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MAPS: Multiscale Attention-Based PreSegmentation of Color Images [chapter]

Nabil Ouerhani, Heinz Hügli
2003 Lecture Notes in Computer Science  
Conclusion This work reports a novel Multiscale Attention-based PreSegmentation method (MAPS).  ...  In this paper we present a n o vel Multiscale Attention-based PreSegmentation (MAPS) method, which addresses the segmentation issues mentioned above.  ... 
doi:10.1007/3-540-44935-3_37 fatcat:n4njstalgzfybbla7ennrl3jta

Convolutional Neural Network with Multiscale Fusion and Attention Mechanism for Skin Diseases Assisted Diagnosis

Zhong Li, Hongyi Wang, Qi Han, Jingcheng Liu, Mingyang Hou, Guorong Chen, Yuan Tian, Tengfei Weng, Jianli Liu
2022 Computational Intelligence and Neuroscience  
Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention.  ...  of melanoma segmentation.  ...  [24] proposed a deep learning framework based on anatomical attention guided for brain ROI segmentation in structural MR images. Ren et al.  ... 
doi:10.1155/2022/8390997 pmid:35747726 pmcid:PMC9213118 fatcat:otrpb34u2rdnlfhlbnvvxuk7li

MVF-CNN: Fusion of Multilevel Features for Large-scale Point Cloud Classification

Yong Li, Guofeng Tong, Xingang Li, Liqiang Zhang, Hao Peng
2019 IEEE Access  
This paper proposes a deep learning-based algorithm for large-scale point cloud classification through the fusion of multiscale voxels and features (MVF-CNN).  ...  However, there are some problems in the methods, such as voxels lacking color information, single receptive fields being considered only at the same voxel scale, and only the global features of voxels  ...  COLOR INFORMATION For projection-based methods, TML-PCR [20] , TMLC-MSR [21] , Deep Projective 3D Semantic Segmentation, Snapnet, etc. project the color information of the point cloud into the images  ... 
doi:10.1109/access.2019.2908983 fatcat:dsmgb44ubrc3xgne5xgw52x5gq

A Review of Point Cloud Semantic Segmentation [article]

Yuxing Xie, Jiaojiao Tian, Xiao Xiang Zhu
2019 arXiv   pre-print
Firstly, we outline the acquisition and evolution of the 3D point cloud from the perspective of remote sensing and computer vision, as well as the published benchmarks for PCSS studies.  ...  In order to provide a needed up-to-date review of recent developments in PCSS, this article summarizes existing studies on this topic.  ...  Inspired by the idea of the attention mechanism, Wang et al. [112] designed a Graph Attention Convolution (GAC), of which kernels could be dynamically adapted to the structure of an object.  ... 
arXiv:1908.08854v2 fatcat:kwpsogl4gbd3nji4xpcx32c4ta

Research on Methods of English Text Detection and Recognition Based on Neural Network Detection Model

Chunlan Li, Bai Yuan Ding
2021 Scientific Programming  
With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information.  ...  It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text.  ...  , and pixel-level extraction. is paper draws on the multiscale network structure of inception and designs an improved English text detection algorithm based on CTPN. e algorithm uses a multiscale convolution  ... 
doi:10.1155/2021/6406856 fatcat:v3uj2gfozrgulkasy65ywhvwl4

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Lotfi Tlig, Moez Bouchouicha, Mohamed Tlig, Mounir Sayadi, Eric Moreau
2020 Sensors  
This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses.  ...  The novel approach is tested on various color images.  ...  It is the task of superpixels extraction. As mentioned above, we have introduced the multiscale image transformation based on the Gabor filtering.  ... 
doi:10.3390/s20226429 pmid:33182838 pmcid:PMC7696074 fatcat:ykzabohl5vd7dm6o7ytvulek3i

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

Talha Ilyas, Abbas Khan, Muhammad Umraiz, Hyongsuk Kim
2020 Electronics  
Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation.  ...  We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation  ...  [37] combined a neural networks based attention map with the saliency map to generate pseudo-ground truth images. Wang et al.  ... 
doi:10.3390/electronics9030383 fatcat:eml5bwnmwzdo7e2bpsfplr6iwa

Fusing LIDAR, camera and semantic information: A context-based approach for pedestrian detection

Cristiano Premebida, Urbano Nunes
2013 The international journal of robotics research  
process of image-based classifiers.  ...  a semantic map of the roads, and (iii) an image-based detection module, using sliding-window detectors, with the role of validating the presence of pedestrians in regions of interest (ROIs) generated  ...  Detection window: a image-based classifier, in the form of a multiscale sliding-window detector, is the primary stage used to identify potential pedestrians inside the ROIs. 2.  ... 
doi:10.1177/0278364912470012 fatcat:x5anwft6snhrvhgyuqumgpxcsm

Survey of contemporary trends in color image segmentation

Sreenath Rao Vantaram, Eli Saber
2012 Journal of Electronic Imaging (JEI)  
We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed.  ...  and pertinent representation of image information.  ...  Carlson Center for Imaging Science and the Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY.  ... 
doi:10.1117/1.jei.21.4.040901 fatcat:dco5abqsvzcuxi5ydktbssz6xi

Advances in Spectral-Spatial Classification of Hyperspectral Images

M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, J. C. Tilton
2013 Proceedings of the IEEE  
To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map.  ...  They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.  ...  Attention is paid to MM tools that allow the analysis of the image at the region level for the purpose of classification.  ... 
doi:10.1109/jproc.2012.2197589 fatcat:ty2k66i45fa2hnpwpl336lchlu

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Adam Hilbert, Vince I. Madai, Ela M. Akay, Orhun U. Aydin, Jonas Behland, Jan Sobesky, Ivana Galinovic, Ahmed A. Khalil, Abdel A. Taha, Jens Wuerfel, Petr Dusek, Thoralf Niendorf (+3 others)
2020 Frontiers in Artificial Intelligence  
Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries.  ...  Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases.  ...  For 1000Plus data, the MeVisLab-based presegmentation step was replaced by using a 2D Unet segmentation model, developed in earlier work (Livne et al., 2019a) .  ... 
doi:10.3389/frai.2020.552258 pmid:33733207 pmcid:PMC7861225 fatcat:x3wnuik3lnbwhaphwt37mic7ja

Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging [article]

Samet Akcay, Toby Breckon
2021 arXiv   pre-print
The first part briefly discusses the classical machine learning approaches utilised within X-ray security imaging, while the latter part thoroughly investigates the use of modern deep learning algorithms  ...  Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery.  ...  Durham Baggage (DB) Patch/Full Image Dataset This dataset comprises 15, 449 X-ray samples with associated false color materials mapping from dual-energy four-view Smiths 6040i machine.  ... 
arXiv:2001.01293v2 fatcat:qsb2zg33tbevldqljgycmxnhf4

Multi-information Spatial–temporal LSTM Fusion Continuous Sign Language Neural Machine Translation

Qinkun Xiao, Xin Chang, Xue Zhang, Xing Liu
2020 IEEE Access  
ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Nos. 60972095, 61271362, 61671362,62071366) and the Natural Science Basic Research Plan of Shaanxi Province  ...  [19] used color, depth data, and custom-made gesture descriptors to represent sign language features and then established a multiscale deep structure for sign language recognition.  ...  and attention-based machine translation.  ... 
doi:10.1109/access.2020.3039539 fatcat:dcxkodn3vbaavdiixcko5zooyy

A Study on the Optimization Simulation of Big Data Video Image Keyframes in Motion Models

Jianbang Guo, Peng Sun, Sang-Bing Tsai, Chao-Yang Lee
2022 Wireless Communications and Mobile Computing  
The sports video image-processing technology realizes the rapid extraction of key technical parameters of the sports scene, the panoramic map technology of sports video images, the split-lane calibration  ...  For the feature extraction problem of fuzzy videos, this paper proposes a fuzzy kernel extraction scheme based on the low-rank theory.  ...  Video behavior recognition algorithms based on local features of motion behavior do not require presegmentation processing of video images.  ... 
doi:10.1155/2022/2508174 fatcat:u2yl56lllrhdpasphzjvrnnkoq

Object-level image segmentation with prior information [article]

Chunlai Wang, Universität Stuttgart, Universität Stuttgart
-Ing Eckehard Steinbach, who owns the Chair of Media Technology, TU Munich.  ...  Jörg Roth-Stielow (president) in my doctoral committee for taking time out of their busy schedules. I am grateful to the colleges at ISS.  ...  Given a well-trained model as a mapping function Ψ : R H×W×3 → R H×W×C , which maps color images to segmentation maps with C object classes. It is typically a deep CNN [106, 132, 9, 175] .  ... 
doi:10.18419/opus-10545 fatcat:n47jchwgvredlcj3a3ijse5bbm
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