In this paper, an effective method is proposed for restoring underwater image based on color correction and image sharpening. The main purpose of this method is to improve the visibility of underwater images. Traditional methods generally adopt color balancing method to restore the images. However, we found that color balancing has a poor effectiveness on underwater image when the values of blue channel

With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification

Multi-exposure image fusion is an effective method for depicting high dynamic range of the target scene in a single image. However, there are still some problems remaining: the preserving of global contrast, the preserving of the local details in saturated regions, and the existence of halo artifacts. To solve these problems, this paper proposes a new multi-exposure image fusion algorithm with detail

With the world’s interaction moving more and more toward using online social media platforms, the advent of cyberbullying has also raised its head. Multiple forms of cyberbullying exist from the more common text based to images or even videos, and this paper will explore the context of textual comments. Even in the niche area of considering only text-based data, several approaches have already been

View variation is one of the greatest challenges in the field of gait recognition. Subspace learning approaches are designed to solve this issue by projecting cross-view features into a common subspace before recognition. However, similarity measures are data-dependent, which results in low accuracy when cross-view gait samples are randomly arranged. Inspired by the recent developments of data-driven

The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization

Virtual reality 360° video has an ultra-high resolution (usually 4–8 K), which takes more coding time than traditional video. Considering the degree of horizontal stretching in a different area of the ERP projected video, a fast coding unit (CU) partition algorithm based on the sum of region-directional dispersion is proposed. The relationship between the current block and its adjacent ones is measured

In today's information age, information is gathered from text and more complex media, such as images, audio, and video. Among these data sources, the rapid growth of video information has led to it to gradually become the main source of information in people's lives. Video information is characterized by many kinds of information, complex forms, and a low degree of structure. Therefore, effectively

One of the biggest changes in H.266/Versatile Video Coding (VVC) is introduced quad-tree with nested multi-type tree (QTMT) coding tree architecture, where the multi-type tree (MTT) structure in H.266/VVC includes binary tree (BT) and ternary tree (TT). Compared with H.265/High Efficiency Video Coding (HEVC) which only is divided by quad-tree (QT), the QTMT architecture makes the coding unit (CU) partition

Action recognition in video is one of the important applications in computer vision. In recent years, the two-stream architecture has made significant progress in action recognition, but it has not systematically explored spatial–temporal features. Therefore, this paper proposes an integrated approach using Gaussian mixture model (GMM) and dilated convolution residual network (GD-RN) for action recognition

Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen–Loeve

Cricket highlight generation is the process of summarizing a full-length video to a shortened form which should preserve the important moments present in the original video. In this paper, a new approach has been proposed for recognizing the key events and summarization. Audio features are initially used for extracting the excitement clips. Then, the important events like replay, players, umpires,

Color image quantization is a significant procedure of reducing the huge range of color values of a digital color image into a limited range. In this paper, an automated clustering of pixels and color quantization algorithm is proposed. The ideal number of representative colors is unknown beforehand in most color quantization algorithms. This is an important handicap in most practical cases. The proposed

Semantic image segmentation in computer networks is designed to determine the category to which each pixel in an image belongs. It is a basic computer vision task and has a very wide range of applications in practice. In recent years, semantic image segmentation algorithms in computer networks based on deep learning have attracted widespread attention due to their fast speed and high accuracy. However

With the emergence of cyber technology, the biggest evolution has been observed in the use of Internet for financial purposes, in particular for the Internet banking sector. However, with the increase in the number of Internet banking users, many security issues have been discovered. In the recent past, there have been many successful cyber-attacks on the Internet banking services (IBS) throughout

Most state-of-the-art multiple-object tracking (MOT) methods adopt the tracking-by-detection (TBD) paradigm, which is a two-step procedure including the detection module and the tracking module. In these methods, the tracking performance heavily depends on initial detections and data association. In this paper, we present an online MOT method by introducing a single-object tracking (SOT) based on correlation

The deep learning techniques have achieved great success in the application of human activity recognition (HAR). In this paper, we propose a technique for HAR that utilizes the RGB and skeleton information with the help of a convolutional neural network (Convnet) and long short-term memory (LSTM) as a recurrent neural network (RNN). The proposed method has two parts: first, motion representation images

In dark or poorly lit environments, it is often difficult for the naked eye to distinguish low-light-level images because of low brightness, low contrast and noise, and it is difficult to perform subsequent image processing (such as video surveillance and target detection). To solve these problems, this paper proposes a low-light-level image enhancement algorithm based on deep learning. First, the

Research on image retrieval and classification in the food field has become one of the more and more concerned research topics in the field of multimedia analysis and applications. In recent years, with the rapid development of the Internet industry and multimedia technology, image classification and retrieval technology has become a research hotspot at home and abroad. Traditional keyword-based image

A great number of Internet of things (IoT) applications rely on real-time communication (RTC) mechanisms for transmission of media. Essentially, applications analyze and process media to make decisions that typically affect actuation and control of embedded devices. IoT networks, however, are subjected to constrains that limit the computational and resource complexity of all entities involved. This

Automatic detection of cyberbullying in social media content is a natural language understanding and generic text classification task. The cultural diversities, country-specific trending topics hash-tags on social media, the unconventional use of typographical resources such as capitals, punctuation, emojis and easy availability of native language keyboards add to the variety and volume of user-generated

Crowd congestion-level analysis (CCA) is one of the most important tasks of crowd analysis and helps to control crowd disasters. The existing state-of-the-art approaches either utilize spatial features or spatial–temporal texture features to implement the CCA. The state-of-the-art deep-learning approaches utilize a single column convolution neural network (CNN) to extract deep spatial features to solve

Visual Object Tracking is a very challenging task because of the large appearance variance caused by illumination, deformation, and motion. Siamese network-based trackers, which select target through a matching function, are widely used for visual object tracking. The trackers are capable of robustly recognizing the target with appearance variance. However, while the filter template is a crucial part

Deep belief networks (DBNs) are used in many applications such as image processing and pattern recognition. But the data vectorized resulting in the loss of high-dimensional data and valuable spatial information. The classical DBNs model is based on restricted Boltzmann machines (RBMs) and full connectivity between the visible units and the hidden units. It requires a large number of parameters to

This research aims to assist users to seek information efficiently while interacting with speech-based information, particularly in multimedia delivery, and reports on an experiment that tested two speech-based designs for communicating multiple speech-based information streams efficiently. In this experiment, a high-rate playback design and a concurrent playback design are investigated. In the high-rate

Nowadays, highly sensitive medical images are vulnerable to data threats and privacy attacks. They must be kept secure while transmitting them across insecure channels precisely for this purpose. The robust image steganography is focused on this work by exploiting Redundant Integer Wavelet Transform (RIWT), Laplacian Pyramid, Arnold scrambling and Histogram shifting algorithm to facilitate secure communication

In this paper, we propose a novel visual object tracking method using a part based appearance model. First, a local kernel feature is developed to encode edge information of patches. Next, bounding box of the target is divided into multiple parts. Then, each part uses correlation filter based tracking to predict position in the next frame. The matrix cosine similarity is utilized to measure reliabilities

Human activity recognition (HAR) is an important branch of human-centered research. Advances in wearable and unobtrusive technologies offer many opportunities for HAR. While much progress has been made in HAR using wearable technology, it still remains a challenging task using unobtrusive (non-wearable) sensors. This paper investigates detection and tracking of multi-occupant HAR in a smart-home environment

Denigration is a specialized form of cyberbullying which describes a recurrent, sustained and intentional attempt to damage the victim’s reputation or ruin the friendships that he or she has by spreading unfounded gossip or rumors online. It is the most common bullying tactic involving character assassination of public figures like celebrities and politicians. As a comprehensive approach to match to

Recognition of micro-expressions depends on the key features provided in the form of the temporal information. It needs considerable effort, however, to manually design useful characteristics. Subtle or micro-facial expressions are much difficult than regular facial expressions rich in emotional expressions in a true environment to be identified. An easy solution is discussed in this paper to recognise

Nowadays, with the development of social media networks, micro-videos, an emerging form of user-generated contents (UGCs), are gradually attracting greater interest. Some of them are widely spread, while others draw little attention. The popular micro-videos have significant commercial potential in many ways, such as online advertising and bandwidth allocation. In recent years, the popularity prediction

A stereo visual odometry algorithm based on the fusion of optical flow tracking and feature matching called LK-ORB-SLAM2 was proposed. In LK-ORB-SLAM2, the operation of optical flow tracking is introduced to adjust the intensive and time-consuming operation of feature matching. This requires solving a key issue: how to solve the problem of losing feature points during optical flow tracking. For this

Plant recognition is a meaningful research that has attracted many researchers. Due to the variety of plants, it is difficult for the existing identification methods to identify their species efficiently. We proposes a plant recognition method based on Jaccard distance and Bag of words (BOW). Firstly, Jaccard distance is employed to calculate the similarity between the test sample and part of the training

Thermal sensors bring a lot of benefits for object tracking due to the robustness to lighting changes over time, which still give a great difficulty to traditional tracking methods. Despite this powerful advantage, the ambiguity due to background clutters and occlusions makes the problem of thermal sensor-based tracking intractable. In this paper, we propose a novel framework for multiple object tracking

The Flexible Dual TCP-UDP Streaming Protocol (FDSP) combines the reliability of TCP with the low latency of UDP, thus providing transport layer improvements towards maintaining high QoE of multi-bitrate videos in adaptive streaming. FDSP delivers the more critical parts of the video data via TCP and the rest via UDP. FDSP also uses Bitstream Prioritization (BP), a sliding scale that determines the

The text to speech technology has achieved significant progress during the past decade and is an active area of research and development in providing different human–computer interactive systems. Even though a number of speech synthesis models are available for different languages focusing on the domain requirements with many motive applications, a source of information on current trends in Indian

Visual media has become one of the most potent means of conveying opinions or sentiments on the web. Millions of photos are being uploaded by the people on famous social networking sites for expressing themselves. The area of visual sentiment analysis is abstract in nature due to the high level of biasing in the human recognition process. This work proposes a residual attention-based deep learning

Skewness and obliqueness of vehicle plate images influence license plate recognition. The more tilted plate images are, the harder the recognition task is. To this end, if plate images are preprocessed to be aligned and rectified, the recognition performance would improve. We propose deep neural network models that can locate four corner plate positions, which can then be used to perform the perspective

We propose a novel method for modeling crowd video dynamics by adopting a two-stream convolutional architecture which incorporates spatial and temporal networks. Our proposed method cope with the key challenge of capturing the complementary information on appearance from still frames and motion between frames. In our proposed method, a motion flow field is obtained from the video through dense optical

Image stitching, or known as image mosaic, is the process that combines images with overlapped areas to form an image with wide view and high resolution. Image stitching technique has been quickly developed these years. It has become an important branch in digital image processing and has wide applications. Many image stitching methods have been proposed. This article takes investigation on some image

With the advancement of time, the computer vision systeams are focusing on mimicking the human visual system. In this manuscript, we tried to develop a model which works at improving both the detection accuracy and computation time. First, two double opponent color based features and twelve directional edge features using Gabor filter are computed. Then the most dominant feature pertaining to the salient

Schizophrenia, a serious psychological disorder, causes auditory and visual hallucinations, and delusions in a person. Several studies have shown that schizophrenia imposes structural changes in human brain. Relative changes in the grey matter volume of the schizophrenia patients in comparison to healthy controls have been well explored. However, identification of relevant brain regions that exhibit

In order to realize the secure transmission of the H.264 compressed video bitstream, a detailed analysis of the structure for video bitstream is performed. The parameter set and frame header data related to video decoding are retained, and the slice data payload in the network abstraction layer unit is selected as the encryption object. The video format is compliant under the premise of real time and

With the massive use of images over the Internet, the security of some of them has become critical. One of the most used encryption algorithms is AES. Although this algorithm provides high security for text data, it is not suitable for images in view of their characteristics including correlation and redundancy. To have a more suitable and efficient algorithm for the encryption of images, we have deeply

RGB-D sensors have been in great demand due to its capability of producing large amount of multimodal data like RGB images and depth maps, useful for better training of deep learning models. In this paper, a deep learning model for recognizing human activities in a video sequence by combining multiple CNN streams has been proposed. The proposed work comprises the use of dynamic images generated from

Owing to the rapid development and advancements in the field of networks and communication, sharing of multimedia contents over insecure networks has become vital. The confidentiality of audio signals is predominantly needed in military and intelligence bureau applications. The proposed algorithm addresses this issue by encrypting audio signal using chaotic maps in spatial and transform domain. Discrete

In this paper, we propose a proximity-based indoor positioning system which is capable of monitoring mobile device user’s indoor locations where the commonly used GPS signal is unavailable or weak. The designed system is aimed to be integrated into a context-aware communications system to prevent transmission of irrelevant content to all users but easing delivery of the location-based information.

Keyframe extraction plays a significant role in wide variety of real-time video processing applications such as video summarization, video management and retrieval, etc. A keyframe captures the whole content of its shot and does not contain any redundant information. The keyframe extraction algorithms are facing challenges due to different visual characteristics in videos of different categories. Therefore

Occlusion is an important factor for analysis of human gait recognition in real-time scenarios. In multi-person gait (MPG) or dynamic occlusion, gait recognition is affected due to occluded body parts known as region of interests (ROIs). The aim of this article is to reconstruct the occluded ROIs and measure the errors associated with the reconstruction methods. The contribution of this article is

In data encryption, recently commonly used schemes are chaos-based due to their properties of sensitivity to initial conditions, control parameters, and pseudo-randomness. The chaotic crypto-algorithms are appropriate for extensive data encryption for images, audio or videos. In this paper, a novel audio encryption algorithm based on substitution and permutation network is proposed. In this study,

Along with the growth of the internet, the number of information published increased exponentially. This huge flow of information causes a problem called “information overload” which makes it harder for internet users to find key information they needed on the internet. To solve this, this paper proposes an application that helps user find trending news of their query/interest easily. Some challenges

As the VoIP steganographic methods provide a low capacity covert channel for data transmission, an efficient and real-time data transmission protocol over this channel is required which provides reliability with minimum bandwidth usage. This paper proposes a micro-protocol for data embedding over covert storage channels or covert hybrid channels developed by steganographic methods where real-time transport

It is an extremely interesting work to understand the minority costumes in computer vision and ethnology community. It explored some crucial clue for understanding minority costumes via computer vision technology. As it known to all, complicated and subtle structure between different minority costumes lead it becomes hard work to recognize them with computer vision even people. An intelligent framework

Human activity recognition has been a significant goal of computer vision since its inception and has developed considerably in the last years. Recent approaches to this problem increasingly favour the use of data-driven deep learning methods. To facilitate the comparison of these methods, several datasets pertaining to labelled human activity have been created, having great variation in content and

Package RMoCap is an advanced open-source tool for scientists, engineers and computer graphics familiar with R language who work with motion capture (MoCap) technology. Package provides them with MoCap data handling, statistical processing, visualizing and analysis. Package uses well-established MoCap file exchange format and can be easily integrated with most of the motion analysis workflows. Among

The preferences of Web information purchasers are changing. Cost-effectiveness (i.e., an emphasis on performance with respect to price) is becoming less regarded than cost-satisfaction, which emphasizes the purchaser’s psychological satisfaction. A method to improve a user’s cost-satisfaction in recommending multimedia content is to use the mood-inherent in multimedia items. An example of applications

With the recent advent of three-dimensional (3D) sound home theater systems (HTS), more and more TV viewers are experiencing rich, immersive auditory presence at home. In this paper, visual processing approaches are provided to make 3D audio–visual (AV) systems more realistic to the viewers. In the proposed system, a visual engine processes stereo video streams to extract a disparity map for each pair

Aiming at the problem of 3D point cloud noise affecting the efficiency and precision of human body 3D reconstruction in complex scenes, a 3D point cloud registration denoising method for human motion image using depth learning algorithm is proposed. First, two Kinect sensors are used to collect the three-dimensional data of the human body in the scene, and the spatial alignment under the Bursa linear

Representing image sets and videos with Grassmann manifold has become popular due to its powerful capability to extract discriminative information in machine learning research. However, existing techniques operations on Grassmann manifold are usually suffering from the problem of computational expensive, thus the application range of Grassmann manifold is limited. In this paper, we propose the Fréchet