P. Javier Herrera

A segmentation method using Otsu and fuzzy k-Means for stereovision matching in hemispherical images from forest environments

In this paper we describe a novel pixel-based strategy of segmentation and stereovision matching for obtaining disparity maps from hemispherical images captured with fish-eye lenses from forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded and extracts six attributes of each pixel as features. This is achieved by applying both Otsu and fuzzy k-Means methods. It is a combination of strategies appropriately sequenced to automate the process and facilitate the matching. At a second stage, a stereovision matching process is designed based on the application of three stereovision matching constraints: epipolar, similarity, and uniqueness. The epipolar guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a majority voting criterion. The main finding of this paper is the combination of strategies in the both stages. The method is compared against the usage of simple features and some existing similarity matching strategies using also combination.

Combination of Attributes in Stereovision Matching for Fish-Eye Lenses in Forest Analysis

This paper describes a novel stereovision matching approach by combining several attributes at the pixel level for omni-directional images obtained with fish-eye lenses in forest environments. The goal is to obtain a disparity map as a previous step for determining distances to the trees and then the volume of wood in the imaged area. The interest is focused on the trunks of the trees. Because of the irregular distribution of the trunks, the most suitable features are the pixels. A set of six attributes is used for establishing the matching between the pixels in both images of the stereo pair. The final decision about the matched pixels is taken by combining the attributes. Two combined strategies are proposed: the Sugeno Fuzzy Integral and the Dempster-Shafer theory. The combined strategies, applied to our specific stereo vision matching problem, make the main finding of the paper. In both, the combination is based on the application of three well known matching constraints. The proposed approaches are compared among them and favourably against the usage of simple features.

Fuzzy Cognitive Maps Applied to Computer Vision Tasks

Computer vision is an emerging area which is demanding solutions for solving different problems. The data to be processed are bi-dimensional (2D) images captured from the tri-dimensional (3D) scene. The objects in 3D are generally composed of related parts that joined form the whole object. Fortunately, the relations in 3D are preserved in 2D. Hence, we can exploit this fact by considering specific and basic elements which are related to other elements in the 2D images. The relations with other elements allow establishing a link among them. Hence, we have the necessary ingredients to build a structure under the Fuzzy Cognitive Maps (FCMs) paradigm. FCMs have been satisfactorily used in several areas of computer vision including: pattern recognition, image change detection or stereo vision matching. In this chapter we establish the general framework of fuzzy cognitive maps in the context of 2D images and describe three applications in the three mentioned areas of computer vision. We also give some details about the performance of this paradigm in these applications.

Combining Support Vector Machines and simulated annealing for stereovision matching with fish eye lenses in forest environments

We present a novel strategy for computing disparity maps from omni-directional stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. Two of them are identified by applying the powerful Support Vector Machines approach. At a second stage, a stereovision matching process is designed based on the application of four stereovision matching constraints: epipolarity, similarity, uniqueness and smoothness. The epipolarity guides the process. The similarity and uniqueness are mapped once again through the Support Vector Machines, but under a different way to the previous case; after this an initial disparity map is obtained. This map is later filtered by applying the Discrete Simulated Annealing framework where the smoothness constraint is conveniently mapped. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies.

Choquet Fuzzy Integral Applied to Stereovision Matching for Fish-Eye Lenses in Forest Analysis

This paper describes a novel stereovision matching approach based on omni-directional images obtained with fish-eye lenses in forest environments. The goal is to obtain a disparity map as a previous step for determining the volume of wood in the imaged area. The interest is focused on the trunks of the trees, due to the irregular distribution of the trunks; the most suitable features are the pixels. A set of six attributes is used for establishing the matching between the pixels in both images of the stereo pair. The final decision about the matched pixel is taken based on the Choquet Fuzzy Integral paradigm, which is a technique well tested for combining classifiers. The use and adjusting of this decision approach to our specific stereo vision matching problem makes the main finding of the paper. The procedure is based on the application of three well known matching constraints. The proposed approach is compared favourably against the usage of simple features and other fuzzy strategy that combines the simple ones.

New unsupervised hybrid classifier based on the fuzzy integral: applied to natural textured images

This study presents a new unsupervised hybrid classifier for natural texture identification in aerial images. The proposed strategy combines through the fuzzy integral (FI) six well-tested base supervised classifiers. This automation is based on the generation of a general rule inferred through decision tree learning, ID3 strategy from the training data. This rule allows generation of a partition of the set of images that the base classifiers use to estimate automatically their parameters. These parameters are the inputs to calculate the relative importance of each classifier in their combination by the FI. The resulting classifier has been compared with related techniques getting an improvement of 8.04% average. The study includes discussion on this comparison.

Combining Stereovision Matching Constraints for Solving the Correspondence Problem

A major portion of the research efforts of the computer vision community has been directed toward the study of the three-dimensional (3-D) structure of objects using machine analysis of images (Scharstein & Szeliski, 2002). We can view the problem of stereo analysis as consisting of the following steps: image acquisition, camera modelling, feature acquisition, image matching, depth determination and interpolation. The key step is that of image matching, that is, the process of identifying the corresponding points in two images that are cast by the same physical point in 3-D space (Barnard & Fishler, 1982). This chapter is devoted solely to this problem. A correspondence needs to be established between features from two images that correspond to some physical feature in space. Then, provided that the position of centres of projection, the focal length, the orientation of the optical axes, and the sampling interval of each camera are known, the depth can be established by triangulation. The stereo correspondence problem can be defined in terms of finding pairs of true matches, namely, pairs of features in two images that are generated by the same physical entity in space. These true matches generally satisfy some constraints (Tang et al., 2002): 1. Epipolar, given two features, one in an image and a second in the other one in the stereoscopic pair, if we follow a given line, established by the system geometry, these two features must lie on this line, which is the epipolar. 2. Similarity, matched features have similar local properties or attributes. 3. Smoothness, disparity values in a given neighbourhood change smoothly, except at a few depth discontinuities. 4. Ordering, the relative position among two features in an image is preserved in the other one for the corresponding matches. 5. Uniqueness, each feature in one image should be matched to a unique feature in the other image. A review of the state-of-art in stereovision matching allows us to distinguish two sorts of techniques broadly used in this discipline: area-based and feature-based. Area-based stereo techniques use correlation between brightness (intensity) patterns in the local neighbourhood of a pixel in one image with brightness patterns in the local neighbourhood of the other image (Scharstein & Szeliski, 2002; Herrera et al., 2009a,b,c; Herrera, 2010; Klaus et al., 2006). Feature-based methods use sets of pixels with similar attributes, normally, either pixels belonging to edges (Grimson, 1985; Ruichek & Postaire, 1996; Tang et al., 2002),

On Combining Classifiers by Relaxation for Natural Textures in Images

One objective for classifying textures in natural images is to achieve the best performance possible. As reported in the literature, the combination of classifiers performs better than simple ones. The problem is how they can be combined. We propose a relaxation approach, which combines two base classifiers, namely: the probabilistic Bayesian and the fuzzy clustering. The first establishes an initial classification, where the probability values are reinforced or punished by relaxation based on the support provided by the second. A comparative analysis is carried out against classical classifiers, verifying its performance.

A combined strategy using FMCDM for textures segmentation in hemispherical images from forest environments

The research undertaken in this work comprises the design of a segmentation strategy to solve the stereoscopic correspondence problem for a specific kind of hemispherical images from forest environments. Images are obtained through an optical system based on fisheye lens. The aim consists on the identification of the textures belonging to tree trunks. This is carried out through a segmentation process which uses the combination of five single classical classifiers using the Multi-Criteria Decision Making method under Fuzzy logic paradigm. The combined proposal formulated in this research work is of unsupervised nature and can be applied to any type of forest environment, with the appropriate adaptations inherent to the segmentation process in accordance with the nature of the forest environment analyzed.

Performance analysis of fuzzy aggregation operations for combining classifiers for natural textures in images

One objective for classifying pixels belonging to specific textures in natural images is to achieve the best performance in classification as possible. We propose a new unsupervised hybrid classifier. The base classifiers for hybridization are the Fuzzy Clustering and the parametric Bayesian, both supervised and selected by their well-tested performance, as reported in the literature. During the training phase we estimate the parameters of each classifier. During the decision phase we apply fuzzy aggregation operators for making the hybridization. The design of the unsupervised classifier from supervised base classifiers and the automatic computation of the final decision with fuzzy aggregation operations, make the main contributions of this paper.