Titlebook: Biologically Rationalized Computing Techniques For Image Processing Applications; Jude Hemanth,Valentina Emilia Balas Book 2018 Springer

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书目名称Biologically Rationalized Computing Techniques For Image Processing Applications影响因子(影响力)学科排名




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书目名称Biologically Rationalized Computing Techniques For Image Processing Applications被引频次




书目名称Biologically Rationalized Computing Techniques For Image Processing Applications被引频次学科排名




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书目名称Biologically Rationalized Computing Techniques For Image Processing Applications年度引用学科排名




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书目名称Biologically Rationalized Computing Techniques For Image Processing Applications读者反馈学科排名

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Arroyo

Book 2018enefits of high accuracy and less complexity, which explains their low usage in connection with practical imaging applications, such as the medical scenario. Lastly, the book illustrates the methodologies in detail, making it suitable for newcomers to the field and advanced researchers alike..

渗入

Jingting Wang,Tianxing Wu,Jiatao Zhangthe proposed visual saliency-based image compression method is producing reliable results, in terms of peak signal-to-noise ratio (PSNR), compression ratio, and structural similarity (SSIM), compared to the state-of-the-art methods.

habitat

https://doi.org/10.1007/978-981-16-1964-9oencoder, and stacked sparse autoencoder are used. The performance of these techniques is analyzed and compared with the existing methods. From the analysis, it is observed that the stacked sparse autoencoder performs better compared to other methods.

Fabric

Shini Chen,Jianfeng Wen,Richong Zhangptimization, bee colony optimization, cuckoo search, fish school search, firefly algorithm have been reviewed. The application of these algorithms to image processing has been outlined, and few case studies have been presented.

DOLT

https://doi.org/10.1007/978-3-031-01918-0olves combining two (or more) techniques wisely such that the resultant algorithm contains the good characteristics of both (or all) the methods. Interesting hybridization techniques include many local and global search approaches. Results for the SR reconstruction of still and video images are presented for the PSO and the HPSO algorithms.

脆弱带来

Goblet-Cells

Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis,oencoder, and stacked sparse autoencoder are used. The performance of these techniques is analyzed and compared with the existing methods. From the analysis, it is observed that the stacked sparse autoencoder performs better compared to other methods.

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