With the advance in content-based image retrieval and the popularity of Data-as-a-Service, enterprises can outsource their image retrieval systems on cloud platforms to reduce heavy storage, computation, and communication burdens. However, this brings many privacy problems. Although several privacy-preserving image retrieval schemes have been proposed to protect users’ privacy, they have two major

Image compression systems that exploit the properties of the human visual system have been studied extensively over the past few decades. For the JPEG2000 image compression standard, all previous methods that aim to optimize perceptual quality have considered the irreversible pipeline of the standard. In this work, we propose an approach for the reversible pipeline of the JPEG2000 standard. We introduce

Lane detection is a useful technique in modern autonomous vehicles systems, which assists vehicle to accurately localize itself according to detected road lines. Traditional methods leveraged edge detection and Hough transform based algorithms to plot lines along the detected lane. Noticeably, they did not take the informative feature road gradient into account. In addition, most previous deep learning-based

Human action recognition plays an important role in modern intelligent systems, such as human–computer interaction (HCI), sport analysis, and somatosensory game. Compared with conventional 2-D based human action analysis, using Kinect sensor can obtain depth information of human action, which is significant for human action recognition. In this paper, we propose a joint angle sequence model for recognizing

The task of object tracking is very important since its various applications. However, most object tracking methods are based on visible images, which may fail when visible images are unreliable, for example when the illumination conditions are poor. To address this issue, in this paper a fusion tracking method which combines information from RGB and thermal infrared images (RGB-T) is presented based

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied

Target recognition is a key module in modern human–computer interaction (HCI) and computer vision systems It is pervasively used in many domains like autonomous vehicles and robot, remote operation, and video surveillance. However, due to the complicated environment and object occlusion, target recognition is still a challenging task. In this paper, we propose a novel target recognition algorithm toward

Detecting and understanding human action under sophisticated lighting condition and backgrounds, also known as human action recognition in real-world context, is an indispensable component in modern intelligent systems and has becoming a hot research topic currently. Nowadays, human action recognition is still a tough challenge due to intra-class and inter-class, environment and temporal-level differences

Intelligently tracking objects with varied shapes, color, lighting conditions, and backgrounds is an extremely useful application in many HCI applications, such as human body motion capture, hand gesture recognition, and virtual reality (VR) games. However, accurately tracking different objects under uncontrolled environments is a tough challenge due to the possibly dynamic object parts, varied lighting

Image shadow detection and removal can effectively recover image information lost in the image due to the existence of shadows, which helps improve the accuracy of object detection, segmentation and tracking. Thus, aiming at the problem of the scale of the shadow in the image, and the inconsistency of the shadowed area with the original non-shadowed area after the shadow is removed, the multi-scale

In this paper, a novel method for lossless image encryption based on set partitioning in hierarchical trees and cellular automata. The proposed encryption method embeds the encryption into the compression process, in which a small part of the data is encrypted quickly, while maintaining the good coding characteristics of set partitioning in hierarchical trees (SPIHT). The proposed encryption system

Rate Distortion Optimization (RDO) is employed in High-Efficiency Video Coding (HEVC) to enhance its coding efficiency. Owing to its coding complexity, RDO is traditionally performed within each frame with fixed quantization parameters (QPs), without fully considering the coding dependencies between the current frame and future frames within a temporal propagation chain. To improve the coding efficiency

Emotion recognition is a hot research in modern intelligent systems. The technique is pervasively used in autonomous vehicles, remote medical service, and human–computer interaction (HCI). Traditional speech emotion recognition algorithms cannot be effectively generalized since both training and testing data are from the same domain, which have the same data distribution. In practice, however, speech

Fitting a model to data contaminated by noise and outliers is a common task in computer vision, and it is often solved by maximizing inlier set. Most existing methods cannot guarantee global optimality, due to the two techniques widely utilized in inlier maximization: randomized sampling to generate candidate models and a hard threshold on residuals to classify inliers and outliers. In this paper,

In recent years, hyperspectral image super-resolution has attracted the attention of many researchers and has become a hot topic in the field of computer vision. However, it is difficult to obtain high-resolution images due to imaging hardware devices. At present, many existing hyperspectral image super-resolution methods have not achieved good results. In this paper, we propose a hyperspectral image

Considering the image captioning problem, it is difficult to correctly extract the global features of the images. At the same time, most attention methods force each word to correspond to the image region, ignoring the phenomenon that words such as “the” in the description text cannot correspond to the image region. To address these problems, an adaptive attention model with a visual sentinel is proposed

Video-based person re-identification (Re-ID) is of important capability for artificial intelligence and human–computer interaction. The spatial and temporal features play indispensable roles to comprehensively represent the person sequences. In this paper, we propose a comprehensive feature fusion mechanism (CFFM) for video-based Re-ID. We use multiple significance-aware attention to learn attention-based

This paper presents an effective hand-crafted texture operator for texture recognition, referred to as oriented star sampling structure based multi-scale ternary pattern (O3S-MTP) which is expected to better represent salient local texture structure. Instead of heuristic code constructions, the proposed O3S-MTP descriptor is defined as a gray-scale invariant texture measure, derived from a general

In this work, we explore the feasibility and rationality of the glove-type massage information acquisition system combined with the pneumatic human organ simulator in the traditional massage context. We proposed the glove-type massage information acquisition system and the pneumatic human organ simulator to compare and analyze the effectiveness of the compression displacement, stiffness, and pressure

With the rapid development of computer vision and multimedia technology, especially the visual tracking technology and network transmission, teacher-centered education is popular nowadays. The shortcomings of the conventional classroom teaching mode by manually student behavior analysis are gradually becoming less effective. Aiming at the main problems existing in the application of classroom teaching

Human action recognition is the key technique in many modern intelligent systems, such as video surveillance, sport motion analysis, virtual reality, and shopping behavior analysis. Human action recognition is a challenging task due to the sophisticated backgrounds and possible occlusions In this paper, we aim to recognize human actions from the video stream of sport scenes by intelligently fusing

Universal Multimedia Access (UMA) calls for solutions where content is created once and subsequently adapted to given requirements. With regard to UMA and scalability, which is required often due to a wide variety of end clients, the best suited codecs are wavelet based (like the MC-EZBC) due to their inherent high number of scaling options. However, most transport technologies for delivering videos