Salient object detection has been revolutionised by convolutional neural network (CNN) recently. However, it is hard to transfer the state-of-the-art still-image based saliency detectors to videos directly, owing to the neglect of temporal contexts between frames. In this study, the authors propose a flow-driven attention network (FDAN) to exploit motion information for video salient object detection

The crucial hurdle faced by the watermarking technique is to maintain the steadiness corresponding to several attacks while assisting a sufficient level of security. In this study, a robust lossless sparse domain-based watermarking approach combined with discrete cosine transform (DCT) is introduced to hide the secret message in the selected significant sparse elements of the host image. The proposed

In recent years, many image encryption approaches have been proposed on the basis of chaotic maps. The various types of chaotic maps such as one-dimensional and multi-dimensional have been used to generate the secret keys. Chaotic maps require some parameters and value assignment to these parameters is very crucial. Because, poor value assignments may make the chaotic map un-chaotic. Therefore, hyper-parameter

During the process of watching 3D synthesised video (3D-SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D-SV with binocular asymmetric distortions is a new and challenging problem. Aiming

Classical morphological filters suffer from well performing in a noisy environment, and intrinsic image structures are not taken into account. The authors propose here an alternative to overcome such weaknesses, by properly using robust shock filters and inhomogeneity. Thus, they obtain multiscale morphological operators by using image edge functions as local weights in inhomogeneous Hamiltonians in

Single image reflection removal is an ill-posed problem. To solve this problem, this study develops a network structure based on a deep encoder–decoder RRnet. Unlike most deep learning strategies applied in this context, the authors find that redundant information increases the difficulty of predicting images on the network; thus, the proposed method uses mixed reflection image cascaded edges as input

Action recognition has been an active research area for many years. Extracting discriminative spatial and temporal features of different actions plays a key role in accomplishing this task. Current popular methods of action recognition are mainly based on two-stream Convolutional Networks (ConvNets) or 3D ConvNets. However, the computational cost of two-stream ConvNets is high for the requirement of

In many scenarios, where videos are transmitted through bandwidth-limited channels for subsequent semantic analytics, the choice of frame rates has to balance between bandwidth constraints and analytics performance. Faced with this practical challenge, this study focuses on enhancing object tracking at low frame rates and proposes a learning Interpolation for tracking framework. This framework embeds

The principle of generative adversarial net is to fit the given data distribution by combining a generative model and discriminative model. There are two major challenges to conventional systems – they are difficult to train and they easily fall into ‘mode collapse’. To improve it, this study describes a novel network structure with dual generators. A ‘cooperation’ mechanism is introduced to help the

Retinal blood vessels, the diagnostic bio-marker of ophthalmologic and diabetic retinopathy, utilise thick and thin vessels for diagnostic and monitoring purposes. The existing deep learning methods attempt to segment the retinal vessels using a unified loss function. However, a difference in spatial features of thick and thin vessels and a biased distribution creates an imbalanced thickness, rendering

Automated pancreas segmentation in abdominal computed tomography (CT) scans is of high clinical relevance (i.e. pancreas cancer diagnosis and prognosis), but extremely difficult because the pancreas is a soft, small, and flexible abdominal organ with high anatomical variability, which causes the previous segmentation methods to result in low precision. In this study, the authors present a new deep

This study introduces an effective solution for text-independent writer identification by generalising contour-hinge feature, which is called n -tuple direction feature. For extracting n -tuple direction feature, the authors first obtain all contours from connected components, then n + 1 points are considered on the contour with a certain distance apart, and next, the directions of the fragments connecting

Contrast enhancement of an image can be performed by using a simple histogram equalisation (HE) technique. However, there are some drawbacks of HE like immense brightness change, artificial effects, over-enhancement, which make it unsuitable to be used in many applications. To resolve these issues a new adaptive heuristic HE approach is proposed in this study. First, probability distribution function

Encryption is a very important way to secure data in storage and communication, and it is a process of encoding messages or information in such a manner that only authorised persons can access it. Different techniques are used to protect confidential image data against illicit access. In image encryption using chaotic systems, most authors use or design algorithms to generate the initial parameters’

Part of important structural edges in the image is smoothed due to the small gradients, while the others are preserved with greater gradients. Therefore, the authors propose a two-stage image smoothing method based on edge-patch histogram equalisation and patch decomposition. The authors' purpose is to increase the gradient of important structural edges while reducing the gradient of the texture region

Fire detection systems in ships are based on smoke and heat detection in accordance with safety regulations. The rapid advancement of machine vision technology has led to the development of video smoke detection (VSD) systems. In this study, a VSD system is applied to smoke detection within the engine room of the ship. A dataset for a range of scenarios was created with a smoke generator. The method

In natural rainy scenes, visibility is significantly degraded by two types of phenomena: specular highlights of nearby individual rain streaks and atmospheric veiling effect caused by distant accumulated rain. However, most existing deraining methods only take the first kind of degradation into consideration, which limits their potential application in heavy rain. In this study, a joint rain and atmospheric

The authors know that a common and effective way to protect digital images security are to encrypt these images into white noise image. In this study, the authors have designed a new two-dimensional (2D) chaotic system which is derived from two existing one-dimensional (1D) chaotic maps. The simulation results show that the new 2D chaotic system is able to produce many 2D chaotic maps by selecting

Image denoising under low signal-to-noise ratio (SNR) and non-Gaussian noise is still a challenging problem in image processing. In this study, the authors prose a kind of improved convolution neural network auto-encoders for image denoising. Different from other priors based methods, the denoising auto-encoders (DAEs) can learn end-to-end mappings from noisy images to the target ones. This study research

Haze and fog removing from videos and images has got massive concentration in the field of video and image processing because videos and images are severely affected by fog in tracking and surveillance system, object detection. Different defogging techniques proposed so far are based on polarisation, colour-line model, anisotropic diffusion, dark channel prior (DCP) etc. However, these methods are

In this study, the authors focus on the task of leaf segmentation under different imaging conditions (e.g. backgrounds and shadows). A new method - robust random walk (RW) is proposed to propagate the prior of user's specified pixels. Specifically, they first employ RWs to take the relationship of pairwise pixels into consideration. A superpixel-consistent constraint is added to make the edges of segmentation

The authors consider the problem of compressive sensed video recovery via iterative thresholding algorithm. Traditionally, it is assumed that some fixed sparsifying transform is applied at each iteration of the algorithm. In order to improve the recovery performance, at each iteration the thresholding could be applied for different transforms in order to obtain several estimates for each pixel. Then

The authors propose a novel HEp-2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state-of-the-art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns

This study presents a novel encryption method for the security of DICOM (Digital Imaging and Communications in Medicine) images used in medical applications. The proposed algorithm splits the original image into blocks of size 16 × 16 pixels, then encrypts it through three steps. Firstly, the keys k 1 , k 2 , k 3 and k 4 are transformed from four vectors of 16 pixels to a matrix of 16 × 16 pixels.

In this study, the authors investigate the problem of enhanced image no-reference (NR) quality assessment. For resolving the problem of the enhanced images, it is difficult to obtain reference images, this study proposes an NR image quality assessment (IQA) model based on colour space distribution. Given an enhanced image, our method first uses a gist to select a clear target image in which the scene

Human motion analysis has received a lot of attention in the computer vision community during the last few years. This research domain is supported by a wide spectrum of applications including video surveillance, patient monitoring systems, and pedestrian detection, to name a few. In this study, an improved cascaded design for human motion analysis is presented; it consolidates four phases: (i) acquisition

Infrared imaging is less susceptible to illumination conditions and haze than visible light imaging. The advantage makes infrared sensing suitable for providing remote visibility with reduced distortion. However, infrared images tend to have low resolution and lack rich textures that facilitate stereo matching. To enhance the applicability of infrared stereo imaging, the authors re-examine the guided-image

Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a novel sampling method called random neighbourhood resampling

There is an urgent need for a robust video quality assessment (VQA) model that can efficiently evaluate the quality of a video content varying in terms of the distortion and content type in the absence of the reference video. Considering this need, a novel no reference (NR) model relying on the spatiotemporal statistics of the distorted video in a three-dimensional (3D)-discrete cosine transform (DCT)

Image classification is very important in pattern recognition and computer vision, where, for integrating final representation, feature pooling methods of the max-pooling, sum-pooling and average-pooling have been widely used. In this study, the authors propose a new method called K -strongest responses (KSRs) on the dictionary atoms for integrating the coding coefficients to generate the final representation

As an important, challenging, and difficult problem in image processing, multiplicative noise removal (MNR) has attracted great attention. To this end, many variational methods have been effectively proposed in the past few decades. Among these variational methods, total variation (TV) and its higher-order extensions are very effective, where the former can preserve sharp edges but cause some undesirable

Managing large personal image databases requires efficient privacy preserving indexing methods to allow for their outsourcing to possibly curious cloud servers. To construct a secure inverted index in this paper, first, visual words are extracted from stored images based on the Speeded-Up and Robust Features (SURF). Next, Order Preserving Encryption (OPE) is used to encipher the frequencies of occurrence

Visual evaluation of many magnetic resonance images is a difficult task. Therefore, computer-assisted brain tumor classification techniques have been proposed. These techniques have several drawbacks or limitations. Capsule based neural networks are new approaches that can preserve spatial relationships of learned features using dynamic routing algorithm. By this way, not only performance of tumor

Lung nodule segmentation is an interesting research topic, and it serves as an effective solution for the diagnosis of Lung cancer. The existing methods of lung nodule segmentation suffer from accuracy issues due to the heterogeneity of the nodules in the lungs and the presence of visual deviations in the nodules. Thus, there is a requirement for an effective lung nodule segmentation, which assists

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention

In this study, the authors propose a novel methodology for static gesture recognition in a complex background using only depth map from Microsoft's Kinect camera. Four different types of features are extracted and analysed on two public static gesture datasets. The features extracted from the segmented hand are geometrical, local binary patterns, number of fingers ( Num ) raised in a gesture and distance

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which makes the objects and their features difficult to be identified by computer vision systems. In the past decades, image de-hazing is used to remove the influence of weather factors and improve image visualisation in hazy scenes by providing easy image post-processing towards human

The scale-invariant feature transform (SIFT) algorithm is the most widely used feature extraction as well as a feature matching method in remote sensing image registration. However, the performance of this algorithm is affected by the influence of speckle noise in synthetic aperture radar (SAR) images. It reduces the number of correct matches as well as the correct matching rate in SAR image registration

Nowadays, traffic sign recognition is the most important task of advanced driver assistance systems since it improves the safety and comfort of drivers. However, it remains a challenging task due to the complexity of road traffic scenes. In this study, a novel two-stage approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. The first stage aims to detect

The location of the smallest object in a scene plays an essential role in the perception of a viewer. Any tampering with it, may evolve in adverse consequences especially with surveillance videos of banks, ATMs, traffic monitoring etc. Therefore, a scientific approach is required to thoroughly observe the fine details of tampering (forgery) in a video. A spatio-temporal detection method is proposed

Offline recognition of handwritten text in Indian regional scripts is a major area of research as nearly 910 million people use such scripts in India. Most of the reported research works on Indian script-based optical character recognition (OCR) system have focused on a single script only. Research for developing methodologies that are capable of handling more than one Indian script is yet to be focused

In this study, the critical factor of the chaos system has been analysed to improve the randomicity of the encryption keyspace. Several chaos systems have been integrated together with a linear function to form a much more efficient key sequence generator. This study also presents the cross colour field confusion method which scrambles the pixels among the R G B colour matrixes. In this way, the range

This paper presents new image descriptors for heterogeneous face recognition (HFR). The proposed descriptors combine directional and neighborhood information using a rotating spoke and concentric rings concept. We name the descriptors as multi-directional local adjacency descriptors (MDLAD). This family of descriptor captures the directional information through successive rotations of a pair of orthogonal

Apoptotic programmed cell death (PCD) is a fundamental aspect of developmental maturation. However, the authors' understanding of apoptosis, especially in the multi-cell regime, is incomplete because of the difficulty of identifying dying cells by conventional strategies. Real-time in vivo microscopy of Drosophila, an excellent model system for studying the PCD during development, has been used to

Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area)