Titlebook: Image and Video Technology; PSIVT 2019 Internati Joel Janek Dabrowski,Ashfaqur Rahman,Manoranjan Pa Conference proceedings 2020 Springer Na

挑剔為人

Detecting Global Exam Events in Invigilation Videos Using 3D Convolutional Neural Network videos are defined according to the human activity performed at a certain phase in the entire exam process. Unlike general event detection which involves different scenes, global event detection focuses on differentiating different collective activities in the exam room ambiance. The challenges lie

STEER

Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognitionhieved on multimodal RGB-D images. The latter can play an important role in several computer vision and robotics applications. In this paper, we present spatial hierarchical analysis deep neural network, called ShaNet, for RGB-D object recognition. Our network consists of convolutional neural networ

Dendritic-Cells

Ebct207

0302-9743 apers presented were carefully selected from 26 submissions. The papers cover the full range of state-of-the-art research in image and video technology with topics ranging from well-established areas to novel current trends..978-3-030-39769-2978-3-030-39770-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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災(zāi)禍

Detecting Global Exam Events in Invigilation Videos Using 3D Convolutional Neural Networkatures and its effectiveness in detecting video events. Experiment results show the designed 3D convolutional neural network achieves an accuracy of its capability of 93.94% in detecting the global exam events, which demonstrates the effectiveness of our model.

GIBE

Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognitioned model has been tested on two different publicly available RGB-D datasets including Washington RGB-D and 2D3D object dataset. Our experimental results show that the proposed deep neural network achieves superior performance compared to existing RGB-D object recognition methods.

駭人

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