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Titlebook: Advances in Visual Computing; 15th International S George Bebis,Zhaozheng Yin,George Baciu Conference proceedings 2020 Springer Nature Swit

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發(fā)表于 2025-3-21 18:22:24 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Advances in Visual Computing
期刊簡稱15th International S
影響因子2023George Bebis,Zhaozheng Yin,George Baciu
視頻videohttp://file.papertrans.cn/151/150125/150125.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Visual Computing; 15th International S George Bebis,Zhaozheng Yin,George Baciu Conference proceedings 2020 Springer Nature Swit
影響因子.This two-volume set of LNCS 12509 and 12510 constitutes the refereed proceedings of the 15th International Symposium on Visual Computing, ISVC 2020, which was supposed to be held in San Diego, CA, USA in October 2020, took place virtually instead due to the COVID-19 pandemic...The 118 papers presented in these volumes were carefully reviewed and selected from 175 submissions. The papers are organized into the following topical sections: .Part I: deep learning; segmentation; visualization; video analysis and event recognition; ST: computational bioimaging; applications; biometrics; motion and tracking; computer graphics; virtual reality; and ST: computer vision advances in geo-spatial applications and remote sensing..Part II: object recognition/detection/categorization; 3D reconstruction; medical image analysis; vision for robotics; statistical pattern recognition; posters.
Pindex Conference proceedings 2020
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書目名稱Advances in Visual Computing影響因子(影響力)




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發(fā)表于 2025-3-21 23:58:18 | 只看該作者
A Scale-Aware YOLO Model for Pedestrian Detectionization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection,
板凳
發(fā)表于 2025-3-22 01:28:44 | 只看該作者
Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixturesalgorithms for the purpose of categorizing and recognizing images. Hierarchical clustering methods have shown promising results in computer vision applications. In this paper, we present a new unsupervised image categorization technique in which we cluster images using an agglomerative hierarchical
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Minimal Free Space Constraints for Implicit Distance Boundsction often involves evaluating the model’s implicit function at several points in space. When the model is expensive to evaluate, the number of points can become a bottleneck, making the use of volumetric information, such as free space constraints, challenging. When the model is the Euclidean dist
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發(fā)表于 2025-3-23 04:14:34 | 只看該作者
Fetal Brain Segmentation Using Convolutional Neural Networks with Fusion Strategiesthe following consequences; firstly, the model may not be optimised, and secondly the model may be prone to noise hence more sensitive to false positives/negatives, both resulting in poorer results. In this paper, we propose four fusion strategies to promote ensemble learning within a network archit
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