Titlebook: Computer Vision, Graphics, and Image Processing; ICVGIP 2016 Satellit Snehasis Mukherjee,Suvadip Mukherjee,Santanu Chaud Conference proceed

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書目名稱Computer Vision, Graphics, and Image Processing影響因子(影響力)




書目名稱Computer Vision, Graphics, and Image Processing影響因子(影響力)學科排名




書目名稱Computer Vision, Graphics, and Image Processing網(wǎng)絡公開度




書目名稱Computer Vision, Graphics, and Image Processing網(wǎng)絡公開度學科排名




書目名稱Computer Vision, Graphics, and Image Processing被引頻次




書目名稱Computer Vision, Graphics, and Image Processing被引頻次學科排名




書目名稱Computer Vision, Graphics, and Image Processing年度引用




書目名稱Computer Vision, Graphics, and Image Processing年度引用學科排名




書目名稱Computer Vision, Graphics, and Image Processing讀者反饋




書目名稱Computer Vision, Graphics, and Image Processing讀者反饋學科排名

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Vision Based Pose Estimation of Multiple Peg-in-Hole for Robotic Assemblypeg-in-hole assembly with the use of genetic algorithm based two-stage camera calibration procedure. The proposed algorithm has also been tested for its performance in estimating the pose of the multiple pegs in wheel-hub of a car. The result reveals that the proposed method estimates the pose of th

moratorium

Generosity

A Hybrid Deep Architecture for Face Recognition in Real-Life Scenarioategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characte

口訣

A Text Recognition Augmented Deep Learning Approach for Logo Identification complex features which are further input to a multiclass support vector machine (SVM) for classification. We tested our proposed logo recognition system on 32 logo classes, and a non-logo class obtained by combining FlickrLogos-32 and MICC logo databases, amounting to a total of 23582 training and

rheumatism

rheumatism

Self-Help-Group

Ingenuity

https://doi.org/10.1007/978-3-030-89895-3the researcher community. Further, quantitative experimental performance analysis in the form of identification rate at rank 1, was conducted on 22 photometric normalization techniques using four state-of-the-art face recognition algorithms. The performance analysis indicates outstanding results wit

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