Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito

impale

978-3-031-31416-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

GRIN

Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/234061.jpg

南極

Computer Vision and Image Processing978-3-031-31417-9Series ISSN 1865-0929 Series E-ISSN 1865-0937

向前變橢圓

Integrating Plone with Other Systemsocation of a fire incident. Therefore smoke detection using vision based machine learning techniques have been quite useful. Recent techniques deploy deep learning models for smoke detection in an outdoor environment. Despite advancements in the field, smoke detection in challenging environments is

GRE

Customizing Plone’s Look and Feelased sensors is the main principle behind vision based Autonomous Robotic Grasping. To realise this task of autonomous object grasping, one of the critical sub-tasks is the 6D Pose Estimation of a known object of interest from sensory data in a given environment. The sensory data can include RGB ima

Calculus

xanthelasma

Administering and Scaling Plone,ions of this country. To safeguard the power vested in the people, it is essential that the voting process is safe, fair and transparent. This can be very well ensured by effective surveillance of polling activities and analysis of the real-time data that can be gathered from the polling stations. T

chuckle

Integrating with Other Systems,tc.) is a challenging and time-consuming process. In this paper, a non-averaged DenseNet-169 (NADenseNet-169) CNN architecture is proposed and demonstrated to perform real-time plant species recognition. The architecture is evaluated on two datasets namely, Flavia (Standard) and Leaf-12 (custom crea

聰明

https://doi.org/10.1007/978-1-4302-0534-0ightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters. We propose two novel algorithms to rank and prune

FISC

Introducing the Model and SQLAlchemyalculating the pairwise similarity between each sampled frame of the video, using the per frame features extracted by the feature extraction module and a suitable distance metric in the temporal self-similarity(TSM) calculation module. We pass this calculated TSM matrix to the count prediction modul