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

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书目名称Advances in Visual Computing影响因子(影响力)




书目名称Advances in Visual Computing影响因子(影响力)学科排名




书目名称Advances in Visual Computing网络公开度




书目名称Advances in Visual Computing网络公开度学科排名




书目名称Advances in Visual Computing被引频次




书目名称Advances in Visual Computing被引频次学科排名




书目名称Advances in Visual Computing年度引用




书目名称Advances in Visual Computing年度引用学科排名




书目名称Advances in Visual Computing读者反馈




书目名称Advances in Visual Computing读者反馈学科排名

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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

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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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