Unsupervised segmentation of color-texture regions in images and video

2001 ◽  
Vol 23 (8) ◽  
pp. 800-810 ◽  
Author(s):  
Y. Deng ◽  
B.S. Manjunath
Keyword(s):  
Unsupervised Segmentation ◽  
Color Texture

scholarly journals Unsupervised color texture segmentation based on multi-scale region-level Markov random field models

2019 ◽  
Vol 43 (2) ◽  
pp. 264-269
Author(s):  
X. Song ◽  
L. Wu ◽  
G. Liu
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Texture Segmentation ◽  
Unsupervised Segmentation ◽  
Spatial Constraints ◽  
Multi Scale ◽  
Markov Random ◽  
Single Scale ◽  
Proposed Model ◽  
Color Texture

In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.

scholarly journals T1K+: A Database for Benchmarking Color Texture Classification and Retrieval Methods

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1010
Author(s):  
Claudio Cusano ◽  
Paolo Napoletano ◽  
Raimondo Schettini
Keyword(s):  
Design Of Experiments ◽  
Texture Classification ◽  
Construction Materials ◽  
Heterogeneous Database ◽  
High Quality ◽  
Visual Descriptors ◽  
Classification Tasks ◽  
Color Texture

In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials, etc. T1K+ allows the design of experiments especially aimed at understanding the specific issues related to texture classification and retrieval. To help the exploration of the database, all the 1129 classes are hierarchically organized in 5 thematic categories and 266 sub-categories. To complete our study, we present an evaluation of hand-crafted and learned visual descriptors in supervised texture classification tasks.

scholarly journals Efficient and Consumer-Centered Item Detection and Classification with a Multicamera Network at High Ranges

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4818
Author(s):  
Nils Mandischer ◽  
Tobias Huhn ◽  
Mathias Hüsing ◽  
Burkhard Corves
Keyword(s):  
Machine Learning ◽  
Shop Floor ◽  
Unsupervised Segmentation ◽  
User Friendliness ◽  
Industrial Environment ◽  
Machine Learning Methods ◽  
Eu Project ◽  
Detection And Localization ◽  
Industrial Context ◽  
The Eu

In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In this work, we present the methods used for detecting and classifying items on the shop floor. Important in the context of SHAREWORK is the user-friendliness of the methodology. Thus, we renounce heavy-learning-based methods in favor of unsupervised segmentation coupled with lenient machine learning methods for classification. Our algorithm is a combination of established methods adjusted for fast and reliable item detection at high ranges of up to eight meters. In this work, we present the full pipeline from calibration, over segmentation to item classification in the industrial context. The pipeline is validated on a shop floor of 40 sqm and with up to nine different items and assemblies, reaching a mean accuracy of 84% at 0.85 Hz.

Color texture synthesis based on structure

2003 ◽  
Author(s):  
Peng Zhang ◽  
Silong Peng
Keyword(s):  
Texture Synthesis ◽  
Color Texture
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