A tremendous amount of video data is transferred over the Internet from one location to another, and its amount is growing exponentially every day. Significant advances made in multipurpose video editing software technology have considerably increased the chances of digital video tampering/forgeries. Therefore, the authenticity of digital video has become important. In this paper, the passive algorithm

With the increase of vehicles and the diversification of road conditions, people pay more attention to the safety of driving. In recent years, autonomous driving technology by Franke et al. (IEEE Intell Syst Their Appl 13(6):40–48, 1998) and unmanned driving technology by Zhang et al. (CAAI Trans Intell Technol 1(1):4–13, 2016) have entered our field of vision. Both automatic driving by Levinson et

Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding

Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pronunciation mistakes occur due to unfamiliarity with a new language and differences in accent. In this paper, we perform speech mistakes analysis using deep feature-based clustering. We

In the pinyin-based Chinese input method engine (IME), its performance depends mainly on the Pinyin-to-Chinese (P2C) conversion module. Traditional methods for P2C follow a pipeline procedure, which typically suffers from error propagation. Also, the ability to input the whole sentence of pinyin-based Chinese IME for domain-specific application needs to be improved. In this paper, we propose a neural

The biggest difference between video-based action recognition and image-based action recognition is that the former has an extra feature of time dimension. Most methods of action recognition based on deep learning adopt: (1) using 3D convolution to modeling the temporal features; (2) introducing an auxiliary temporal feature, such as optical flow. However, the 3D convolution network usually consumes

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought

Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have

Few-shot imbalanced classification tasks are commonly faced in the real-world applications due to the unbalanced data distribution and few samples of rare classes. As known, the traditional machine learning algorithms perform poorly on the imbalanced classification, usually ignoring the few samples in the minority class to achieve a good overall accuracy. To solve this few-shot problem, a novel data

Image forgeries created using easy-to-use image editing tools like Adobe Photoshop have become prime sources of fake news, and are often used in malevolent ways in politics, courtrooms, and in scientific publishing as well. To reduce the problems caused by manipulated images in these domains, it is important to have reliable and fast mechanisms in place for verifying their authenticity before their

Recently, computer-aided diagnosis (CAD) systems powered by deep learning (DL) algorithms have shown excellent performance in the evaluation of digital mammography for breast cancer diagnosis. However, such systems typically require pixel-level annotations by expert radiologists which is prohibitively time-consuming and expensive. Medical institutes would wonder if a high-performance breast cancer

A correction to this paper has been published: https://doi.org/10.1007/s00530-021-00819-0

Natural images often have intrinsic low-rank structures and are susceptible to interference from outliers or perturbation noise, especially mixed noise. Low-rank matrix recovery via outlier estimation (ROUTE) has been proposed to determine the location of gross corruption by estimating the outliers; however, this approach ignores local structural smoothness. In this paper, we incorporate TV norm regularization

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey

In recent times, active research is going on for bridging the gap between computer vision and natural language. In this paper, we attempt to address the problem of Hindi video captioning. In a linguistically diverse country like India, it is important to provide a means which can help in understanding the visual entities in native languages. In this work, we employ a hybrid attention mechanism by extending

In the original article, the author list is incorrect.

This paper presents an approximate multiplication-free of discrete cosine transform (DCT) for still image compression. The introduction of null elements into a specified integer DCT leads to a new low complexity, faster and more efficient transform. Furthermore, an efficient fast algorithm primarily involving a small amount of arithmetical computation is well developed as no multiplications are required

Hand gesture recognition system helps in development of interface system for entering text in human computer interaction. In this paper, we have presented a hand gesture recognition system designed for dataset consisting of numerals and alphabets in lower case. The proposed system detects the hand with the help of skin color and motion information. Hand tracking is done with the help of two-level tracking

Human motion prediction aims to automatically predict the future motion sequence based on an observed human motion sequence. In this paper, we propose a novel skip-attention encoder–decoder (SAED) framework to model human motion dependences in spatiotemporal space, by utilizing the encoder and decoder to encode the observed motions, and decode the predicted motions, respectively. Overall, this framework

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling

To improve the imperceptibility without decreasing the steganographic capacity when steganography by a modification to an audio is carried out, this paper proposes an optimal matching path algorithm to reduce the modification to achieve it. A steganography operation unit is defined, which is composed of every N consecutive audio frames and each frame includes M LPC parameters. All these parameters

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification

Video question answering (VideoQA) is challenging as it requires models to capture motion and spatial semantics and to associate them with linguistic contexts. Recent methods usually treat space and time symmetrically. Since the spatial structures and temporal events often change at different speeds in the video, these methods will be difficult to distinguish spatial details and different scale motion

Since human pose can be naturally represented by a graph, graph convolutional networks (GCNs) have recently been proposed for 3D human pose estimation and achieved promising results. But most GCN-based methods use vanilla graph convolution which aggregates features of 1-hop neighbors and long-range dependencies between joints can only be captured by stacking multiple layers of graph convolution. To

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based

Few animals can hide their signatures into their surroundings to hunt or avoid being hunted, termed as natural camouflage. Natural camouflage inspires researchers to design various algorithms to artificially create camouflage images and widely apply them in the military field. It is also essential to evaluate the effectiveness of artificially created camouflage images. Several algorithms have been

Predicting human visual attention cannot only increase our understanding of the underlying biological mechanisms, but also bring new insights for other computer vision-related tasks such as autonomous driving and human–computer interaction. Current deep learning-based methods often place emphasis on high-level semantic feature for prediction. However, high-level semantic feature lacks fine-scale spatial

Code generation from graphical user interface images is a promising area of research. Recent progress on machine learning methods made it possible to transform user interface into the code using several methods. The encoder–decoder framework represents one of the possible ways to tackle code generation tasks. Our model implements the encoder–decoder framework with an attention mechanism that helps

Pupil detection in a human eyeimage or video plays a key role in many applications such as eye-tracking, diabetic retinopathy screening, smart homes, iris recognition, etc. Literature reveals pupil detection faces many complications including light reflections, cataract disease, pupil constriction/dilation moments, contact lenses, eyebrows, eyelashes, hair strips, and closed eye. To cope with these

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according

To enhance the security of single-chaotic systems, we propose a novel image encryption cryptosystem based on true random numbers and chaotic systems. First, we select any one of several chaotic systems. Next, the hash function is used to calculate the initial value of the chaotic system using a plaintext image. Then, we obtain a solution of this chaotic system and use the k-medoids clustering machine-learning

Image forgery detection focuses more on tampering regions than image content of semantic segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient semantic information causes low efficiency of forgery detection. To address these issues, we propose a hybrid features and semantic reinforcement network (HFSRNet) for image forgery detection, which is an encoding and

Machine learning and deep learning play a crucial role in identification of various diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can be performed promising for prediction of Alzheimer’s disease using MRI scans. Alzheimer disease becoming more common in the people (age 65 years or above). The disease becomes severe before the symptoms appear and causes brain

Key frame extraction is an important manner of video summarization. It can be used to interpret video content quickly. Existing approaches first partition the entire video into video clips by shot boundary detection, and then, extract key frames by frame clustering. However, in most team-sport videos, a video clip usually includes many events, and it is difficult to extract the key frames related to

Implementing pedestrian detection real-time embedded systems remains a major challenge. Detecting pedestrians in an advanced driver assistance system requires a lot of time and resources. The method based on Multi-Scale Region Covariance Descriptor (MSRCD) and Support Vector Machine (SVM) is one of the most effective approaches to perform pedestrian detection. However, such implementation is difficult

The performance of multi-person pose estimation has significantly improved with the development of deep convolutional neural networks. However, two challenging issues are still ignored but are key factors causing deterioration in the keypoint localization. These two issues are scale variation of human body parts and huge information loss caused by consecutive striding in multiple upsampling. In this

Visible light polarization image desmogging aims to recover the clear image solely from an input visible light polarization image with smog. It is a challenging research topic due to desmogging is an ill-posed problem. Most of existing methods effort to build desmogging physical model based on classical atmospheric scattering principle. However, compared with natural image dehazing task, the smog in

Malignant melanoma is one of the most dreadful skin cancer types caused by the abnormal growth of melanocyte cells. Deep convolutional neural network (CNN) models are becoming prominent for the automated diagnosis of melanoma from dermoscopic images. Although being incredibly accurate, the “black-box” nature of deep CNN models due to the lack of proper interpretability still prevents their wide-spread

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep

Weight normalization (WN) can help to stabilize the distribution of activations over layers, which boost the performance of DNNs in generalization. In this paper, we further propose deep structural weight normalization (DSWN) methods to inject the network structure measurements into the WN to fully acknowledge the data propagation through the neural network. In DSWN, two novel structural measurements

Scientists and researchers have tried to establish a bond between the emotions conveyed and the subsequent mood perceived in a person. Emotions play a major role in terms of our choices, preferences, and decision-making. Emotions appear whenever a person perceives a change in their surroundings or within their body. Since early times, a considerable amount of effort has been made in the field of emotion

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand

In this paper, an enhanced version of Embedded zerotree wavelet (EZW) image coding algorithm is proposed, referred to as EZW-SC. By exploiting a new principle that relies on a subband classification concept, the enhanced algorithm allows the prediction of insignificant subbands at early passes, along with the use of an improved significance map. This reduces the redundancy of zerotree symbols, speeds

Due to the superior performance and fast running speed, deep learning methods have been widely employed in image processing fields. However, most deep learning-based denoising methods require a specific noise level to train their models, and denoising models are built to remove specific levels of noise, which lacks the flexibility to deal with spatially variant noise. In this paper, we present a discrete

Various performance measures based on the ground truth and without ground truth exist to evaluate the quality of a developed tracking algorithm. The existing popular measures—average center location error (acle) and average tracking accuracy (ata) based on ground truth—may sometimes create confusion to quantify the quality of a developed algorithm for tracking an object under some complex environments

Steganalysis is a technique for detecting existence of secret data hidden in digital media. Researchers are often concentrated in minimizing the misclassification rate. Many steganalysis methods try to choose high-dimension features to reveal subtle changes due to data embedding. By exploring properties of the filter bases, we steer the filters to capture tiny embedding traces in different scales and

The advent of internet-based video streaming service has changed the paradigm of streaming content delivery. However, there has been a call for traditional satellite and the online platform to coexist to improve the access to content delivered to subscribers. In addition, revenue earning is also challenging for service providers especially those unknown to subscribers. Therefore, it is important to

Aggressive comments containing offensive images and inappropriate gesture signs together with textual comments have grown exponentially in the recent past on social media. These aggressive contents on social media are affecting the victims negatively causing fear, stress, sleeping problems and even suicide in some cases. Since social media contents are unmoderated, a technical solution with the characteristic

The live streaming of large-scale events over the Internet attracts a highly diverse audience. Despite the progress in streaming technologies, which employ adaptive bitrate to dynamically adjust the streaming of individual clients to reflect their resource availability, content providers still struggle to efficiently match provisioned capacity and demand in such events. In this paper, we present an

In this paper, a novel two-stage R-CNN network called ParallelNet is proposed for thigh fracture detection task. In the proposed method, multiple parallel backbone networks and a feature fusion connection structure are designed, which can extract features with different reception fields. Specifically, the first backbone network is denoted as main network, which adopted normal convolution to detect

Both JPEG restoration and anti-forensics remove the artifacts left by JPEG compression, and recover the JPEG compressed image. However, how to restore the high-frequency details of a JPEG compressed image for JPEG restoration and how to deceive the existing JPEG compression detectors without sacrificing visual quality in JPEG anti-forensics remain challenging. To address these issues, a framework of

Pre-processing of medical images plays a vibrant part in the field of medicine to detect patient’s disease at an earlier stage. Hospitals and medical centers generate an enormous volume of digital medical images day by day, which is used for several purposes of diagnostic procedures. Because of many images,for secured transmission, image compression is required to reduce the redundancies in the image

A correction to this paper has been published: https://doi.org/10.1007/s00530-021-00791-9

With the increase in the culture of social media and netizen, every day, millions of comments are posted on the uploaded posts. The use of abusive language in user comments has been increased rapidly. Abusive language in online comments initiates cyber-bullying that targets individuals (celebrity, politician, and product) and a group of people (specific country, age, and religion). It is important

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January

Artificial intelligence has the potential to revolutionize disease diagnosis, classification, and identification. However, the implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. This study presents a diagnostic tool based on a deep-learning framework for four-class classification of ocular diseases by automatically detecting

In the technological era, the primary source of information is in the form of digital data, which has to be secured while storing or transmitting during communication over an unsecured network. Different approaches are used to provide security to digital data, viz. text, audio, image, and video. This paper initially explains the security system such as cryptography, watermarking, and steganography

Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions. Hence, an automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of heart. However, due to the small size of left atrium with respect to the whole MRI volume, accurate segmentation of left atrium is

Recent studies show that the performance of deep convolutional neural network (CNN) applied to steganalysis is better than that of traditional methods. However, the existing network structure is still caused by artificial design, which may not be the optimal training network. This paper describes a deep residual network based on a neural architecture search (NAS) algorithm, to minimize the artificial

Point cloud analysis becomes a fundamental but challenging problem in the field of 3D scene understanding. To deal with unstructured and unordered point clouds in the embedded 3D space, we propose a novel direction-induced convolution (DIConv) to obtain the hierarchical representations of point clouds and then boost the performance of point cloud analysis. Specifically, we first construct a direction