0000000000505704

AUTHOR

Desire Sidibe

0000-0002-5843-7139

showing 3 related works from this author

Control of a PTZ Camera in a Hybrid Vision System

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications
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Outdoor Scenes Pixel-wise Semantic Segmentation using Polarimetry and Fully Convolutional Network

2019

International audience; In this paper, we propose a novel method for pixel-wise scene segmentation application using polarimetry. To address the difficulty of detecting highly reflective areas such as water and windows, we use the angle and degree of polarization of these areas, obtained by processing images from a polarimetric camera. A deep learning framework, based on encoder-decoder architecture, is used for the segmentation of regions of interest. Different methods of augmentation have been developed to obtain a sufficient amount of data, while preserving the physical properties of the polarimetric images. Moreover, we introduce a new dataset comprising both RGB and polarimetric images…

Ground truthModality (human–computer interaction)reflective areasPixelbusiness.industryComputer scienceDeep learningsegmentationPolarimetryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONdeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineering02 engineering and technologyMarket segmentationaugmentation0202 electrical engineering electronic engineering information engineeringRGB color model020201 artificial intelligence & image processingComputer visionSegmentationArtificial intelligencebusinesspolarimetryProceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
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Deep multimodal fusion for semantic image segmentation: A survey

2021

International audience; Recent advances in deep learning have shown excellent performance in various scene understanding tasks. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. A variety of studies have demonstrated that deep multimodal fusion for semantic image segmentation achieves significant performance improvement. These fusion approaches take the benefits of multiple information sources and generate an optimal joint prediction automatically. This paper describes the essential background concepts of deep multimodal fusion and the relevant applications in compute…

Computer science02 engineering and technologyMachine learningcomputer.software_genre0202 electrical engineering electronic engineering information engineeringImage fusionSegmentationmutimodal fusionImage segmentationImage fusionHeuristicbusiness.industryDeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Deep learning020207 software engineeringImage segmentationSemantic segmentationVariety (cybernetics)Multi-modal[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Signal ProcessingBenchmark (computing)020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencePerformance improvementbusinesscomputerImage and Vision Computing
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