標(biāo)題: Titlebook: Computer Analysis of Images and Patterns; 19th International C Nicolas Tsapatsoulis,Andreas Panayides,Mario Vento Conference proceedings 20 [打印本頁(yè)] 作者: 女孩 時(shí)間: 2025-3-21 17:58
書(shū)目名稱Computer Analysis of Images and Patterns影響因子(影響力)
書(shū)目名稱Computer Analysis of Images and Patterns影響因子(影響力)學(xué)科排名
書(shū)目名稱Computer Analysis of Images and Patterns網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Computer Analysis of Images and Patterns網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computer Analysis of Images and Patterns被引頻次
書(shū)目名稱Computer Analysis of Images and Patterns被引頻次學(xué)科排名
書(shū)目名稱Computer Analysis of Images and Patterns年度引用
書(shū)目名稱Computer Analysis of Images and Patterns年度引用學(xué)科排名
書(shū)目名稱Computer Analysis of Images and Patterns讀者反饋
書(shū)目名稱Computer Analysis of Images and Patterns讀者反饋學(xué)科排名
作者: faction 時(shí)間: 2025-3-21 21:38
Measurement and Measurement Uncertainty,earn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearch (ES). In the first stage, a coarse search based on short hash codes is performed using multi-index hashing and ES terms lookup of neighbor作者: 很是迷惑 時(shí)間: 2025-3-22 01:49
Systems Analysis for Water Technology The architecture of the Siamese network is a combination of two multi-filter multi-scale deep convolutional neural networks (MFMS DCNN). Initially, the Siamese network is trained by utilizing the image-level semantic labels of the image pairs in the dataset. The features of the image pairs are obta作者: 小母馬 時(shí)間: 2025-3-22 06:49 作者: Gesture 時(shí)間: 2025-3-22 10:08 作者: 全能 時(shí)間: 2025-3-22 14:00 作者: 全能 時(shí)間: 2025-3-22 17:58
System Boundaries and Material Balances, medical counterparts, but the task entrusted to the scanner operator is different from that of the doctor. The scanner operator’s task is to find if there are any dangerous objects in the X-ray image. The operator has to evaluate the shape and kind of material of the scanned objects within a few se作者: 雇傭兵 時(shí)間: 2025-3-22 22:10 作者: Metamorphosis 時(shí)間: 2025-3-23 02:38
Hydraulic Residence Time Distribution,nalysis of biomedical image with blood vessels highlighting, graph-shape structures, cracks detection, satellite images or remote sensing data. Multi-scale processing of line feature is essentially required for the extraction of more relevant information or line structures of heterogeneous widths. I作者: MORT 時(shí)間: 2025-3-23 06:06
System Boundaries and Material Balances,e correct articulation of such Descriptor Manifold (DM) by the camera poses is the cornerstone for precise Appearance-based Localization (AbL), which implies knowing the correspondent descriptor for any given pose of the camera in the environment. Since such correspondences are only given at sample 作者: manifestation 時(shí)間: 2025-3-23 10:23
Jennifer L. Beverly,David L. Martellicient simplified version of the Homological Spanning Forest (.) for encoding homological and homotopy-based information of binary digital images. We create one Adjacency Tree (.) for each intensity contrast in a fully parallel manner. These trees, which define a Contrast Adjacency Forest (.), are i作者: enfeeble 時(shí)間: 2025-3-23 14:56 作者: goodwill 時(shí)間: 2025-3-23 20:22 作者: Synovial-Fluid 時(shí)間: 2025-3-24 00:43 作者: CANE 時(shí)間: 2025-3-24 02:24 作者: FLORA 時(shí)間: 2025-3-24 07:16 作者: 推崇 時(shí)間: 2025-3-24 10:48
Computer Analysis of Images and Patterns978-3-030-89131-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 反話 時(shí)間: 2025-3-24 18:55
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/233449.jpg作者: padding 時(shí)間: 2025-3-24 20:56
Conference proceedings 2021ers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration..作者: Defraud 時(shí)間: 2025-3-24 23:54
https://doi.org/10.1007/978-3-540-77278-1based methods, focused on image background and pedestrian appearance, show that realism in both of them is beneficial to a different extent, depending on the kind of model (regression- or detection-based) and on pedestrian size in the images.作者: airborne 時(shí)間: 2025-3-25 04:33
https://doi.org/10.1007/978-1-4613-3560-3vel data set that resembles the watermeter digits with a focus on their deformations by bubbles. We report on promising experimental recognition results, based on a deep and recurrent network architecture performed on our data set.作者: Femine 時(shí)間: 2025-3-25 08:45
Measurement and Measurement Uncertainty,performance of . for more than 120,000 query images on about 6.9 million database images of the OpenImages data set. The results show that our approach achieves high-quality retrieval results and low search latencies.作者: 刀鋒 時(shí)間: 2025-3-25 14:21
Systems Analysis for Water Technologyce the change map for the pair of images. Experiments were carried out using two remotely sensed image datasets. The weakly supervised method proposed in this paper offers better results in comparison to both weakly supervised- and unsupervised-based state-of-the-art models and techniques.作者: 借喻 時(shí)間: 2025-3-25 17:11
Jennifer L. Beverly,David L. Martelley the information of the contours and the flat regions of the original color image, plus the relations between them. Using both the . and .-trees, this new topological representation prevents some classical drawbacks that appear when working with a single tree. An implementation in OCTAVE/MATLAB validates the correctness of our algorithm.作者: 難管 時(shí)間: 2025-3-25 21:03
: Semantic Image Similarity Search by Deep Hashing with Elasticsearchperformance of . for more than 120,000 query images on about 6.9 million database images of the OpenImages data set. The results show that our approach achieves high-quality retrieval results and low search latencies.作者: Asseverate 時(shí)間: 2025-3-26 00:39 作者: 厭食癥 時(shí)間: 2025-3-26 07:06 作者: 膠水 時(shí)間: 2025-3-26 11:33 作者: 六個(gè)才偏離 時(shí)間: 2025-3-26 15:50 作者: 使痛苦 時(shí)間: 2025-3-26 20:23
John Sessions,Pamela Overhulserreal world cases, like the one we deal in the current paper, remain impractical. In the framework of the current work, we also examine and discuss, in a critical way, the effectiveness of transfer learning for real face verification cases through specific examples.作者: 豐富 時(shí)間: 2025-3-26 22:08 作者: FER 時(shí)間: 2025-3-27 03:31 作者: AWE 時(shí)間: 2025-3-27 09:13
A Multi-scale Line Feature Detection Using Second Order Semi-Gaussian Filtersges by using their tied hand-labeled images. Finally, the experimental results and comparison of images containing different line feature widths with state-of-the-art techniques have sufficiently supported the effectiveness of our technique.作者: LINE 時(shí)間: 2025-3-27 09:40 作者: Collar 時(shí)間: 2025-3-27 17:04 作者: 割公牛膨脹 時(shí)間: 2025-3-27 19:49 作者: NAUT 時(shí)間: 2025-3-28 00:27
Systems Analysis for Water Technologyrform data imputation, choose the best classifier and tune all necessary hyper-parameters. Experiments on the MNIST data-set show the effectiveness of our solution to optimize the end-to-end multi-view classification pipeline.作者: PIZZA 時(shí)間: 2025-3-28 04:01
AMI-Class: Towards a Fully Automated Multi-view Image Classifierrform data imputation, choose the best classifier and tune all necessary hyper-parameters. Experiments on the MNIST data-set show the effectiveness of our solution to optimize the end-to-end multi-view classification pipeline.作者: inventory 時(shí)間: 2025-3-28 09:10
How Realistic Should Synthetic Images Be for Training Crowd Counting Models?based methods, focused on image background and pedestrian appearance, show that realism in both of them is beneficial to a different extent, depending on the kind of model (regression- or detection-based) and on pedestrian size in the images.作者: sebaceous-gland 時(shí)間: 2025-3-28 11:59 作者: Arresting 時(shí)間: 2025-3-28 14:36
0302-9743 ; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration..978-3-030-89130-5978-3-030-89131-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: inchoate 時(shí)間: 2025-3-28 21:41
Conference proceedings 2021Patterns, CAIP 2021, held virtually, in September 2021...?..The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature 作者: tooth-decay 時(shí)間: 2025-3-29 00:27
Measurement and Measurement Uncertainty,rate indentation vertices positions are then calculated applying further geometric processing steps. The accuracy of the model is compared to known algorithms from the literature and results are presented. The evaluation is conducted on two significant indentation image databases with 150 and 216 hi作者: 放棄 時(shí)間: 2025-3-29 06:34 作者: TEM 時(shí)間: 2025-3-29 07:56
System Boundaries and Material Balances,classes are created. As a result, local classes are combined into global ones, corresponding to the specific material. Our experiments show that the proposed method achieves performance on the same level in comparison to the standard semi-automatic lookup table based methods, but due to its ability 作者: LURE 時(shí)間: 2025-3-29 13:58 作者: 廣告 時(shí)間: 2025-3-29 16:20 作者: indignant 時(shí)間: 2025-3-29 20:13 作者: contrast-medium 時(shí)間: 2025-3-30 03:30
Nicolas Tsapatsoulis,Andreas Panayides,Mario Vento作者: FLING 時(shí)間: 2025-3-30 05:17 作者: 外面 時(shí)間: 2025-3-30 10:48 作者: 過(guò)份 時(shí)間: 2025-3-30 15:44 作者: fabricate 時(shí)間: 2025-3-30 18:20 作者: 享樂(lè)主義者 時(shí)間: 2025-3-30 22:22 作者: Ccu106 時(shí)間: 2025-3-31 04:06 作者: Recess 時(shí)間: 2025-3-31 05:50
Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learning The architecture of the Siamese network is a combination of two multi-filter multi-scale deep convolutional neural networks (MFMS DCNN). Initially, the Siamese network is trained by utilizing the image-level semantic labels of the image pairs in the dataset. The features of the image pairs are obta作者: AVANT 時(shí)間: 2025-3-31 10:24
AMI-Class: Towards a Fully Automated Multi-view Image Classifierwith a scalable AutoML library, DeepHyper. The proposed framework is able to, all at once, train a model to find a common latent representation and perform data imputation, choose the best classifier and tune all necessary hyper-parameters. Experiments on the MNIST data-set show the effectiveness of
