標(biāo)題: Titlebook: Computer Vision and Image Processing; 5th International Co Satish Kumar Singh,Partha Roy,P. Nagabhushan Conference proceedings 2021 The Edi [打印本頁(yè)] 作者: Alacrity 時(shí)間: 2025-3-21 16:20
書目名稱Computer Vision and Image Processing影響因子(影響力)
書目名稱Computer Vision and Image Processing影響因子(影響力)學(xué)科排名
書目名稱Computer Vision and Image Processing網(wǎng)絡(luò)公開度
書目名稱Computer Vision and Image Processing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Vision and Image Processing被引頻次
書目名稱Computer Vision and Image Processing被引頻次學(xué)科排名
書目名稱Computer Vision and Image Processing年度引用
書目名稱Computer Vision and Image Processing年度引用學(xué)科排名
書目名稱Computer Vision and Image Processing讀者反饋
書目名稱Computer Vision and Image Processing讀者反饋學(xué)科排名
作者: 母豬 時(shí)間: 2025-3-21 21:43 作者: 朦朧 時(shí)間: 2025-3-22 01:05
Stephen Chin,Johan Vos,James Weavercovering Esylum and Amentia quadrangles of Mars. The result shows the change detection map generated using MCC and Viking images, which focus the changing landscape of Mars due to wind streaks, dust deposits, landslides, lava flows and new impact craters formation.作者: Tremor 時(shí)間: 2025-3-22 08:17 作者: cogitate 時(shí)間: 2025-3-22 09:44 作者: 平常 時(shí)間: 2025-3-22 13:07
Eelco Plugge,Peter Membrey,Tim Hawkinsd concept is generic and can be used with any existing loss function. We test the Hard-Mining loss with different losses such as Cross-Entropy, Angular-Softmax and ArcFace. The proposed Hard-Mining loss is tested over widely used Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. The 作者: 平常 時(shí)間: 2025-3-22 20:24
The Definitive Guide to MongoDBal Neural Network (CNN) to utilize the benefits of both fixed camera vision and first-person vision. NST generates images having features from both egocentric datasets and fixed camera datasets, that are fed through a VGG-16 network trained on a fixed-camera dataset for feature extraction. These ext作者: paragon 時(shí)間: 2025-3-22 23:05 作者: Thyroiditis 時(shí)間: 2025-3-23 03:36 作者: Highbrow 時(shí)間: 2025-3-23 06:20 作者: 用不完 時(shí)間: 2025-3-23 12:05 作者: 中國(guó)紀(jì)念碑 時(shí)間: 2025-3-23 16:18 作者: FER 時(shí)間: 2025-3-23 20:00
The Definitive Guide to MongoDB; a) skeleton videos and angles of skeleton bones as features, b) The HOG features from the RGB frames. In both approaches, we train SVMs and recognize the KPs using them. The classifier generated by SVM predicts the sequence of KPs involved in a given .. Since KPs are the string-like encoding symbo作者: Accomplish 時(shí)間: 2025-3-24 00:04
The Definitive Guide to MongoDBed to detect presence of animals, and the ResNet50 model, trained using Triplet Loss, is used for animal re-identification. The prototype is tested using three animal species, and achieves detection accuracy of 80%, 89.47% and 92.56%, and re-identification accuracy of 99.6%, 86.2% and 61.7% respecti作者: 下船 時(shí)間: 2025-3-24 06:04 作者: Dissonance 時(shí)間: 2025-3-24 09:21 作者: glucagon 時(shí)間: 2025-3-24 13:10 作者: mettlesome 時(shí)間: 2025-3-24 15:48
Deep over and Under Exposed Region Detection, architecture and re-trained it on our custom dataset. To the best of our knowledge, this is the first attempt to use semantic segmentation and transfer learning methods to identify these regions in an end-to-end fashion. We obtain a Dice score and a Jaccard score of 0.93 and 0.86, respectively, whi作者: incontinence 時(shí)間: 2025-3-24 22:28
,DeepHDR-GIF: Capturing Motion in?High Dynamic Range Scenes,es and produced three in-between frames in a binary-search manner. At last, generated HDR frames and interpolated frames are merged in?to a GIF image, which depicts the motion in the scene without losing out on the dynamic range of the scene. The proposed framework works on different types of dynami作者: 館長(zhǎng) 時(shí)間: 2025-3-25 02:32
Hard-Mining Loss Based Convolutional Neural Network for Face Recognition,d concept is generic and can be used with any existing loss function. We test the Hard-Mining loss with different losses such as Cross-Entropy, Angular-Softmax and ArcFace. The proposed Hard-Mining loss is tested over widely used Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. The 作者: 舉止粗野的人 時(shí)間: 2025-3-25 03:51 作者: Parameter 時(shí)間: 2025-3-25 09:46 作者: Strength 時(shí)間: 2025-3-25 13:56 作者: Microgram 時(shí)間: 2025-3-25 16:10 作者: cardiac-arrest 時(shí)間: 2025-3-25 21:02 作者: 細(xì)菌等 時(shí)間: 2025-3-26 02:16
Using Class Activations to Investigate Semantic Segmentation, the gradient based CAMs (GradCAM) for the task of semantic segmentation. We also validate our results by using sanity checks for saliency maps and correlate them to those found for the classification task.作者: 整潔 時(shí)間: 2025-3-26 07:04 作者: 馬籠頭 時(shí)間: 2025-3-26 09:37 作者: 巡回 時(shí)間: 2025-3-26 16:01 作者: mosque 時(shí)間: 2025-3-26 18:59 作者: photopsia 時(shí)間: 2025-3-26 22:51 作者: mitten 時(shí)間: 2025-3-27 04:12
U-Net-Based Approach for Segmentation of Tables from Scanned Pages,odel is verified by testing the proposed system on the ICDAR 2013, ICDAR 2019, Marmot datasets and some randomly clicked images. Our model outperforms all the other methods presented in ICDAR 2019 table segmentation competition with an F score of 0.9694.作者: craven 時(shí)間: 2025-3-27 08:13
,Camera Based Parking Slot Detection for?Autonomous Parking,ikes, cones, carton boxes and trees, this method achieved a promising performance with F1 score higher than 97%. With the ability to run on low computational devices such as CPU, this method is adaptable to practical solutions for both AD and aftermarket ADAS systems.作者: PATHY 時(shí)間: 2025-3-27 12:48 作者: 潔凈 時(shí)間: 2025-3-27 17:30 作者: Ataxia 時(shí)間: 2025-3-27 20:09 作者: 吃掉 時(shí)間: 2025-3-27 22:23 作者: Gorilla 時(shí)間: 2025-3-28 03:10
The Definitive Guide to MongoDBts on caricature recognition dataset and subsequent comparison of our proposed network against the baseline model quantitatively substantiates our hypothesis. While comparing the performance of our modified network against the baseline, we were able to improve the recognition accuracy by . for . setting and by . for . setting.作者: Exterior 時(shí)間: 2025-3-28 09:41
Eelco Plugge,Peter Membrey,Tim Hawkinse maps in the final stage. Experimental results on the benchmark KITTI dataset show that the proposed modifications outperform the existing VoxelNet based models and other fusion based methods in terms of accuracy as well as time.作者: Mobile 時(shí)間: 2025-3-28 12:38
Deep Learning-Based Smart Parking Management System and Business Model,nd updated automatically. Billing for the parking space usage will also be done automatically as per the regulated guidelines. Raspberry Pi and deep learning tools are used for the implementation. The proposed system is cost-effective and reduces time and energy.作者: CARE 時(shí)間: 2025-3-28 18:40 作者: 全能 時(shí)間: 2025-3-28 18:47 作者: Axon895 時(shí)間: 2025-3-29 00:22
U-Net-Based Approach for Segmentation of Tables from Scanned Pages,ent while ignoring the rest of the page. We present a deep learning approach for segmentation of tables from a rich variety of documents and explain the effect of preprocessing, distortion correction on the output of deep learning model. Our method is based on the U-net architecture wherein convolut作者: thyroid-hormone 時(shí)間: 2025-3-29 05:22
Air Writing: Tracking and Tracing,ifying the world around us. Air writing as a concept has been prevalent in education systems all across the world; teachers and parents have been training children to gesture the shapes of letters and spell out words in the air to build the habit of writing, but its technology is relatively recent. 作者: FIS 時(shí)間: 2025-3-29 09:17 作者: companion 時(shí)間: 2025-3-29 12:28 作者: savage 時(shí)間: 2025-3-29 15:56 作者: jabber 時(shí)間: 2025-3-29 21:01 作者: 盡忠 時(shí)間: 2025-3-30 00:35
Hard-Mining Loss Based Convolutional Neural Network for Face Recognition,ved in face recognition, which touched upon various parts of the recognition framework like Convolutional Neural Network (CNN), Layers, Loss functions, etc. Various loss functions such as Cross-Entropy, Angular-Softmax and ArcFace have been introduced to learn the weights of network for face recogni作者: 跳脫衣舞的人 時(shí)間: 2025-3-30 06:57 作者: 終點(diǎn) 時(shí)間: 2025-3-30 09:22
Scene Text Recognition in the Wild with Motion Deblurring Using Deep Networks,ct information in a video captured by a moving vehicle. Video captured by a moving vehicle contains a lot of blurs caused by motion which is one of the major issues preventing accurate recognition of text. The second major issue is the orientation of the text being detected, which may not be in the 作者: 逗它小傻瓜 時(shí)間: 2025-3-30 15:58 作者: insipid 時(shí)間: 2025-3-30 18:12 作者: 揮舞 時(shí)間: 2025-3-30 22:13
Design and Implementation of Motion Envelope for a Moving Object Using Kinect for Windows,ithout considering the third dimension or the depth information. This poses a restriction on understanding the complete behavior of any object in space. Thus, this work aims at enlarging the scope of studying and analyzing the 3D behavior of an object through depth imaging. It has its application ac作者: 戲服 時(shí)間: 2025-3-31 02:06
Software Auto Trigger Recording for Super Slow Motion Videos Using Statistical Change Detection,ited CPU and GPU capabilities and memory. Almost all handheld devices cannot record such videos indefinitely and have a time limit (usually around 1?s) to keep the system from straining and crashing. In this time limit, anywhere around 480 or 960 or more full HD (resolution 1920 × 1080) frames are r作者: FOR 時(shí)間: 2025-3-31 08:39 作者: 拍翅 時(shí)間: 2025-3-31 10:08 作者: pulse-pressure 時(shí)間: 2025-3-31 16:08 作者: Oration 時(shí)間: 2025-3-31 19:01 作者: Inculcate 時(shí)間: 2025-4-1 01:11 作者: mendacity 時(shí)間: 2025-4-1 05:16
On the Performance of Convolutional Neural Networks Under High and Low Frequency Information,recognition, medical image analysis, etc. However, generally the trained CNN models are tested over the test set which is very similar to the trained set. The generalizability and robustness of the CNN models are very important aspects to make it to work for the unseen data. In this letter, we study作者: folliculitis 時(shí)間: 2025-4-1 09:12
https://doi.org/10.1007/978-1-4842-5602-2ent while ignoring the rest of the page. We present a deep learning approach for segmentation of tables from a rich variety of documents and explain the effect of preprocessing, distortion correction on the output of deep learning model. Our method is based on the U-net architecture wherein convolut作者: 技術(shù) 時(shí)間: 2025-4-1 13:56 作者: 無(wú)禮回復(fù) 時(shí)間: 2025-4-1 15:00
Stephen Chin,Johan Vos,James WeaverISRO’s Mars Color Camera (MCC) onboard Mangalyaan spacecraft send more than thousand images of planet Mars at varying spatial resolution, which is of utmost importance for doing surface change detection over Mars. In this paper, we have described a methodology for automated change detection using MC
