標(biāo)題: Titlebook: Geometry and Vision; First International Minh Nguyen,Wei Qi Yan,Harvey Ho Conference proceedings 2021 Springer Nature Switzerland AG 2021 [打印本頁] 作者: 黑暗社會(huì) 時(shí)間: 2025-3-21 17:20
書目名稱Geometry and Vision影響因子(影響力)
書目名稱Geometry and Vision影響因子(影響力)學(xué)科排名
書目名稱Geometry and Vision網(wǎng)絡(luò)公開度
書目名稱Geometry and Vision網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Geometry and Vision被引頻次
書目名稱Geometry and Vision被引頻次學(xué)科排名
書目名稱Geometry and Vision年度引用
書目名稱Geometry and Vision年度引用學(xué)科排名
書目名稱Geometry and Vision讀者反饋
書目名稱Geometry and Vision讀者反饋學(xué)科排名
作者: 慢慢沖刷 時(shí)間: 2025-3-21 21:25 作者: 有權(quán)威 時(shí)間: 2025-3-22 02:25
Conference proceedings 2021ary 2021. Due to the COVID-19 pandemic the conference was held in partially virtual format.?.The 29 papers were thoroughly reviewed and selected from 50 submissions. They cover topics in areas of digital geometry, graphics, image and video technologies, computer vision, and multimedia technologies..作者: 額外的事 時(shí)間: 2025-3-22 08:00
,Zentralit?t und Prestige in Netzwerken,without privacy protection, as current methods for privacy preservation will slow down model training and testing. In order to resolve this problem, we develop a new noise generating method based on information entropy by using differential privacy for betterment the privacy protection which owns th作者: Omniscient 時(shí)間: 2025-3-22 10:44 作者: 突變 時(shí)間: 2025-3-22 15:09
,Analyse von Schalt- und übergangsvorg?ngen,s, namely, Faster R-CNN and YOLOv5, representing two-stage and one-stage algorithms, are employed to conduct tree leaves detection. Our results show that YOLOv5 model obviously outperforms to the Faster R-CNN in the speed of both model training and object detection. The difference between these two 作者: 突變 時(shí)間: 2025-3-22 17:31
Schleifen- und Schnittmengenanalyse, motivated researchers to design automatic diagnostic systems. Image segmentation is one of the crucial and challenging steps in the design of a computer-aided diagnosis system owing to the presence of low contrast between skin lesion and background, noise artifacts, color variations, and irregular 作者: mendacity 時(shí)間: 2025-3-22 23:22
https://doi.org/10.1007/978-3-476-05046-5peness automatically. Apple ripeness classification is a problem in computer vision and deep learning for pattern classification. In this paper, the ripeness of apples in digital images will be classified by using convolutional neural networks (CNN or ConvNets) in deep learning. The goal of this pro作者: MIRE 時(shí)間: 2025-3-23 04:09 作者: invade 時(shí)間: 2025-3-23 07:57
https://doi.org/10.1007/978-3-476-05191-2missed detection or incorrect positioning. In this paper, we propose a traffic sign recognition algorithm based on Faster R-CNN and YOLOv5. Firstly, we conduct image preprocessing by using guided image filtering for the input image to remove noises. The processed images are imported into the neural 作者: 鋼筆尖 時(shí)間: 2025-3-23 10:30 作者: reception 時(shí)間: 2025-3-23 14:38
Einführung in die Neurolinguistik (CapsNet) is proposed in this paper, which shows positive result. We also propose a Selective Kernel Network (SKNet) with attention mechanism in order to extract spatial information. Sign language as an important means of communications, the problems of recognizing sign language from digital videos作者: TOXIN 時(shí)間: 2025-3-23 20:53
https://doi.org/10.1007/978-3-642-93370-7and has stringent rules to control fishing and to protect the continued growth of marine inhabitants. Fishing inspections, such as identifying and counting shellfish, are part of the daily routine of many New Zealand Fisheries officers. It is however considered labour-intensive and time-consuming wo作者: ostrish 時(shí)間: 2025-3-24 01:13 作者: dissent 時(shí)間: 2025-3-24 04:27 作者: convert 時(shí)間: 2025-3-24 09:28
Die einfachsten optischen Beobachtungen,oblem of finding a .-approximation Euclidean shortest path between . and ., crossing the segments in . in order. Let . be the maximum Euclidean length of the segments in . and . be the minimum distance between two consecutive segments in .. The running time of our algorithm is .. Currently, the runn作者: interrupt 時(shí)間: 2025-3-24 13:49
Der Dualismus von Welle und Korpuskel,d decoder, in which the encoder part uses VGG16 combined with cavity convolution as the basic network to extract the features of lane lines, and the cavity convolution can expand the receptive field. Through experimental comparison, the full connection layer of the network is discarded, the last max作者: 鐵塔等 時(shí)間: 2025-3-24 17:06
Interferenzerscheinungen nebst Anwendungen, we exploit the representation of graphical objects by maximal primitives we have introduced in previous work. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line seg作者: PON 時(shí)間: 2025-3-24 22:54 作者: alabaster 時(shí)間: 2025-3-24 23:16
Die einfachsten optischen Beobachtungen,ble as elements of discrete objects. For the description of linear objects in a discrete space, algebraic discrete geometry provides a unified treatment employing double Diophantus equations. Furthermore, we develop an algorithm for the polygonalisation of discrete objects on the hexagonal grid syst作者: 柔軟 時(shí)間: 2025-3-25 06:30 作者: 洞察力 時(shí)間: 2025-3-25 08:33 作者: ARY 時(shí)間: 2025-3-25 12:11
https://doi.org/10.1007/978-3-030-72073-5artificial intelligence; communication systems; computer networks; computer systems; computer vision; dee作者: 小故事 時(shí)間: 2025-3-25 18:14
978-3-030-72072-8Springer Nature Switzerland AG 2021作者: grenade 時(shí)間: 2025-3-25 20:35 作者: 極端的正確性 時(shí)間: 2025-3-26 00:16 作者: Irrepressible 時(shí)間: 2025-3-26 06:54 作者: 中古 時(shí)間: 2025-3-26 09:15
https://doi.org/10.1007/978-3-476-05191-2e conduct image preprocessing by using guided image filtering for the input image to remove noises. The processed images are imported into the neural networks for training and testing. The outcomes of the traffic sign recognition are promising.作者: 危險(xiǎn) 時(shí)間: 2025-3-26 14:29 作者: 閑聊 時(shí)間: 2025-3-26 16:54 作者: 轉(zhuǎn)折點(diǎn) 時(shí)間: 2025-3-26 22:27 作者: minaret 時(shí)間: 2025-3-27 03:38 作者: glisten 時(shí)間: 2025-3-27 06:54 作者: Ingratiate 時(shí)間: 2025-3-27 11:39
,Energie der Strahlung und Bündelbegrenzung, 2D, and boundary surface genus for 3D. For 2D images, we designed a linear time algorithm to solve the hole counting problem. In 3D, we also designed a .(.) time algorithm to obtain the genus of a closed surface. These two algorithms are both in . space complexity.作者: 少量 時(shí)間: 2025-3-27 16:48 作者: Affiliation 時(shí)間: 2025-3-27 21:49 作者: Nomadic 時(shí)間: 2025-3-28 00:02 作者: 藕床生厭倦 時(shí)間: 2025-3-28 05:14 作者: 男生如果明白 時(shí)間: 2025-3-28 09:42 作者: intangibility 時(shí)間: 2025-3-28 12:19 作者: neutrophils 時(shí)間: 2025-3-28 15:43
Traffic-Sign Recognition Using Deep Learning,r the traffic-sign recognition in New Zealand. In order to determine which deep learning models are the most suitable one for the TSR, we choose two kinds of models to conduct deep learning computations: Faster R-CNN and YOLOv5. According to the scores of various metrics, we summarized the pros and cons of the picked models for the TSR task.作者: Electrolysis 時(shí)間: 2025-3-28 18:45
Segment- and Arc-Based Vectorizations by Multi-scale/Irregular Tangential Covering,construct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. We explain our novel complete pipeline in this work, and present its experimental evaluation by considering both synthetic and real image data.作者: Reclaim 時(shí)間: 2025-3-29 01:39 作者: Blatant 時(shí)間: 2025-3-29 06:40 作者: Iatrogenic 時(shí)間: 2025-3-29 07:41 作者: 雜役 時(shí)間: 2025-3-29 13:29 作者: parsimony 時(shí)間: 2025-3-29 17:24 作者: 小母馬 時(shí)間: 2025-3-29 21:09
Apple Ripeness Identification Using Deep Learning,ifiers are able to achieve the best result, i.e., the ripeness class of an apple from a given digital image is able to be precisely predicted. We have optimized the deep learning models and trained the classifiers so as to achieve the best outcome.作者: collateral 時(shí)間: 2025-3-30 00:18 作者: pacifist 時(shí)間: 2025-3-30 04:13
Towards a Generic Bicubic Hermite Mesh Template for Cow Udders,ed correspondences occur due to data point occlusion and insufficient sampling points. In summary, a first parametric mesh based 3D model has been constructed for the cow udder and teat. We have examined the efficacy of the morphing algorithm, and also the issues to be solved for a statistical cow udder and teat model.作者: 擔(dān)心 時(shí)間: 2025-3-30 11:22 作者: 費(fèi)解 時(shí)間: 2025-3-30 16:24 作者: 特征 時(shí)間: 2025-3-30 20:04 作者: Common-Migraine 時(shí)間: 2025-3-31 00:37 作者: 膽小懦夫 時(shí)間: 2025-3-31 02:59
Tree Leaves Detection Based on Deep Learning,s, namely, Faster R-CNN and YOLOv5, representing two-stage and one-stage algorithms, are employed to conduct tree leaves detection. Our results show that YOLOv5 model obviously outperforms to the Faster R-CNN in the speed of both model training and object detection. The difference between these two 作者: FLUSH 時(shí)間: 2025-3-31 05:21
Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and motivated researchers to design automatic diagnostic systems. Image segmentation is one of the crucial and challenging steps in the design of a computer-aided diagnosis system owing to the presence of low contrast between skin lesion and background, noise artifacts, color variations, and irregular 作者: Respond 時(shí)間: 2025-3-31 10:01 作者: 是突襲 時(shí)間: 2025-3-31 13:53 作者: 發(fā)牢騷 時(shí)間: 2025-3-31 18:47
Traffic Sign Recognition Using Guided Image Filtering,missed detection or incorrect positioning. In this paper, we propose a traffic sign recognition algorithm based on Faster R-CNN and YOLOv5. Firstly, we conduct image preprocessing by using guided image filtering for the input image to remove noises. The processed images are imported into the neural 作者: 得罪 時(shí)間: 2025-3-31 21:39
Towards a Generic Bicubic Hermite Mesh Template for Cow Udders,y capture its complexity. In this work we propose a parametric cubic Hermite (CH) based mesh to model the shape of cow udders and teats. The workflow starts from selecting a subset of nodes from the data cloud captured by a depth scanner, and constructing a CH mesh from the nodes. Using a coherent p作者: deceive 時(shí)間: 2025-4-1 05:15 作者: 帶子 時(shí)間: 2025-4-1 07:52
New Zealand Shellfish Detection, Recognition and Counting: A Deep Learning Approach on Mobile Devicand has stringent rules to control fishing and to protect the continued growth of marine inhabitants. Fishing inspections, such as identifying and counting shellfish, are part of the daily routine of many New Zealand Fisheries officers. It is however considered labour-intensive and time-consuming wo
