Local binary pattern (LBP) is sensitive to inverse grayscale changes. To overcome this problem, several methods map each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited description ability. In this paper, we introduce a generic histogram sorting method which exploits pattern transition rules to preserve the

Realistic image synthesis is a class of vision problems where the goal is to synthesize new images by fusing a source image and a target image. It is a challenging problem due to the visual gap between the two images, including geometry and appearance. To synthetically address the geometric distortion and appearance realism, we propose a novel method, the adversarial geometric consistency pursuit model

Hexagonal image sampling and processing are theoretically superior to the most commonly used square lattice based sampling and processing, but due to the lack of commercial image sensors, current research mainly relies on virtually hexagonally sampled data through square to hexagonal lattice conversion, which is a typical 2-D interpolation problem. This paper presents a simplified and efficient square

In this paper, a novel method for symmetric image encryption using Lorenz chaotic system is proposed. The main operations performed in the proposed system are keystream generation, permutation, and diffusion. In the permutation phase, both pixel-level and bit-level scrambling are performed to achieve the double scrambling property. The pixel-level scrambling is implemented by a novel approach using

Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional)

Shadowy areas present a big hindrance in target detection from HSI, as the reflectance data received from target materials is significantly diminished when measured from shadowy areas. In this work, we perform an in-depth analysis of hyperspectral target detection on targets in full illumination and in partial or full shadows; and analyze how much target detection can be improved if the hyperspectral

Head pose is an important cue in computer vision when using facial information. Over the last three decades, methods for head pose estimation have received increasing attention due to their application in several image analysis tasks. Although many techniques have been developed in the years to address this issue, head pose estimation remains an open research topic, particularly in unconstrained environments

Capturing the comprehensive information of various sizes and shapes of images in the same convolution layer is typically a challenging task in computer vision. There are two main kinds of methods for capturing those features. The first uses the inception structure and its variants. The second utilizes larger convolution kernels on specific layers or stacks with more convolution blocks. However, these

Computer vision and machine learning tools offer an exciting new way for automatically analyzing and categorizing information from complex computer simulations. Here we design an ensemble machine learning framework that can independently and robustly categorize and dissect simulation data output contents of turbulent flow patterns into distinct structure catalogs. The segmentation is performed using

Low-light images enhancement is a challenging task because enhancing image brightness and reducing image degradation should be considered simultaneously. Although existing deep learning-based methods improve the visibility of low-light images, many of them tend to lose details or sacrifice naturalness. To address these issues, we present a multi-stage network for low-light image enhancement, which

Image deraining is an extensively researched topic in low-level computer vision community. During the past, sufficient works have been proposed to address this problem. Though great improvements these methods have achieved, no derain network can confidently declare that it can solve rain removal problem perfectly. Single complex model may lead to overfitting, while simple model is too weak to achieve

In this paper, we propose an entropy minimization histogram mergence (EMHM) scheme that can significantly reduce the number of grayscales with nonzero pixel populations (GSNPP) without visible loss to image quality. We proved in theory that the entropy of an image is reduced after histogram mergence and that the reduction in entropy is maximized using our EMHM. The reduction in image entropy is good

Here, we propose a new 3D representation designed in the objective of face recognition independently of the expressions. It is defined on localities presenting limited deformations following expression variations. The proposed representation is based on the multipolar parameterization that we recently introduced in Jribi et al. (2019) which is relative invariant under three dimensional Euclidean transformations

MiE is a facial involuntary reaction that reflects the real emotion and thoughts of a human being. It is very difficult for a normal human to detect a Micro-Expression (MiE), since it is a very fast and local face reaction with low intensity. As a consequence, it is a challenging task for researchers to build an automatic system for MiE recognition. Previous works for MiE recognition have attempted

In multimedia communication systems, digital images may contain various visual contents, among which the natural scene images (NSIs) and screen content images (SCIs) are two important and common types. The existing full-reference image quality assessment (IQA) metrics are designed for only one type of images, but cannot precisely perceive the visual quality of another type. It is still unclear what

Recent years have witnessed tremendous improvements in multi-domain image-to-image translation. However, previous methods require multi-generator models or one-generator model with labeled datasets, which will increase the training cost and limit their further applications. In this paper, we propose an unsupervised network that consists of one pair of generator and discriminator as well as n encoders

Cyclic redundancy checks (CRC) are widely used in transmission protocols to detect whether errors have altered a transmitted packet. It has been demonstrated in the literature that CRC can also be used to correct transmission errors. In this paper, we propose an improvement of the state-of-the-art CRC-based error correction method. The proposed approach is designed to significantly increase the error

Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation

Hyperspectral imaging (HSI) is the emerging method that combines traditional imaging and spectroscopy to provide the image with both the spatial and spectral information of the object present in the image. The major challenges of the existing techniques for HSI classification are the high dimensionality of data and its complexity in classification. This paper devises a new technique to classify the

Underwater images contain an interacting mixture of distortions due to the physicochemical properties of the water, suspended organic matter and floating particles in water. Unlike images in traditional natural image quality databases, underwater images are often difficult to acquire with reference images and sets of images with gradient distortion. Therefore, it is even more difficult for the viewers

High Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally

While multi-source adaptation has recently received much attention due to its effectiveness, limited effort has been made on improving its performance by leveraging some shared knowledge among sources and features. To this end, we propose in this paper a robust Multi-source co-Adaptation framework by mining diverse Correlation Information (MACI) among domains as well as features via joint adaptive σ-norm

In this paper, we propose a spatio-temporal contextual network, STC-Flow, for optical flow estimation. Unlike previous optical flow estimation approaches with local pyramid feature extraction and multi-level correlation, we propose a contextual relation exploration architecture by capturing rich long-range dependencies in spatial and temporal dimensions. Specifically, STC-Flow contains three key context

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior

The lossy compression techniques at low bit rate often create ringing and contouring effects on the output images and introduce various blurring and distortion at block bounders. To overcome those compression artifacts different neural network based post-processing techniques have been experimented with over the last few years. The traditional loop-filter methods in the HEVC frame-work support two

This paper proposes a robust and reversible watermarking scheme for the encrypted image by using Paillier cryptosystem. In the proposed method, the original image is divided into a number of non-overlapping blocks sized by 8 × 8 and Paillier cryptosystem is applied to encrypt the pixels in each block. Firstly, a data hider can calculate the statistical values of encrypted blocks by employing modular

In recent years, removing rain streaks from a single image has been a significant issue for outdoor vision tasks. In this paper, we propose a novel recursive residual atrous spatial pyramid pooling network to directly recover the clear image from rain image. Specifically, we adopt residual atrous spatial pyramid pooling (ResASPP) module which is constructed by alternately cascading a ResASPP block

Applications of discrete orthogonal polynomials (DOPs) in image processing have been recently emerging. In particular, Krawtchouk, Chebyshev, and Charlier DOPs have been applied as bases for image analysis in the frequency domain. However, fast realizations and fractional-type generalizations of DOP-based discrete transforms have been rarely addressed. In this paper, we introduce families of multiparameter

Image source identification is important to verify the origin and authenticity of digital images. However, when images are altered by some post-processing, the performance of the existing source verification methods may degrade. In this paper, we propose a convolutional neural network (CNN) to solve the above problem. Specifically, we present a theoretical framework for different tampering operations

A recently proposed pixel-value-ordering (PVO)–based reversible data hiding (RDH) method takes a sharp histogram derived from two skewed histograms, which realizes good performance through reducing the number of shifted pixels. However, in this recent work, since pixels with different local complexity are processed with the same modification manner based on a single histogram without considering their

Deep learning has made remarkable progress in the field of image classification and object detection. Nevertheless, in the autonomous driving research, the real-time lane line detection and lane offset estimation in complex traffic scenes have always been challenging and difficult tasks. Traditional detection methods need manual adjustment of parameters, they face many problems and difficulties and

Analysis of first-person (egocentric) videos involving human actions could help in the solutions of many problems. These videos include a large number of fine-grained action categories with hand–object interactions. In this paper, a compositional verb–noun model including two complementary temporal streams is proposed with various fusion strategies to recognize egocentric actions. The first step is

Recently, discriminative learning methods have gained substantial interest in solving inverse imaging problems due to their decent performance and fast inferencing capability. Those methods need separate models for specific noise levels, which in turn require multiple models to be trained to denoise an image. However, images exhibit spatial variant noise which limits the applicability of such methods

In order to solve the problems of long time consuming and easy failure of the existing coarse registration algorithms based on global registration on two 3D point clouds with low overlap rate, we proposed a coarse registration algorithm based on feature regions and the Super 4-Points Congruent Sets (SUPER4PCS) algorithm. Firstly, intrinsic shape signatures (ISS) algorithm was used to extract and describe

Objects that occupy a small portion of an image or a frame contain fewer pixels and contains less information. This makes small object detection a challenging task in computer vision. In this paper, an improved Single Shot multi-box Detector based on feature fusion and dilated convolution (FD-SSD) is proposed to solve the problem that small objects are difficult to detect. The proposed network uses

3D Image representation is an important topic in computer vision and pattern recognition. Recently, 3D image analysis by fractional-order orthogonal moments has provided a new research direction, which has prompted researchers to think about efficient and fast classification. In this paper, the authors derived novel sets of fractional-order Legendre moment invariants (FrLMIs), for 3D object description

Underwater images often show severe quality degradation due to the light absorption and scattering effects in water medium. This paper introduces a scene depth regularized underwater image dehazing method to obtain high-quality underwater images. Unlike previous underwater image dehazing methods that usually calculate a transmission map or a scene depth map using priors, we construct an exponential

Compression and encryption are often performed together for image sharing and/or storage. The order in which the two operations are carried out affects the overall efficiency of digital image services. For example, the encrypted data has less or no compressibility. On the other hand, it is challenging to ensure reasonable security without downgrading the compression performance. Therefore, incorporating

Image fusion can integrate the complementary information of multiple images. However, when the images to be fused are damaged, the existing fusion methods cannot recover the lost information. Matrix completion, on the other hand, can be used to recover the missing information of the image. Therefore, the step-by-step operation of image fusion and completion can fuse the damaged images, but it will

Various improved methods based on the strategy of bag of words (BoW) are widely used to solve the problem of human action recognition. However, the spatial relationship between features is measured and utilized by these methods in a relatively single way. It limits the recognition performance of these methods. To solve this problem, double constrained bag of words (DC-BoW) is proposed to utilize the

Ridge regression is an important method in feature extraction and has been extended in many different versions. Robust discriminant regression aims to solve the Small Sample Size Problem (SSSP) in ridge regression form by designing a novel regression model and imposing on L2,1-norm as the main metric instead of the regularized term. However, when the dimensions of data are very high, RDR will be faced

Efficiently representing spatio-temporal features of dynamic textures (DTs) in videos has been restricted due to negative impacts of the well-known issues of environmental changes, illumination, and noise. In order to mitigate those, this paper proposes a new approach for an efficient DT representation by addressing the following novel concepts. Firstly, a novel filtering kernel, called Difference

Image texture description and analysis technology are the basis of many practical applications in pattern recognition. This paper presents a novel and simple, yet powerful method, namely multiple channels local binary pattern (MCLBP), which is the natural extension and development of local binary pattern (LBP) algorithm for color texture representation and classification. MCLBP combines single-channel

Generating image is a hot research topic in the field of deep learning, and it is challenging for generating high quality image pairs. The image pair refers to the corresponding image tuples with the same high-level features and different low-level features, generating high-quality image pairs has important applications in some specific fields. Currently, there are many methods to generate high quality

Video object segmentation, aiming to segment the foreground objects given the annotation of the first frame, has been attracting increasing attentions. Many state-of-the-art approaches have achieved great performance by relying on online model updating or mask-propagation techniques. However, most online models require high computational cost due to model fine-tuning during inference. Most mask-propagation

Image cryptosystems aim to secure the transmission of images in the presence of adversaries in the network. For ensuring confidentiality, images are encrypted to form unintelligible cipher images, the algorithms of which are much different from those for text data. Most image cryptosystems follow confusion–diffusion or complex architectures, which involves permutation and substitution of pixel values

Depth maps contain 3D geometrical information of a scene which can be useful in many applications. In this work we address the task of inpainting a depth map afflicted by regions with missing data. Initially, we inpaint depth values missing at random locations as well as due to overlaid text. Subsequently, we propose an approach for filling large holes in the input depth map wherein superpixel division

Crowd counting is a conspicuous task in computer vision owing to scale variations, perspective distortions, and complex backgrounds. Existing research usually adopts the dilated convolution network to enlarge the receptive fields to solve the problem of scale variations. However, these methods easily bring background information into the large receptive fields to generate poor quality density maps

There have been remarkable improvements in the salient object detection in the recent years. During the past few years, graph-based saliency detection algorithms have been proposed and made advances. Nevertheless, most of the state-of-the-art graph-based approaches are usually designed with low-level features, misleading assumption, fixed predefined graph structure and weak affinity matrix, which determine

SoftCast-based linear video delivery (LVD) schemes have been proposed as an alternative to traditional video transmission schemes in wireless error-prone environments. The end-to-end performance of SoftCast-based schemes have been evaluated in Xiong et al. (2016), where a theoretical model based on the Peak Signal-to-Noise Ratio (PSNR) metric has been proposed. The latter is limited to the use of a

A double color image encryption method based on DNA (deoxyribonucleic acid) computation and chaos is proposed. Differently from the conventional algorithms, double color images are encrypted at the same time so that we can save information of each other, which makes the encryption more safe and reliable. In addition, a new chaotic fractional order (FO) discrete improved Henon map (FODIHM) is proposed

An efficient learning scheme is proposed for hypergraph ranking based on multiple optimizations. This scheme dynamically optimizes the hypergraph structure through the incidence matrix and employs adaptive hyperedge weight estimation based on gradient descent method. By doing so, optimized hypergraph ranking vectors are derived, which recommend images of places of interest (POIs) worth visiting or

Free navigation of a scene requires warping some reference views to some desired target viewpoint and blending them to synthesize a virtual view. Convolutional Neural Networks (ConvNets) based methods can learn both the warping and blending tasks jointly. Such methods are often designed for moderate inter-camera baseline distance and larger kernels are required for warping if the baseline distance

Objective quality assessment of digital holograms has proven to be a challenging task. While prediction of perceptual quality of the recorded 3D content from the holographic wavefield is an open problem; perceptual quality assessment from content after rendering, requires a time-consuming rendering step and a multitude of possible viewports. In this research, we use 96 Fourier holograms of the recently

Existing texture filtering techniques acquiesce in the removal of all undesired background texture patterns simultaneously, and lead to the disappearance of small-scale foreground structures. Differently, this paper considers both multi-scale structures and specified texture patterns within an image as significant image contents, and presents a content-aware texture filtering approach via an easy-to-use