Titlebook: Computer Vision and Image Processing; 8th International Co Harkeerat Kaur,Vinit Jakhetiya,Sanjeev Kumar Conference proceedings 2024 The Edi

绊住

Abdullah Almethen,Othon Michail,Igor Potapovd by subtle variations among different grades and the presence of numerous important small features, poses a considerable challenge for accurate recognition. Currently, the process of identifying DR relies heavily on the expertise of physicians, making it a time-consuming and labor-intensive task. H

Alienated

龙卷风

Iman Bagheri,Lata Narayanan,Jaroslav Opatrnyfaces challenges due to altitude, motion blur, and limited resolution. Early detection and identification of these diseases can help prevent their spread and minimize the impact on crop yields. To address the challenge of capturing high-quality images, this paper relies on the new degradation model

Eclampsia

https://doi.org/10.1007/978-3-030-34405-4ortant role as it can affect the efficiency of the learning algorithm and also reduce the redundancy. In this article, a different look on the problem has been explored where application of biogeography based optimization is used for efficient dimensionality reduction in hyperspectral images. After

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https://doi.org/10.1007/978-3-540-74991-2n July 2020 were mainly due to the heavy monsoon rainfall in the region. The Brahmaputra River and its tributaries, which pass through the state, received substantial rainfall, leading to a sudden increase in water levels and flooding in multiple districts. The state‘s topography, hills, and valleys

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https://doi.org/10.1007/978-3-030-96166-4 unsupervised grid graph generation algorithm specifically designed for change detection using Synthetic Aperture Radar (SAR) images. The proposed technique encompasses a multi-step process: starting with an improved log-ratio based difference image generation, followed by shortest path vector compu

interior

HAWK

Orchiectomy

尾随

https://doi.org/10.1007/978-94-017-0703-9 structure of a spine accurately, hence it is essential to demarcate and identify the vertebra in the MRI image. There are both supervised and unsupervised methods for vertebra segmentation and labeling. However, the acquisition of requisite data is a challenge to designing methods with very high ac