Titlebook: Computer Vision and Image Processing; 5th International Co Satish Kumar Singh,Partha Roy,P. Nagabhushan Conference proceedings 2021 The Edi

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中国纪念碑

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The Definitive Guide to MongoDB; a) skeleton videos and angles of skeleton bones as features, b) The HOG features from the RGB frames. In both approaches, we train SVMs and recognize the KPs using them. The classifier generated by SVM predicts the sequence of KPs involved in a given .. Since KPs are the string-like encoding symbo

Accomplish

The Definitive Guide to MongoDBed to detect presence of animals, and the ResNet50 model, trained using Triplet Loss, is used for animal re-identification. The prototype is tested using three animal species, and achieves detection accuracy of 80%, 89.47% and 92.56%, and re-identification accuracy of 99.6%, 86.2% and 61.7% respecti

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Deep over and Under Exposed Region Detection, architecture and re-trained it on our custom dataset. To the best of our knowledge, this is the first attempt to use semantic segmentation and transfer learning methods to identify these regions in an end-to-end fashion. We obtain a Dice score and a Jaccard score of 0.93 and 0.86, respectively, whi

incontinence

,DeepHDR-GIF: Capturing Motion in High Dynamic Range Scenes,es and produced three in-between frames in a binary-search manner. At last, generated HDR frames and interpolated frames are merged in to a GIF image, which depicts the motion in the scene without losing out on the dynamic range of the scene. The proposed framework works on different types of dynami

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Hard-Mining Loss Based Convolutional Neural Network for Face Recognition,d concept is generic and can be used with any existing loss function. We test the Hard-Mining loss with different losses such as Cross-Entropy, Angular-Softmax and ArcFace. The proposed Hard-Mining loss is tested over widely used Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. The