvement on sensitivity of SSVT against FC and Lu's are up to 12.37% and 8.18%. Bigger improvements of SSVT in terms of positive predictive value are up to 37.46% and 30.84% against FC and Lu's, respectively.

Fast detection of skin colour in images is an important task for computer vision and image processing. In this study, a new thresholding method for detecting skin colour has been introduced. The proposed method uses CMYK colour space, which, although not popular in image processing, especially in skin detecting algorithm, turned out to be a good choice. Its properties seem the most appropriate for the task of skin colour detection. Some experiments have been worked out using Compaq Database – a large database of skin and non-skin photos. A comparison of the new method against well-known thresholding methods in RGB, YCbCr and HSV spaces were prepared. In this comparison, the proposed method in CMYK colour space wins. This good result was further improved using a simple cascade algorithm, where two spaces RGB and CMYK were used.

Saliency detection plays an important role in image segmentation, object detection and retrieval, which attracts more attention in the field of computer vision recently. Most existing saliency detection algorithms have not considered the influence of visual focus (VF) shifting yet. In this study, a novel algorithm named moving region contrast (MRC) is proposed to analyse image saliency. The algorithm MRC is built on a novel concept of moving VF. The initial VF is defined as the geometric centre of the image. Then the VF is calculated iteratively by focus-moving technique where a saliency gravitation model is employed to determine the moving direction. The salient region is obtained according to the final VF. The experiments are conducted on the dataset with 1000 images released by Achanta. Experimental results show that the proposed algorithm achieves marked improvements in performance and outperforms other 11 popular algorithms.

As smart audio-visual multimedia devices are developed for various applications, there has been a growing interest in effective human–computer interaction (HCI) interfaces for specific environments. There have also been great efforts to implement HCI interfaces into musical instruments, in which it would be possible to take intuitions, comfort and expressiveness into the musical instruments. However, most of the traditional HCI interfaces are not applicable because both hands are likely to be occupied while playing a musical instrument. In this environment, a lip reading method can be used. A lip reading method is a HCI method that analyses lip motion to recognise spoken words. In this study, a lip reading method is proposed with its application. As a specific example of the interface for musical instruments, the guitar effector application is presented. The proposed lip reading method uses a constrained local model instead of the conventional active appearance model for effective facial feature tracking. The proposed method also uses a dynamic time warping-based classifier for word recognition which is effective for simple real-time implementation of lip reading. The proposed lip reading method shows 85.0% word recognition accuracy on OuluVS database and is effectively applied to the proposed guitar effector application.

In this study, the problem of variable bit rate (VBR) video traffic prediction is addressed. VBR traffic prediction is necessary in dynamic bandwidth allocation for multimedia quality of service control strategies. Autoregressive (AR) models have been widely used in VBR traffic prediction where the least mean square (LMS)-based methods were utilised for parameter estimation. However, they are ineffective when the traffic is dynamic in nature. In this study, using the Brock, Dechert, and Scheinkman (BDS) test, it is shown that the video traffic is non-linear. Kernel is an efficient tool to convert non-linear data into linear one in a higher-dimensional space. The kernel LMS (KLMS) method is proposed to forecast the next frame sizes of I, B and P frames as well as the next group-of-pictures (GOP) size of video traffic. Extensive simulations were performed on different video traces where different performance metrics were considered. KLMS results were very close to those of the Wiener–Hopf optimum solution and better than the results of commonly used normalised LMS and other algorithms such as the least mean kurtosis (LMK), wavelet LMK, adaptive network fuzzy inference system (ANFIS) and neural networks.

A digital image watermarking algorithm using partial pivoting lower and upper triangular (PPLU) decomposition is proposed. In this method, a digital watermark image is factorised into lower triangular, upper triangular and permutation matrices by PPLU decomposition. The permutation matrix is used as the valid key matrix for authentication of the rightful ownership of the watermark image. The product of the lower and upper triangular matrices is embedded into particular sub-bands of a cover image that is decomposed by wavelet transform using the singular value decomposition. The weightage-based differential evolution algorithm is used to achieve the possible scaling factor for obtaining the maximum possible robustness against various image processing operations and pirate attacks. The authors experiments show that the proposed algorithm is highly reliable with better imperceptibility of the embedded image and computationally efficient compared with recently existed methods.

In this study, the authors present an efficient method for approximate large-scale image indexing and retrieval. The proposed method is mainly based on the visual content of the image regions. Indeed, regions are obtained by a fuzzy segmentation and they are described using high-frequency sub-band wavelets. Moreover, because of the difficulty in managing a huge amount of data, which is caused by the exponential growth of the processing time, approximate nearest neighbour algorithms are used to improve the retrieval speed. Therefore they adopted locality-sensitive hashing (LSH) for region-based indexing of images. In particular, since LSH performance depends fundamentally on the hash function partitioning the space, they exposed a new function, inspired from the E ₈ lattice, that can efficiently be combined with the multi-probe LSH and the query-adaptive LSH . To justify the adopted theoretical choices and to highlight the efficiency of the proposed method, a set of experiments related to the region-based image retrieval are carried out on the challenging ‘Wang’ data set.

Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this study, the authors propose a compressed sensing-based algorithm for the detection of moving object. They first use a practical three-dimensional circulant sampling method to yield sampled measurements. Then, they propose an object detection model to simultaneously reconstruct the foreground support, background and video sequence using the sampled measurements directly. Experimental results show that the proposed moving object detection algorithm outperforms the state-of-the-art approaches and it is robust to the movement turbulence, camera motion and video noise.

Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple hypotheses testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. This study presents a hierarchal tracking system in two levels to solve this problem. For each point in the lower-level, a multi objective particle swarm optimisation technique is applied to a group of consecutive frames to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper-level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial information and fitness values. The experimental results show that the proposed method has a superior performance in relation to some common dim object tracking methods over different image sequence data sets.

This study presents a robust motion deblurring method in which an adaptive prediction is used to extract the informative regions for kernel estimation. The prediction not only sharpens the blurry edges, but also adaptively predicts the large scale structure for kernel estimation. It allows to only use the alternating minimisation with a computationally efficient Gaussian prior for both the image and kernel while without employing thoughtful attention such as multi-scale scheme or kernel refinement. Extensive experiments were carried out to validate the proposed method and to compare it with some previous approaches. The experiment results demonstrated that the approach achieves, if not better than, state-of-the-art results for uniformly blurred images.

Content representation for images with well-defined inter-class boundaries in the feature space remains to be a difficult task. Simple distance-based retrieval (SDR) approaches those operate on the feature space for content-based image retrieval (CBIR) are, therefore claimed to be inefficient by many researchers. Different CBIR approaches have been proposed to surmount the drawbacks of SDR scheme. This study proposes a novel image retrieval scheme. In this scheme, effort is taken to reduce the overall search time of the recently proposed approach called ‘class membership-based retrieval’ (CMR). The proposed method identifies the confidence in the classification and limits the search to single output class and therefore, reduces the overall search time by 21.76% as compared to CMR. Quantitative methods are proposed to select various parameters used in the algorithm which were computed empirically in the case of earlier approach CMR. The computed parameters are validated using experimental results. The consistent behaviours of the proposed method and earlier methods used in the experiment are demonstrated using different feature sets and distance metrics. While the method can be used as a general purpose image retrieval system, experiment is performed on four texture databases wit different complexities in terms of size, number of texture classes and orientation.