Identification of JPEG compressed images saved in uncompressed format (JPEG-U images) is an important issue in forensic analysis. The state-of-the-art JPEG compression detection methods fail to identify such images when subjected to post-processing/anti-forensic operations. In this paper, we propose a novel JPEG compression detector which is robust to post-processing and anti-forensic operations. The

Compressed sensing (CS) aims to precisely reconstruct the original signal from under-sampled measurements, which is a typical ill-posed problem. Solving such a problem is challenging and generally needs to incorporate suitable priors about the underlying signals. Traditionally, these priors are hand-crafted and the corresponding approaches generally have limitations in expressive capacity. In this

Different artifacts will manifest, whenever an image is compressed by a lossy compression algorithm. Higher frequency details present in the image may tend to be eliminated by compression. In certain cases, compression may introduce small image structures and noise. This phenomenon will limit the image quality thereby making images to appear much less pleasant to the human eye. Furthermore, other machine

In this paper, we propose a face anti-spoofing strategy by using DWT (Discrete Wavelet Transform), LBP (Local Binary Pattern) and DCT (Discrete Cosine Transform) with a SVM classifier to evaluate whether a video is valid. Firstly, the DWT features are produced by decomposing some selected frames into different frequency components at the 8∗8 multi-resolution blocks. Secondly, the DWT-LBP features are

As cloud storage becomes more popular, concerns about data leakage have been increasing. Encryption techniques can be used to protect privacy of videos stored in the cloud. However, the recently proposed sketch attack for encrypted H.264/AVC video, which is based on the macroblock bitstream size (MBS), can generate the outline images of both intra-frames and inter-frames from a video encrypted by most

In this article, we have investigated a VR simulator of a forestry crane used for loading logs onto a truck. We have mainly studied the Quality of Experience (QoE) aspects that may be relevant for task completion, and whether there are any discomfort related symptoms experienced during the task execution. QoE experiments were designed to capture the general subjective experience of using the simulator

CNN-based steganalysis has recently achieved very good performance in detecting content-adaptive steganography. At the same time, recent works have shown that, by adopting an approach similar to that used to build adversarial examples, a steganographer can adopt an adversarial embedding strategy to effectively counter a target CNN steganalyzer. In turn, the good performance of the steganalyzer can

As an effective feature representation method, non-negative matrix factorization (NMF) cannot utilize the label information sufficiently, which makes it not be suitable for the classification task. In this paper, we propose a joint feature representation and classification framework named adaptive graph semi-supervised nonnegative matrix factorization (AGSSNMF). Firstly, to enhance the discriminative

Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time; hence, a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, (1): to provide a comprehensive and in-depth survey of the deep learning-based

Zero-shot learning (ZSL) aims to recognize new objects that have never seen before by associating categories with their semantic knowledge. Existing works mainly focus on learning better visual-semantic mapping to align the visual and semantic space, while the effectiveness of learning discriminative visual features is neglected. In this paper, we propose an object-centric complementary features (OCF)

License plate recognition is an emerging topic for real-time applications in smart city development because of automatic systems for toll fee-paying, traffic controlling, and vehicle detection. Speed of vehicles, unpredictable weather conditions such as night/low light/limited light images, and capturing images at different angles make recognition harder. This paper presents a new Fractal Series Expansion

Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution

Video frame interpolation is a technology that generates high frame rate videos from low frame rate videos by using the correlation between consecutive frames. Presently, convolutional neural networks (CNN) exhibit outstanding performance in image processing and computer vision. Many variant methods of CNN have been proposed for video frame interpolation by estimating either dense motion flows or kernels

There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires

We address the problem of learning representations from the videos without manual annotation. Different video clips sampled from the same video usually have a similar background and consistent motion. A novel self-supervised task is designed to learn such temporal coherence, which is measured by the mutual information in our work. First, we maximize the mutual information between features extracted

While most existing 3D shape representation features exploit either surface or volumetric information to describe the mesh, in this paper we propose a method that efficiently combines this dual information. Using both representations, convex as well as concave regions are captured, a property which is necessary for effective shape segmentation. The method extends the well-known volumetric visibility

The human visual system has the ability to rapidly identify and redirect attention to important visual information in high complexity scenes such as the human crowd. Saliency prediction in the human crowd scene is the process using computer vision techniques to imitate the human visual system, predicting which areas in a human crowd scene may attract human attention. However, it is a challenging task

Digital image watermarking has become a necessity in many applications such as data authentication, broadcast monitoring on the Internet and ownership identification. Various watermarking schemes have been proposed to protect the copyright information. There are three indispensable, yet contrasting requirements for a watermarking scheme: imperceptibility, robustness and payload. Therefore, a watermarking

In this paper, we address the problem of long-term visual object tracking and we present an efficient real-time single object tracking system suitable for integration in autonomous platforms that need to encompass intelligent capabilities. We propose a novel long-term tracking framework for classification based re-detection and tracking, that incorporates state estimation, object re-identification

In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. Image deblurring task is valuable and challenging in computer vision. However, existing learning-based methods cannot produce images with clear edges and fine details, which exhibit significant challenges for generated-based loss functions used in existing methods. Instead of only

Accurately identifying cloud types in images has multiple uses from meteorological science to computer graphics, especially as clouds are a major factor influencing atmospheric radiative transport. Understanding which cloud types are present in an image is typically performed on a coarse scale, where cloud types are identified per image, but do not permit a finer, per-pixel granularity of labelling

The increasing popularity of video gaming competitions, the so called eSports, has contributed to the rise of a new type of end-user: the passive game video streaming (GVS) user. This user acts as a passive spectator of the gameplay rather than actively interacting with the content. This content, which is streamed over the Internet, can suffer from disturbing network and encoding impairments. Therefore

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature

Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges

The low cost, agility, and mobility of cloud services for processing and storage data have attracted user’s attention today. Untrusted third parties support these services, and users are always concerned about utilizing them for personal data. Addressing these data-privacy issues for image inpainting over the cloud infrastructure(s), we propose a model, 2DInpaint, to perform image inpainting by protecting

It still remains a challenging problem to improve both of the imperceptibility and robustness for the digital image watermarking task. In this study, a color image watermarking scheme is proposed based on quaternion polar harmonic transform (QPHT) with maximum likelihood decoder, which better balances imperceptibility and robustness in a novel fashion. Thanks to the strong robustness of QPHT, the digital

In a previous work, we proposed a color extension of an existing focal-plane tone-mapping operator (FPTMO) and introduced circuit modifications that led to smaller sensing array area and simpler off-chip white-balance operations. The proposed FPTMO chip was fabricated and its experimental test setup is under development. In the present work, we systematically compare the previously proposed FPTMOs

The protection of 3D contents from illegal distribution has attracted considerable attention and depth-image-based rendering (DIBR) is proved to be a promising technology for 3D image and video displaying. In this paper, we propose a new digital watermarking scheme for DIBR 3D images based on feature regions and ridgelet transform (RT). In this scheme, the center view and the depth map are made available

Visible–infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background

Super-resolution reconstruction technology has important scientific significance and application value in the field of image processing by performing image restoration processing on one or more low-resolution images to improve image spatial resolution. Based on the SCSR algorithm and VDSR network, in order to further improve the image reconstruction quality, an image super-resolution reconstruction

Very recently, with the widespread research of deep learning, its achievements are increasingly evident in image inpainting tasks. However, many existing methods fail to effectively reconstruct vivid contents and refine structures. In order to solve this issue, in this paper, a novel two-stage generative adversarial network based on the fusion of edge structures and color aware maps is proposed. In

Saliency detection is still very challenging in computer vision and image processing. In this paper, we propose a novel visual saliency detection framework via bagging-based saliency distribution learning (BSDL). Given an input image, we firstly segment it into superpixels as basic units. Then two prior knowledge containing background prior and center prior are integrated to generate an initial prior

In the literature of secret sharing, the property of essential secret sharing scheme ensures that no set of participants, missing at least one essential participant, should be able to get any information regarding the secret. However, there are essential secret image sharing schemes available in the literature in which we have shown mathematically as well as through experiments that any k or more non-essential

The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic

Image retrieval is an important research topic in pattern recognition and computer vision. We propose a novel content-based image retrieval method. At the image description stage, our method first modifies the traditional micro-structure descriptor (MSD) to capture the direct relationship between the shape and texture features and that between the color and texture features. Our method then extracts

Underwater image processing technologies have always been challenging tasks due to the complex underwater environment. Images captured under water are not only affected by the water itself, but also by the diverse suspended particles that increase the effect of absorption and scattering. Moreover, these particles themselves are usually imaged on the picture, causing the spot noise signal to interfere

We develop a novel no-reference image quality assessment model for stereoscopic 3D (S3D) images that is inspired by functional receptive field models of perceptual mechanisms in primary visual cortex (V1). The approach is called the Blind S3D Integrated Quality Evaluator (BSIQE). BSIQE simulates monocular and binocular responses to stereo views using channel separation and weighted multi-channel combination

Inspired by multiple information processing mechanisms of the human nervous system, a fusion simplified pulse coupled neural network (FSPCNN) model for infrared (IR) image segmentation is proposed in this paper. In the method based on FSPCNN, the time decay factor is set adaptively based on Stevens’ power law, and the synaptic weight is generated adaptively based on Lateral Inhibition (LI), without

Generalized zero shot classification aims to recognize both seen and unseen samples in test sets, which has gained great attention. Recently, many works consider using generative adversarial network to generate unseen samples for solving generalized zero shot classification problem. In this paper, we study how to generate discriminative and meaningful samples. We propose a method to learn discriminative

A common processing scheme of Visual Cryptography (VC) consists of generating two ciphered images from a secret image. The sender transmits these key images via two different channels. The receiver collects the key images and recovers the secret image by superimposing them. In this article, Voronoi tessellation is exploited in Visual Cryptography for the first time to the best of our knowledge. The

Hand pose estimation plays an important role in human–computer interaction and augmented reality. Regressing the joints coordinates is a difficult task due to the flexibility of the joint, self-occlusion and so on. In this paper, we propose a novel and simple hierarchical neural network for hand pose estimation. The hand joint coordinates are divided into six parts and each part is regressed in sequence

The level set method is widely used in medical image segmentation, in which the performance is seriously subject to the initialization and parameters configuration. An automatic segmentation method was proposed in this paper, which integrates fuzzy clustering with level set method through a dynamic constrained term in the new energy functional. It is able to use the results of fuzzy clustering directly

Performance of an orthogonal frequency division multiplexing (OFDM) system is greatest when the exact channel state information (CSI) is used for transmitter rate control and power allocation. However, in real systems CSI can only be approximately known. Moreover, in video communication, it can be difficult to use any CSI for rate control of a video codec if the channel changes significantly during

Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively

The Cover-Source Mismatch (CSM) has been long recognized as a major problem in modern steganography and steganalysis. Indeed, while a vast majority of works in steganography and steganalysis had been tailored to a specific reference database, namely BOSSbase, recent works show that, because of CSM, the results may greatly differ when changing this dataset. Although the CSM has already been the subject

Recently, as the number of cameras built into polydioptric cameras has increased, image rectification of these cameras has become complicated. However, because conventional methods cannot compensate for the calibration errors or are limited by the camera arrangement, they cannot be widely applied to various kinds of polydioptric cameras. In this work, we adopted the idea of disparity-error-minimization

At low bit rates, visually annoying blocking artifacts are usually introduced in JPEG compressed images. In this paper, we proposed an image deblocking method combined with the shape-adaptive low-rank (SALR) prior, the quantization constraint (QC) prior and sparsity-based detail enhancement. We firstly design a deblocking model to obtain initial deblocked images under the maximum a posteriori (MAP)

Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification. Besides the large intra-class variations owing to various illumination conditions, occlusions and varying poses, background clutters in the detected pedestrian bounding boxes further deteriorate

In computer vision, the reflections impair the overall perception of images and introduce undesired interference to high-level image understanding. To date, reflection removal approaches have been developed with advantage of visual quality enhancement. However, the reflection removal for single image is still a challenging task with its performance to be further boosted. During this process, additional

In this paper, a novel method to decompose an image into “intrinsic images” is introduced. This decomposition is based on the dichromatic model which separates the influence of specular and diffuse reflections. The separation of these components is very important in several applications of image analysis including segmentation, classification, recoloring, and specularity removal. The proposed method

Image feature extraction technology is one of main topics in the field of computer vision, which has been widely applied in biological recognition, image retrieval, target detection and other fields. To overcome the drawbacks of WLD under complex illumination condition, we propose a novel illumination–insensitive feature descriptor named as anisotropic Weber synergy gradient descriptor (AWSGD). The

Anaglyph reversion aims to recover the best possible approximation of a stereo pair of images from an anaglyph. Possible applications include a range of practical situations like enabling visualization of legacy anaglyphs on the Web, saving storage/transmission bandwidth by encoding stereo pairs as anaglyphs before stereo visualization or enabling users to enjoy stereo visualization using any available

Humans mainly communicate among them and with the world around them using light and vision, thus implying that visual representation technologies play a central role in human societies. While visual representation has been based on the 2D representation paradigm for many decades, multiple developments are nowadays pressing towards the adoption of more realistic and immersive 3D visual representation

In this study, we propose a new deep learning architecture named Multi-Level Dense Network (MLDNet) for multi-focus image fusion (MFIF). We introduce shallow and dense feature extraction in our feature extraction module to extract images features in a more robust way. In particular, we extracted the features from a mixture of many distributions from prior to the complex distribution through densely

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion