In this paper, a single image multi-scale super-resolution technique is proposed. the concept under study is the learning procedure between steps of amplification in order to predict the next high scale of resolution....
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Multi-resolution imageprocessing are part of this concept that has a purpose to extracting the detail information of the multi-scale input image. However, in general, to process a multi-scale imagethere are issue th...
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Recently, 3D time-of-flight cameras have been developed. the development enables utilization of depthimages in various fields. However, acquired depthimages are corrupted by noise during the image acquisition proces...
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ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy imageprocessing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. these SoCs (Sy...
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ISBN:
(纸本)9781479989379
ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy imageprocessing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. these SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of imageprocessingalgorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.
Stereo matching methods estimate the depth information from stereoscopic images using the characteristics of binocular disparity. We try to find corresponding points from the left and right viewpoint images to obtain ...
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As the development of interactive robots and machines, studies to understand and reproduce facial emotions by computers have become important research areas. For achieving this goal, several deep learning-based facial...
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the proceedings contain 269 papers. the topics discussed include: high-speed multiview 3D structured light imaging technique;integration of advanced stereo obstacle detection with perspectively correct surround views;...
the proceedings contain 269 papers. the topics discussed include: high-speed multiview 3D structured light imaging technique;integration of advanced stereo obstacle detection with perspectively correct surround views;recreating Van Gogh's original colors on museum displays;modeling long range features from serial section imagery of continuous fiber reinforced composites;the quality of stereo disparity in the polar regions of a stereo panorama;do different radiologists perceive medical images the same way? some insights from representational similarity analysis;the intersection of artificial intelligence and augmented reality;self-calibrated surface acquisition for integrated positioning verification in medical applications;gradient management and algebraic reconstruction for single image super resolution;segmentation-based detection of local defects on printed pages;automated optical inspection for abnormal-shaped packages;towards combining domain knowledge and deep learning for computational imaging;a 360-degrees holographic true 3D display unit using a Fresnel phase plate;and visual analytic process to familiarize the average person with ways to apply machine learning.
Semi-supervised learning uses underlying relationships in data with a scarcity of ground-truth labels. In this paper, we introduce an uncertainty quantification (UQ) method for graph-based semi-supervised multi-class ...
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image gradient, as a preprocessing step is an essential tool in imageprocessing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient...
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Clustering is the unsupervised classification of data items into homogeneous groups called clusters. Clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of dat...
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ISBN:
(纸本)0769521282
Clustering is the unsupervised classification of data items into homogeneous groups called clusters. Clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of data and this is the case in many pattern recognition, image analysis applications. A possible approach to reduce the processing time is based on the implementation of clustering algorithms on scalable parallel computers. this paper describes the design and implementation of P-AFLC, a parallel version of the Adaptive Fuzzy Leader Clustering system based upon the competitive learning model for determining optimal classes in large data sets. the system architecture, its implementation, and experimental performance results are reported, together withtheoretical performance evaluation.
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