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Titlebook: Recent Trends in Image Processing and Pattern Recognition; Second International K. C. Santosh,Ravindra S. Hegadi Conference proceedings 201

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樓主: Malnutrition
41#
發(fā)表于 2025-3-28 18:16:51 | 只看該作者
R. Srinivasa Perumal,K. C. Santosh,P. V. S. S. R. Chandra Mouliential of experience-centered design to continue the humanist agenda by giving a voice to those whomight otherwise be excluded from design and by creating opportunities for people to enrich their lived experience with and through technology. Table of Contents: How Did We Get Here? / Some Key Ideas B
42#
發(fā)表于 2025-3-28 18:56:05 | 只看該作者
43#
發(fā)表于 2025-3-29 02:11:22 | 只看該作者
Shridevi S. Vasekar,Sanjivani K. Shahential of experience-centered design to continue the humanist agenda by giving a voice to those whomight otherwise be excluded from design and by creating opportunities for people to enrich their lived experience with and through technology. Table of Contents: How Did We Get Here? / Some Key Ideas B
44#
發(fā)表于 2025-3-29 03:31:24 | 只看該作者
45#
發(fā)表于 2025-3-29 10:59:02 | 只看該作者
LBP-Haar Cascade Based Real-Time Pedestrian Protection System Using Raspberry Pin detection. A comparison of results between this custom classifier with the standard Haar classifier is shown here. This system is tested on an Indian dataset collected by us and on the Penn-Fudan Database for Pedestrian Detection and Segmentation dataset for comparing the results.
46#
發(fā)表于 2025-3-29 13:45:15 | 只看該作者
Reconstructing a 3D Room from a Kinect Carrying UAVnted in this paper is approached from a practical point of view rather than purely theoretical basis. The end result is a physical prototype which is ready to be deployed in the field for further testing.
47#
發(fā)表于 2025-3-29 18:37:30 | 只看該作者
48#
發(fā)表于 2025-3-29 20:21:34 | 只看該作者
49#
發(fā)表于 2025-3-30 00:33:39 | 只看該作者
A Novel Foreground Segmentation Method Using Convolutional Neural Networkn this background subtraction technique. However, the algorithms give the false alarm in case of complex scenarios such as dynamic background, camera motion, shadow, illumination variation, camouflage, etc. A foreground segmentation system using convolutional neural network framework is proposed in
50#
發(fā)表于 2025-3-30 06:21:41 | 只看該作者
Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Comg on this area with different approaches to solve the problem since it has a many challenge. Every vehicle has its on own unique features for recognition. This paper focus on identifying the vehicle brand based on its geometrical features and diverse appearance-based attributes like colour, occlusio
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