<title>Image partition boundary coding</title>

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

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Image Processing Applications Based on Texture and Fractal Analysis

Applied Signal and Image Processing ◽

10.4018/978-1-60960-477-6.ch014 ◽

2011 ◽

pp. 226-250 ◽

Cited By ~ 6

Author(s):

Radu Dobrescu ◽

Dan Popescu

Keyword(s):

Fractal Dimension ◽

Texture Classification ◽

Malignant Tumour ◽

Classification Performance ◽

Textured Surfaces ◽

Box Counting ◽

Image Partition ◽

Fractal Analyses ◽

Occurrence Matrix

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

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A region-level motion-based graph representation and labeling for tracking a spatial image partition

Lecture Notes in Computer Science - Energy Minimization Methods in Computer Vision and Pattern Recognition ◽

10.1007/3-540-62909-2_94 ◽

1997 ◽

pp. 409-424 ◽

Cited By ~ 2

Author(s):

Marc Gelgon ◽

Patrick Bouthemy

Keyword(s):

Graph Representation ◽

Spatial Image ◽

Image Partition

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GM-VPC: An Algorithm for Multi-robot Coverage of Known Spaces Using Generalized Voronoi Partition

Robotica ◽

10.1017/s0263574719001127 ◽

2019 ◽

Vol 38 (5) ◽

pp. 845-860 ◽

Cited By ~ 2

Author(s):

Vishnu G. Nair ◽

K. R. Guruprasad

Keyword(s):

Geodesic Distance ◽

Manhattan Distance ◽

Simulation Experiments ◽

Cellular Decomposition ◽

Coverage Path Planning ◽

Coverage Gap ◽

Voronoi Partition ◽

Tree Coverage ◽

Grid Level ◽

Partition Boundary

SUMMARYIn this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2D×2D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.

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