Results 31 to 40 of about 48,049 (318)

Snake based Unsupervised Texture Segmentation using Gaussian Markov Random Field Models [PDF]

, 2011
A functional for unsupervised texture segmentation is investigated in this paper. An auto-normal model based on Markov Random Fields is employed to model textures. The functional investigated here is optimized with respect to the model parameters and the
Mahmoodi, Sasan, Gunn, Steve, Steve Gunn, Sasan Mahmoodi   +3 more
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Unsupervised texture segmentation [PDF]

, 1998
A novel unsupervised multispectral texture segmentation algorithm is introduced. The textured image segmentation is based on a causal adaptive regression model prediction for detecting different types of texture segments which are present at the image. Texture segments are detected in four mutually perpendicular directions in the image lattice.
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Transductive Segmentation of Textured Meshes [PDF]

, 2010
This paper addresses the problem of segmenting a textured mesh into objects or object classes, consistently with user-supplied seeds. We view this task as transductive learning and use the flexibility of kernel-based weights to incorporate a various number of diverse features.
Chauve, Anne-Laure, Pons, Jean-Philippe, Audibert, Jean-Yves, Keriven, Renaud   +3 more
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Unsupervised Segmentation Of Texture Images [PDF]

SPIE Proceedings, 1988
Past work on unsupervised segmentation of a texture image has been based on several restrictive assumptions to reduce the difficulty of this challenging segmentation task. Typically, a fixed number of different texture regions is assumed and each region is assumed to be generated by a simple model.
MICHEL X., LEONARDI, Riccardo, GERSHO A.
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Automatic texture segmentation for content-based image retrieval application

, 2006
In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm.
Fauzi, M.F.A., Fauzi, Mohammad Faizal Ahmad, Lewis, P.H., Lewis, Paul H.   +3 more
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DTNLS: 3D Point Cloud Segmentation Based on 2D Image and 3D Point Cloud Double Texture Feature

IEEE Access
Panoramic segmentation of 3D point clouds is an essential and challenging technology for robots with 3D detection and measurement capabilities. In order to fuse the color information of 2D image pixels with the spatial position information of the 3D ...
Zhiguang Liu, Xiaoxiao Yan, Jiahui Zhao, Yong Shi, Fei Yu, Jian Zhao   +5 more
doaj   +1 more source

Segmentation precedes face categorization under suboptimal conditions

Frontiers in Psychology, 2015
Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and
Carlijn eVan Den Boomen, Carlijn eVan Den Boomen, Johannes Jacobus Fahrenfort, Johannes Jacobus Fahrenfort, Tineke M. Snijders, Tineke M. Snijders, Tineke M. Snijders, Tineke M. Snijders, Chantal eKemner, Chantal eKemner, Chantal eKemner   +10 more
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A Fully Unsupervised Texture Segmentation Algorithm

, 2003
This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm.
Fauzi, Mohammad F. A., M. F. A. Fauzi, P. H. Lewis, Mohammad F. A. Fauzi, Paul H. Lewis, Lewis, Paul H.   +5 more
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Texture Segmentation using LBP embedded Region Competition

ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2005
In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information.
Qing Xu, Jie Yang, Siyi Ding
doaj   +1 more source

Semantic segmentation of textured mosaics

EURASIP Journal on Image and Video Processing, 2023
AbstractThis paper investigates deep learning (DL)-based semantic segmentation of textured mosaics. Existing popular datasets for mosaic texture segmentation, designed prior to the DL era, have several limitations: (1) training images are single-textured and thus differ from the multi-textured test images; (2) training and test textures are typically ...
Melissa Cote, Amanda Dash, Alexandra Branzan Albu   +2 more
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