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

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書目名稱Advances in Visual Computing影響因子(影響力)




書目名稱Advances in Visual Computing影響因子(影響力)學科排名




書目名稱Advances in Visual Computing網(wǎng)絡公開度




書目名稱Advances in Visual Computing網(wǎng)絡公開度學科排名




書目名稱Advances in Visual Computing被引頻次




書目名稱Advances in Visual Computing被引頻次學科排名




書目名稱Advances in Visual Computing年度引用




書目名稱Advances in Visual Computing年度引用學科排名




書目名稱Advances in Visual Computing讀者反饋




書目名稱Advances in Visual Computing讀者反饋學科排名

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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,

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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

AMOR

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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