標(biāo)題: Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito [打印本頁(yè)] 作者: supplementary 時(shí)間: 2025-3-21 19:02
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: 褲子 時(shí)間: 2025-3-22 00:13 作者: 強(qiáng)所 時(shí)間: 2025-3-22 01:12 作者: 冒失 時(shí)間: 2025-3-22 06:10 作者: 獎(jiǎng)牌 時(shí)間: 2025-3-22 12:42 作者: judiciousness 時(shí)間: 2025-3-22 14:10
Self Similarity Matrix Based CNN Filter Pruning,ightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters. We propose two novel algorithms to rank and prune作者: judiciousness 時(shí)間: 2025-3-22 17:46
,Class Agnostic, On-Device and?Privacy Preserving Repetition Counting of?Actions from?Videos Using Salculating the pairwise similarity between each sampled frame of the video, using the per frame features extracted by the feature extraction module and a suitable distance metric in the temporal self-similarity(TSM) calculation module. We pass this calculated TSM matrix to the count prediction modul作者: 初次登臺(tái) 時(shí)間: 2025-3-23 00:42 作者: 必死 時(shí)間: 2025-3-23 03:34
,Attention Residual Capsule Network for?Dermoscopy Image Classification, automated classification algorithms using deep convolutional neural network (DCNN) models have been proposed, the need for performance improvement remains. The key limitations of developing a robust DCNN model for the dermoscopic image classification are (a) sub-sampling or pooling layer in traditi作者: forbid 時(shí)間: 2025-3-23 08:08
,SAMNet: Semantic Aware Multimodal Network for?Emoji Drawing Classification,ile writing on touch-responsive devices, searching for emojis to capture the true intent is cumbersome. To solve this problem, the existing solutions consider either the text or only stroke-based drawings to predict the appropriate emojis. We do not leverage the full context by considering only a si作者: acrimony 時(shí)間: 2025-3-23 13:35 作者: Innovative 時(shí)間: 2025-3-23 16:26
Rain Streak Removal via Spatio-Channel Based Spectral Graph CNN for Image Deraining,g deraining methods ignores long range contextual information and utilize only local spatial information. To address this issue, a Spatio-channel based Spectral Graph Convolutional Neural Network (SCSGCNet) for image deraining was proposed and two new modules were introduced to extract representatio作者: 高腳酒杯 時(shí)間: 2025-3-23 20:44 作者: allergen 時(shí)間: 2025-3-24 01:48 作者: Graduated 時(shí)間: 2025-3-24 04:16 作者: Stricture 時(shí)間: 2025-3-24 06:56 作者: 箴言 時(shí)間: 2025-3-24 11:45
A Curated Dataset for Spinach Species Identification,es because of the structure similarity of many plant species. So, automated spinach recognition will support the people community to a greater extent. In this study, we present spinach dataset, a freely accessible annotated collection of images of spinach leaves in Indian scenario. We propose three 作者: 我們的面粉 時(shí)間: 2025-3-24 17:38 作者: 襲擊 時(shí)間: 2025-3-24 20:34
,Computing Digital Signature by?Transforming 2D Image to?3D: A Geometric Perspective,o various 3D reconstruction techniques using neural nets, with the majority of approaches producing high-quality results and efficiency. This paper presents an approach to convert 2D facial images to 3D and then use the 3D data and features to construct a unique digital signature. The proposed solut作者: 圓木可阻礙 時(shí)間: 2025-3-25 01:14
A Curated Dataset for Spinach Species Identification,different custom designed convolutional neural networks (CNN) and compare the performance of the same. Also we apply the transfer learning approach using MobileNetV2 pretrained model for this spinach species recognition. Using transfer learning approach we got an accuracy of 92.96%.作者: epicardium 時(shí)間: 2025-3-25 04:52
https://doi.org/10.1007/978-1-4302-0534-0g after training the model. Both the training and pruning process is completed simultaneously. We benchmark our method on two of the most popular CNN models - ResNet and VGG and record their performance on the CIFAR-10 dataset.作者: BRIBE 時(shí)間: 2025-3-25 10:29
https://doi.org/10.1007/978-1-4302-0534-0 images). We present a simple yet efficient algorithm using the concepts of classical image processing techniques to solve the problem, and the obtained results are promising in comparison to the Office Lens.作者: 有抱負(fù)者 時(shí)間: 2025-3-25 11:38
Self Similarity Matrix Based CNN Filter Pruning,g after training the model. Both the training and pruning process is completed simultaneously. We benchmark our method on two of the most popular CNN models - ResNet and VGG and record their performance on the CIFAR-10 dataset.作者: mediocrity 時(shí)間: 2025-3-25 16:45 作者: accessory 時(shí)間: 2025-3-25 22:36 作者: cartilage 時(shí)間: 2025-3-26 02:08
1865-0929 sentation,? Motion? and? Tracking,? Image/? Video? Scene? Understanding,? Image/Video? Retrieval,? Remote? Sensing,? Hyperspectral? Image? Processing,? Face,? Iris,?Emotion, Sign Language and Gesture Recognition, etc..978-3-031-31416-2978-3-031-31417-9Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: ELUDE 時(shí)間: 2025-3-26 06:00
Integrating with Other Systems,trics. In case of Flavia dataset, an accuracy of 98.58% is obtained with a computational time of 3.53?s. For Leaf-12 dataset, an accuracy of 99% is obtained with a computational time of 4.45?s. The model trained on Leaf-12 dataset performed better in identifying the plant species under unconstrained environment.作者: Matrimony 時(shí)間: 2025-3-26 12:18 作者: 多骨 時(shí)間: 2025-3-26 14:49
Working with Forms and Validators technique outperforms state-of-the-art approaches (. SFFCM and an approach by Wang .) when tested using metrics such as Accuracy, the Jaccard Index, F1-score, False Alarms and Misses. We also show that the FLLF technique is more computationally efficient.作者: Gnrh670 時(shí)間: 2025-3-26 17:47 作者: 一致性 時(shí)間: 2025-3-26 21:52
Working with Forms and Validators. The residual connection used in the network avoids the vanishing gradient problem. We have extracted a two-class (low-grade/high-grade) dataset from REMBRANDT repository. The proposed model has attained an accuracy of 96.39% and outperforms its competing models in vital metrics.作者: ineptitude 時(shí)間: 2025-3-27 02:04 作者: Redundant 時(shí)間: 2025-3-27 07:34
,Class Agnostic, On-Device and?Privacy Preserving Repetition Counting of?Actions from?Videos Using Szation to actions not observed during training. We utilize the largest available dataset for repetition counting, Countix, for training and evaluation. We also propose a way for effectively augmenting the training data in Countix. Our experiments show SOTA comparable accuracies with significantly smaller model footprints.作者: Vsd168 時(shí)間: 2025-3-27 13:28
,Segmentation of?Smoke Plumes Using Fast Local Laplacian Filtering, technique outperforms state-of-the-art approaches (. SFFCM and an approach by Wang .) when tested using metrics such as Accuracy, the Jaccard Index, F1-score, False Alarms and Misses. We also show that the FLLF technique is more computationally efficient.作者: BAIT 時(shí)間: 2025-3-27 14:16
Rain Streak Removal via Spatio-Channel Based Spectral Graph CNN for Image Deraining,ur network was able to model feature representations from local, global spatial patterns and channel correlations. Experimental results on five synthetic and real-world datasets shows that the proposed network achieves state-of-the-art (SOTA) results.作者: CLAIM 時(shí)間: 2025-3-27 18:46
,Brain Tumor Grade Detection Using Transfer Learning and?Residual Multi-head Attention Network,. The residual connection used in the network avoids the vanishing gradient problem. We have extracted a two-class (low-grade/high-grade) dataset from REMBRANDT repository. The proposed model has attained an accuracy of 96.39% and outperforms its competing models in vital metrics.作者: GUMP 時(shí)間: 2025-3-27 22:21
1865-0929 Processing, CVIP 2022, held in Nagpur, India, November 4–6, 2022..The 110 full papers and 11 short papers?were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical?Image? Analysis,? I作者: 非實(shí)體 時(shí)間: 2025-3-28 04:36
Conference proceedings 2023, CVIP 2022, held in Nagpur, India, November 4–6, 2022..The 110 full papers and 11 short papers?were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical?Image? Analysis,? Image/? Vid作者: Transfusion 時(shí)間: 2025-3-28 06:37
The Web Server Gateway Interface (WSGI)different custom designed convolutional neural networks (CNN) and compare the performance of the same. Also we apply the transfer learning approach using MobileNetV2 pretrained model for this spinach species recognition. Using transfer learning approach we got an accuracy of 92.96%.作者: modish 時(shí)間: 2025-3-28 12:29
https://doi.org/10.1007/978-3-031-31417-9Computer Science; Informatics; Conference Proceedings; Research; Applications作者: impale 時(shí)間: 2025-3-28 14:53
978-3-031-31416-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: GRIN 時(shí)間: 2025-3-28 19:27
Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/234061.jpg作者: 南極 時(shí)間: 2025-3-28 23:56
Computer Vision and Image Processing978-3-031-31417-9Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 向前變橢圓 時(shí)間: 2025-3-29 07:05
Integrating Plone with Other Systemsocation of a fire incident. Therefore smoke detection using vision based machine learning techniques have been quite useful. Recent techniques deploy deep learning models for smoke detection in an outdoor environment. Despite advancements in the field, smoke detection in challenging environments is 作者: GRE 時(shí)間: 2025-3-29 09:22
Customizing Plone’s Look and Feelased sensors is the main principle behind vision based Autonomous Robotic Grasping. To realise this task of autonomous object grasping, one of the critical sub-tasks is the 6D Pose Estimation of a known object of interest from sensory data in a given environment. The sensory data can include RGB ima作者: Calculus 時(shí)間: 2025-3-29 13:44 作者: xanthelasma 時(shí)間: 2025-3-29 17:16
Administering and Scaling Plone,ions of this country. To safeguard the power vested in the people, it is essential that the voting process is safe, fair and transparent. This can be very well ensured by effective surveillance of polling activities and analysis of the real-time data that can be gathered from the polling stations. T作者: chuckle 時(shí)間: 2025-3-29 20:23
Integrating with Other Systems,tc.) is a challenging and time-consuming process. In this paper, a non-averaged DenseNet-169 (NADenseNet-169) CNN architecture is proposed and demonstrated to perform real-time plant species recognition. The architecture is evaluated on two datasets namely, Flavia (Standard) and Leaf-12 (custom crea作者: 聰明 時(shí)間: 2025-3-30 01:32
https://doi.org/10.1007/978-1-4302-0534-0ightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters. We propose two novel algorithms to rank and prune作者: FISC 時(shí)間: 2025-3-30 04:54
Introducing the Model and SQLAlchemyalculating the pairwise similarity between each sampled frame of the video, using the per frame features extracted by the feature extraction module and a suitable distance metric in the temporal self-similarity(TSM) calculation module. We pass this calculated TSM matrix to the count prediction modul作者: incisive 時(shí)間: 2025-3-30 12:17 作者: Adrenaline 時(shí)間: 2025-3-30 15:16
Working with Forms and Validators automated classification algorithms using deep convolutional neural network (DCNN) models have been proposed, the need for performance improvement remains. The key limitations of developing a robust DCNN model for the dermoscopic image classification are (a) sub-sampling or pooling layer in traditi作者: MUMP 時(shí)間: 2025-3-30 18:42 作者: FADE 時(shí)間: 2025-3-30 21:53 作者: 為寵愛 時(shí)間: 2025-3-31 02:42 作者: BOOST 時(shí)間: 2025-3-31 08:33 作者: Flustered 時(shí)間: 2025-3-31 12:51
Starting the SimpleSite Tutorialng to WHO statistics, India is one of the developing countries with highest prevalence of anaemia. Conventional invasive methods are cost-prohibitive and difficult to administer globally which essentially demands non-invasive, accurate, and low-cost approaches for screening of anaemia. The current w作者: AMITY 時(shí)間: 2025-3-31 15:07 作者: 鄙視讀作 時(shí)間: 2025-3-31 18:14
Working with Forms and Validatorsclassification which are trained for non-medical image classification.Thus, there is a need for re-training and feature enhancement for better performance in medical image classification. In this paper, we have proposed a residual multi-head attention network to uplift the re-training process with p作者: lambaste 時(shí)間: 2025-3-31 22:56
The Web Server Gateway Interface (WSGI)es because of the structure similarity of many plant species. So, automated spinach recognition will support the people community to a greater extent. In this study, we present spinach dataset, a freely accessible annotated collection of images of spinach leaves in Indian scenario. We propose three 作者: 我不死扛 時(shí)間: 2025-4-1 03:48
https://doi.org/10.1007/978-1-4302-0534-0Automatic extraction, recognition, and retrieval are necessary to process the huge chunk of digitized document data. However, an important step in all of these pipelines is the pre-processing step, mainly image enhancement or clean-up, which enhances the text regions and suppresses the non-text regi作者: Favorable 時(shí)間: 2025-4-1 07:39 作者: 酷熱 時(shí)間: 2025-4-1 12:44
