Titlebook: Computer Vision and Image Processing; 4th International Co Neeta Nain,Santosh Kumar Vipparthi,Balasubramanian Conference proceedings 2020 S

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CNN and RF Based Classification of Brain Tumors in MR Neurological Imagesery difficult to estimate the accurate information regarding the tumor position, shape and size and it requires a lot of years of experience. So, computer-aided diagnostic system is required to assist the radiologists correctly. In this paper, random forest (RF) and convolutional neural network (CNN

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Nonparametric Vibration Based Damage Detection Technique for Structural Health Monitoring Using 1D Cucture as a whole. The continuous real-time diagnosis allows optimal use of the structure, a minimized downtime, and the avoidance of catastrophic failures. A 1-Dimensional Convolutional Neural Network (CNN) approach for SHM is proposed in this paper which can minimize human involvement, and thus im

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1865-0929 mage Processing. held in Jaipur, India, in September 2019. .The 73 full papers and 10 short papers were carefully reviewed and selected from 202 submissions. The papers are organized according to the following topics:​ .Part I: Biometrics;  Computer Forensic; Computer Vision; Dimension Reduction; He

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Microsoft Office, OpenOffice/StarOffice algorithm out performs state-of-the-art methods for image blending both qualitatively as well as quantitatively on publicly available High Dynamic Range (HDR) imaging dataset. Qualitative improvements are demonstrated in terms of details, halos and dark halos. For quantitative comparison, the no-reference perceptual metric MEF-SSIM was used.

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The Definitive Guide to MySQL 5eployed in real-world applications with limited computational power and memory. The new models are also compared with the models recently proposed in literature to establish a state of the art depth prediction model that can be used in real-time.

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Energy Based Convex Set Hyperspectral Endmember Extraction Algorithmnce of proposed algorithms and prevailing algorithms are evaluated with spectral angle error, spectral information divergence, and normalized cross-correlation for the synthetic and real dataset. It is observed from the simulation results that the proposed algorithm is giving worthy performance than other prevailing algorithms.

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