IEEE Access Special Section Editorial: Biologically Inspired Image Processing Challenges and Future Directions

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain

Guest editorial: special issue on signal processing

It affords us great pleasure to introduce you to a collection of the best 10 invited papers focusing on different areas of signal processing that were originally mainly submitted by young scientists and Ph.D. students and, on a limited scale, presented at the 2011 34th International Conference on Telecommunications and Signal Processing (TSP) held on 18-20 August, 2011, in Budapest, Hungary. Here you can find their extended versions, in which the authors present their research results in more depth and detail. [...

Guest editorial: high dynamic range imaging

High Dynamic Range (HDR) imagery is a step-change in imaging technology that is not limited to the 8-bits per pixel for each color channel that traditional or low-dynamic range digital images have been constrained to. These restrictions have meant that the current and relatively novel imaging technologies including stereoscopic, HD and ultraHD imaging do not provide an accurate representation of the lighting available in a real world environment. HDR technology has enabled the capture, storage, handling and display of content that supports real world luminance and facilitated the use of rendering methods in special effects, video games and advertising via novel rendering methods such as image-based lighting; it is also compatible with the other imaging methods and will certainly be a requirement of future high-fidelity imaging format specifications. However, HDR still has challenges to overcome before it can become a fully fledged commercially successful technology. This special issue goes someway in to rectify any limitations and also shines a light on future potential uses and directions of HDR

A Hierarchical Distributed Processing Framework for Big Image Data

Abstract—This paper introduces an effective processing framework nominated ICP (Image Cloud Processing) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-Image and Big-Image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging ImageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-the-art methods, both in time efficiency and quality of results

Second-order neural core for bioinspired focal-plane dynamic image processing in CMOS

Based on studies of the mammalian retina, a bioinspired model for mixed-signal array processing has been implemented on silicon. This model mimics the way in which images are processed at the front-end of natural visual pathways, by means of programmable complex spatio-temporal dynamic. When embedded into a focal-plane processing chip, such a model allows for online parallel filtering of the captured image; the outcome of such processing can be used to develop control feedback actions to adapt the response of photoreceptors to local image features. Beyond simple resistive grid filtering, it is possible to program other spatio-temporal processing operators into the model core, such as nonlinear and anisotropic diffusion, among others. This paper presents analog and mixed-signal very large-scale integration building blocks to implement this model, and illustrates their operation through experimental results taken from a prototype chip fabricated in a 0.5-μm CMOS technology.European Union IST 2001 38097Ministerio de Ciencia y Tecnología TIC 2003 09817 C02 01Office of Naval Research (USA) N00014021088

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture