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Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

Alberto de Santos Sierra, Carmen Sánchez Ávila, Javier Guerra Casanova, Gonzalo Bailador del Pozo
2011 Sensors  
The results 7 highlight that the proposed method outperforms current competitive segmentation methods 8 with regard to computational cost, time performance, accuracy and memory usage. 9  ...  This paper presents an image segmentation algorithm based on gaussian multi-1 scale aggregation oriented to hand biometric applications.  ...  Image Segmentation by Probabilistic Bottom-Up Ag-393 gregation and Cue Integration. IEEE Conference on Computer Vision and Pattern Recognition, 394 2007.  ... 
doi:10.3390/s111211141 pmid:22247658 pmcid:PMC3251975 fatcat:qb4xdbkv3baophxhpzpqucrw2e

Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks [article]

Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou, Ruili Wang, Jie Yang
2020 arXiv   pre-print
To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation.  ...  A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them.  ...  areas with different lighting set-ups.  ... 
arXiv:2008.04021v1 fatcat:rrxxun2eqbfjbajznlgwrtvdqq

Scalable Certified Segmentation via Randomized Smoothing [article]

Marc Fischer, Maximilian Baader, Martin Vechev
2022 arXiv   pre-print
We present a new certification method for image and point cloud segmentation based on randomized smoothing.  ...  and certification guarantees on real-world segmentation tasks.  ...  For example, to achieve faster inference we scale an image down half its length and width, invoke the segmentation model, and scale up the result.  ... 
arXiv:2107.00228v2 fatcat:wtdmhhwqq5hazc4bt3bjbckufe

Fast semantic scene segmentation with conditional random field

Wen Yang, Dengxin Dai, Bill Triggs, Guisong Xia, Chu He
2010 2010 IEEE International Conference on Image Processing  
The comparison experiments on four multiclass image segmentation databases show that our approach can achieve comparable semantic segmentation results and work faster than that of the state-of-the-art  ...  First, we use the regularized logistic regression to combine different appearance-based features and the improved spatial layout of labeling information.  ...  the images to encode the unary and pairwise probabilistic preferences.  ... 
doi:10.1109/icip.2010.5652023 dblp:conf/icip/YangDTXH10 fatcat:wxolu62st5hmdpe3j2g3l6mbzy

Multi-Scale Criteria for the Evaluation of Image Segmentation Algorithms

Sylvie Philipp-Foliguet, Laurent Guigues
2008 Journal of Multimedia  
These criteria, based on an energetic formalism, take into account both the complexity of the segmented image, through the boundary length and the goodness-offit of an underlying model with the initial  ...  This paper deals with evaluation of image segmentation methods. We start with a state-of-the art of the evaluation criteria, involving a reference segmentation or not.  ...  ACKNOWLEDGMENT The authors wish to thank Ludovic Macaire and Fella Hachouf for their segmentation results respectively on the image House and the image Parrot, as well as David Picard and Jérôme Dantan  ... 
doi:10.4304/jmm.3.5.42-56 fatcat:ddwf32rolbfilph3q4ttdmczey

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth Gerstner (+56 others)
2015 IEEE Transactions on Medical Imaging  
Abstract-In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences.  ...  In this paper we report the set-up and the results of this BRATS benchmark effort.  ... 
doi:10.1109/tmi.2014.2377694 pmid:25494501 pmcid:PMC4833122 fatcat:csrnfqc4i5eilh7wk5howvpr4u

Analysis of Tracheobronchial Diverticula Based on Semantic Segmentation of CT Images via the Dual-Channel Attention Network

Maoyi Zhang, Changqing Ding, Shuli Guo
2022 Frontiers in Public Health  
TD with robust results.  ...  For efficient network training, we constructed a data set containing 218 TD and related ground truth (GT).  ...  the segmentation performance of multiple sets of medical images.  ... 
doi:10.3389/fpubh.2021.813717 pmid:35071176 pmcid:PMC8766980 fatcat:nl5qq75xnrhfzjixgfwcbxcgvy

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

Rina D. Rudyanto, Sjoerd Kerkstra, Eva M. van Rikxoort, Catalin Fetita, Pierre-Yves Brillet, Christophe Lefevre, Wenzhe Xue, Xiangjun Zhu, Jianming Liang, İlkay Öksüz, Devrim Ünay, Kamuran Kadipaşaogˇlu (+36 others)
2014 Medical Image Analysis  
analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.  ...  j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / m e d i a Segmentation Challenge new participants.  ...  Method W is a probabilistic version of method V. It uses the binary segmentation result and the vesselness map calculated by method V.  ... 
doi:10.1016/j.media.2014.07.003 pmid:25113321 pmcid:PMC5153359 fatcat:w5frt2g2rjgepjthnnciba444a

A Contrario Selection of Optimal Partitions for Image Segmentation [article]

Juan Cardelino, Vicent Caselles, Marcelo Bertalmio, Gregory Randall
2013 arXiv   pre-print
Our first goal is to extend the search space of greedy merging algorithms to the set of all partitions spanned by a certain hierarchy, and to cast the segmentation as a selection problem within this space  ...  In this way we increase the number of tested partitions and thus we potentially improve the segmentation results.  ...  Cardoso for kindly sharing and explaining the code of his algorithm to us. We also thank P. Arbelaez for sharing their benchmarks and results with us. We thank L. Guigues  ... 
arXiv:1305.1206v1 fatcat:c2spkuv45fdbfg7toyhqqcp6zu

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, Carsten Steger
2019 Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data.  ...  We propose to use a perceptual loss function based on structural similarity which examines inter-dependencies between local image regions, taking into account luminance, contrast and structural information  ...  Although both architectures remove the defect in the reconstruction, only the SSIM residual map reveals the defects and allows for an accurate segmentation result.  ... 
doi:10.5220/0007364503720380 dblp:conf/visapp/BergmannLFSS19 fatcat:wptnte5bcffvhkcolgmrigt2ca

Geodesic interactive segmentation in the color monogenic signal framework

G. Demarcq, H. Le Capitaine, M. Berthier
2012 2012 19th IEEE International Conference on Image Processing  
The user first draws some scribbles into regions that must be discriminated, and the segmentation is then automatically obtained.  ...  It results in a much more user-friendly segmentation process. Experimental results and comparisons with recent methods show the effectiveness of the approach.  ...  Results and comparisons First, we give in Figure 2 two original images with their weight maps, their D F maps. We compare our results with two recent methods.  ... 
doi:10.1109/icip.2012.6467174 dblp:conf/icip/DemarcqCB12 fatcat:togwx7tge5cm5bdwqeea7ejyjq

A Contrario Selection of Optimal Partitions for Image Segmentation

Juan Cardelino, Vicent Caselles, Marcelo Bertalmío, Gregory Randall
2013 SIAM Journal of Imaging Sciences  
Our first goal is to extend the search space of greedy merging algorithms to the set of all partitions spanned by a certain hierarchy, and to cast the segmentation as a selection problem within this space  ...  In this way we increase the number of tested partitions and thus we potentially improve the segmentation results.  ...  Cardoso for kindly sharing and explaining the code of his algorithm to us. We also thank P. Arbelaez for sharing their benchmarks and results with us. We thank L. Guigues  ... 
doi:10.1137/11086029x fatcat:mn3ltds55nbyhdaijr3isgi73q

Image Segmentation Using Deep Learning: A Survey [article]

Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, Demetri Terzopoulos
2020 arXiv   pre-print
Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality  ...  , and image compression, among many others.  ...  and suggestions.  ... 
arXiv:2001.05566v5 fatcat:wiep26nijncwxjojxbzrqoonti

RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation [article]

Tariq M Khan, Antonio Robles-Kelly, Syed S. Naqvi
2021 arXiv   pre-print
We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity.  ...  Two publicly available retinal vessel segmentation datasets were used in our experiments.  ...  Cai, “Multiscale network followed network model for retinal vessel segmentation,” in Medical Image Computing and Computer Assisted Intervention, 2018, pp. 119– 126. [35] O.  ... 
arXiv:2112.11078v1 fatcat:2egojswg25eqxl5dkngs7mqk5q

Spinal cord segmentation and classification of degenerative disease

Arjun, Kanchana V
2019 International Journal of Research in Pharmaceutical Sciences  
In our work first, we are preprocessing the MRI image and locate the degenerative part of the spinal cord, finding the degenerative part using various segmentation approach after that classifying degenerative  ...  In our work, we are using digital image processing technique, the interior part of the human body can be analyzed using MRI, CT and X-RAY etc.  ...  She has helped to ind a dataset of spinal cord MRI image. She is the dental doctor of mother and child in Kannur. The dataset will be collected from MIMs international hospital Calicut.  ... 
doi:10.26452/ijrps.v10i3.1490 fatcat:m3ueaef6fvhdxitxxoamiaqgxy
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