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Summary: Network‐on‐chip (NoC) is adopted as a flexible and effective communication backbone by multiprocessor systems with core counts ranging from a few to hundreds. The performance of NoC based systems greatly rely on the communication between cores when two dependent tasks of an application are mapped to these cores. Hence, application mapping becomes a critical issue in NoC‐based systems as it affects the overall performance of the system. Application mapping aims to reduce the communication among the cores by carefully mapping the highly communicating tasks. Most of the existing static mapping approaches consider only single‐task platforms. In this article, we propose an efficient multi‐task static mapping algorithm called SpecMap$$ SpecMap $$, for regular 2D NoC systems. SpecMap$$ SpecMap $$ consists of three stages namely, (i) graph partitioning, (ii) topology clustering, and (iii) mapping. In the graph partitioning stage, SpecMap$$ SpecMap $$ accurately identifies and groups the highly communicating tasks based on spectral graph partitioning. Then, the clustering and mapping stages allocate the most communicating tasks in the same or adjacent multi‐tasking cores. It also eliminates the interference between multiple applications by mapping them into different clusters of the NoC network. Simulations of SpecMap$$ SpecMap $$ have been conducted on different synthetic and real‐life applications using Gem5 simulator. The simulation results show considerable improvements for SpecMap in terms of communication cost, average network latency, energy consumption, throughput, and execution time as compared to the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Visual effects such as shadows and highlights appear in photographs due to variations in lighting conditions. Although these effects add more meaning to the images, they pose various problems to specific computer vision algorithms. Hence, the removal of shadows and highlights is often considered as a prerequisite to such algorithms. This paper presents an interactive technique for shadow elimination from images. Our method requires user input in the form of rough strokes on the shadow region and its corresponding non-shadow region in the image. We further use quaternion rotation in the YCbCr color space to derive an image that is invariant to shadows. The actual colors of the image are finally recovered by color transfer from the original shadow image. The proposed method takes less time to generate the shadow-free image and does not necessitate the detection of shadows prior to its removal. Also, unlike the existing shadow-removal techniques, our method generates invariant image with minor texture loss. Experimental findings are reported to demonstrate the performance of our shadow-removal technique. [ABSTRACT FROM AUTHOR]

Shadows are inescapable elements in a scene formed due to the presence of an object between the light source and the surface on which it is cast. Appearance of shadows often causes severe issues in computer vision applications like object extraction, and surveillance. Researchers have made effort to device techniques to locate and remove shadows from images and videos. This paper attempts to survey the various shadow detection algorithms for a single image. For the purpose of survey, the notable research work in the literature is classified under five major categories: invariant-based detection, feature-based detection, region-based detection, color model based detection and interactive shadow detection. The survey also includes a qualitative and quantitative evaluation of the methods discussed. As outcomes of the survey, it is observed that, (i) although the works discussed under each of these categories are capable of detecting shadows in different scenarios, the accuracy and time taken need further improvements to make the shadow detection process acceptable for practical applications; (ii) detection of self-shadows and heavily scattered shadows is challenging; (iii) the risk of misclassifying dark objects as shadows should be addressed; (iv) soft shadows that span multiple surfaces pose challenge in accurate detection of shadows. [ABSTRACT FROM AUTHOR]