A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.

A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions

As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.

A Comprehensive Survey of Transformers for Computer Vision

As a special type of transformer, Vision Transformers (ViTs) are used to various computer vision applications (CV), such as image recognition. There are several potential problems with convolutional neural networks (CNNs) that can be solved with ViTs. For image coding tasks like compression, super-resolution, segmentation, and denoising, different variants of the ViTs are used. The purpose of this survey is to present the first application of ViTs in CV. The survey is the first of its kind on ViTs for CVs to the best of our knowledge. In the first step, we classify different CV applications where ViTs are applicable. CV applications include image classification, object detection, image segmentation, image compression, image super-resolution, image denoising, and anomaly detection. Our next step is to review the state-of-the-art in each category and list the available models. Following that, we present a detailed analysis and comparison of each model and list its pros and cons. After that, we present our insights and lessons learned for each category. Moreover, we discuss several open research challenges and future research directions.

Learning-Driven Lossy Image Compression; A Comprehensive Survey

In the realm of image processing and computer vision (CV), machine learning (ML) architectures are widely applied. Convolutional neural networks (CNNs) solve a wide range of image processing issues and can solve image compression problem. Compression of images is necessary due to bandwidth and memory constraints. Helpful, redundant, and irrelevant information are three different forms of information found in images. This paper aims to survey recent techniques utilizing mostly lossy image compression using ML architectures including different auto-encoders (AEs) such as convolutional auto-encoders (CAEs), variational auto-encoders (VAEs), and AEs with hyper-prior models, recurrent neural networks (RNNs), CNNs, generative adversarial networks (GANs), principal component analysis (PCA) and fuzzy means clustering. We divide all of the algorithms into several groups based on architecture. We cover still image compression in this survey. Various discoveries for the researchers are emphasized and possible future directions for researchers. The open research problems such as out of memory (OOM), striped region distortion (SRD), aliasing, and compatibility of the frameworks with central processing unit (CPU) and graphics processing unit (GPU) simultaneously are explained. The majority of the publications in the compression domain surveyed are from the previous five years and use a variety of approaches.