標(biāo)題: Titlebook: Advances in Visual Computing; 15th International S George Bebis,Zhaozheng Yin,George Baciu Conference proceedings 2020 Springer Nature Swit [打印本頁] 作者: 貪污 時間: 2025-3-21 18:22
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(xué)科排名
書目名稱Advances in Visual Computing網(wǎng)絡(luò)公開度
書目名稱Advances in Visual Computing網(wǎng)絡(luò)公開度學(xué)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(xué)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(xué)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(xué)科排名
作者: Creatinine-Test 時間: 2025-3-21 23:58
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,作者: voluble 時間: 2025-3-22 01:28
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 作者: 轉(zhuǎn)換 時間: 2025-3-22 07:38 作者: 微不足道 時間: 2025-3-22 09:39 作者: 無能的人 時間: 2025-3-22 14:39 作者: 母豬 時間: 2025-3-22 18:48 作者: 試驗 時間: 2025-3-22 23:19
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作者: AMOR 時間: 2025-3-23 04:14
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作者: DNR215 時間: 2025-3-23 09:18 作者: NEG 時間: 2025-3-23 13:20
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scanspose a challenge for current Convolutional Neural Networks (CNN) which often fail when image features are very small (8 pixels). Our approach modifies the effective receptive field at different layers in a CNN so that objects with a broad range of varying scales can be detected in a single forward p作者: 讓步 時間: 2025-3-23 17:12
DeepTKAClassifier: Brand Classification of Total Knee Arthroplasty Implants Using Explainable Deep Cr the vast majority of patients. However, a TKA surgery may fail over time for several reasons, thus it requires a revision arthroplasty surgery. Identifying TKA implants is a critical consideration in preoperative planning of revision surgery. This study aims to develop, train, and validate deep co作者: 贊成你 時間: 2025-3-23 20:46 作者: FILLY 時間: 2025-3-24 01:40
Robust Prostate Cancer Classification with Siamese Neural Networks breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with expert radiologists, can obtain very accurate predictions, working with noisy data can induce a wrong diagnose or require a new acquisition, spending time and exposing the patient to作者: tackle 時間: 2025-3-24 06:08
Simple Camera-to-2D-LiDAR Calibration Method for General Usetems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low-cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration ro作者: Collision 時間: 2025-3-24 10:05 作者: 舔食 時間: 2025-3-24 12:19
DeepTKAClassifier: Brand Classification of Total Knee Arthroplasty Implants Using Explainable Deep Cnvolutional neural network models to precisely classify four widely-used TKA implants based on only plain knee radiographs. Using 9,052 computationally annotated knee radiographs, we achieved weighted average precision, recall, and F1-score of 0.97, 0.97, and 0.97, respectively, with Cohen Kappa of 0.96.作者: 切碎 時間: 2025-3-24 16:30
Parliament and the Constitutionta augmentation and balancing. We show that a very small convolutional neural network (SAT-CNN) with approximately three million parameters can outperform a deep pre-trained classifier, VGG16 - which is used for many state-of-the-art tasks - with over 138 million parameters.作者: NATAL 時間: 2025-3-24 21:59
Politikwissenschaftliche Paperbacks. We use data coming from the ProstateX challenge and demonstrate the superior robustness of our model to random noise compared to a similar architecture, albeit deprived of the Siamese branch. In addition, our approach is also resistant to adversarial attacks and shows overall better AUC performance.作者: 來自于 時間: 2025-3-25 02:33
Conference proceedings 2020tracking; 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.作者: 正面 時間: 2025-3-25 05:49 作者: hedonic 時間: 2025-3-25 09:19 作者: 滔滔不絕地說 時間: 2025-3-25 11:53
Parliament and the Constitutionp?to 67 frames per second for . inputs using a single nVIDIA GTX1080 GPU. The proposed network also outperforms the current state-of-the-art methods on the KITTI benchmark. The ASPPF-based network and edge-guided post-processing produces better results, both quantitatively and qualitatively than the competitors.作者: 可卡 時間: 2025-3-25 17:30 作者: LAST 時間: 2025-3-25 20:47 作者: 地牢 時間: 2025-3-26 03:07
https://doi.org/10.1007/978-3-663-09508-8a cascade residual-inception module and a deconvolution module with additional context information. When integrated into a Single Shot MultiBox Detector (SSD), these additions permit more accurate detection of small-scale objects. The results permit efficient real-time analysis of medical images in pathology and related biomedical research fields.作者: Anemia 時間: 2025-3-26 07:53 作者: Permanent 時間: 2025-3-26 12:07 作者: 去掉 時間: 2025-3-26 14:06
Image Categorization Using Agglomerative Clustering Based Smoothed Dirichlet Mixturesset that contains different indoor and outdoor places reveal the importance of the hierarchical clustering when categorizing images. The conducted tests prove the robustness of the proposed image categorization approach as compared to the other related-works.作者: 思考才皺眉 時間: 2025-3-26 17:53 作者: 尾隨 時間: 2025-3-26 21:15 作者: FOVEA 時間: 2025-3-27 05:01 作者: Coterminous 時間: 2025-3-27 08:05
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scansa cascade residual-inception module and a deconvolution module with additional context information. When integrated into a Single Shot MultiBox Detector (SSD), these additions permit more accurate detection of small-scale objects. The results permit efficient real-time analysis of medical images in pathology and related biomedical research fields.作者: 常到 時間: 2025-3-27 11:12 作者: 朋黨派系 時間: 2025-3-27 17:08
Politikwissenschaftliche Paperbacksnvolutional neural network models to precisely classify four widely-used TKA implants based on only plain knee radiographs. Using 9,052 computationally annotated knee radiographs, we achieved weighted average precision, recall, and F1-score of 0.97, 0.97, and 0.97, respectively, with Cohen Kappa of 0.96.作者: 可卡 時間: 2025-3-27 18:51 作者: receptors 時間: 2025-3-27 22:58 作者: 沙草紙 時間: 2025-3-28 05:44
Conference proceedings 2020which 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:作者: avenge 時間: 2025-3-28 07:58 作者: Enrage 時間: 2025-3-28 11:38 作者: 暫時別動 時間: 2025-3-28 16:08
The General Elections: 2015, 2017, 2019algorithms 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 作者: FEAT 時間: 2025-3-28 20:59
Parliament and the Constitutionlow object resolution. In this work we focus on recognizing objects taken from the xView Satellite Imagery dataset. The xView dataset introduces its own set of challenges, the most prominent being the imbalance between the 60 classes present. xView also contains considerable label noise as well as b作者: Retrieval 時間: 2025-3-28 23:27
https://doi.org/10.1007/978-3-031-21464-6d subtle inter-class differences. In this paper, we tackle this problem in a weakly supervised manner, where neural network models are getting fed with additional data using a data augmentation technique through a visual attention mechanism. We perform domain adaptive knowledge transfer via fine-tun作者: Ingredient 時間: 2025-3-29 05:10
Parliament and the Constitutionecent work introduced a post-processing method to reduce occlusion fading; however, the results have a severe halo effect. This work proposes a novel edge-guided post-processing method that reduces occlusion fading for self-supervised monocular depth estimation. We also introduce Atrous Spatial Pyra作者: 追逐 時間: 2025-3-29 07:25 作者: 自愛 時間: 2025-3-29 12:17
,Umpire or Player: Nixon’s Economic Games,ction 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作者: blackout 時間: 2025-3-29 18:21
Richard Nixon: An Electable Conservative?,the 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作者: 慢跑鞋 時間: 2025-3-29 22:00 作者: ETCH 時間: 2025-3-30 02:48
https://doi.org/10.1007/978-3-663-09508-8pose a challenge for current Convolutional Neural Networks (CNN) which often fail when image features are very small (8 pixels). Our approach modifies the effective receptive field at different layers in a CNN so that objects with a broad range of varying scales can be detected in a single forward p作者: octogenarian 時間: 2025-3-30 08:07 作者: ALERT 時間: 2025-3-30 10:30
Politikwissenschaftliche Paperbacksning an efficient image fusion technique is still a challenging task. In this paper, we propose an improved multi-modal medical image fusion method to enhance the visual quality and contrast of the fused image. To achieve this work, the registered source images are firstly decomposed into low-freque作者: 中世紀(jì) 時間: 2025-3-30 16:16
Politikwissenschaftliche Paperbacks breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with expert radiologists, can obtain very accurate predictions, working with noisy data can induce a wrong diagnose or require a new acquisition, spending time and exposing the patient to作者: 蕁麻 時間: 2025-3-30 19:16
https://doi.org/10.1057/9781137295248tems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low-cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration ro
