Raúl Enrique Sánchez-Yáñez

Circle detection on images using genetic algorithms

In this paper, we present a circle detection method based on genetic algorithms. Our genetic algorithm uses the encoding of three edge points as the chromosome of candidate circles (x,y,r) in the edge image of the scene. Fitness function evaluates if these candidate circles are really present in the edge image. Our encoding scheme reduces the search space by avoiding trying unfeasible individuals, this results in a fast circle detector. Our approach detects circles with sub-pixellic accuracy on synthetic images. Our method can also detect circles on natural images with sub-pixellic precision. Partially occluded circles can be located in both synthetic and natural images. Examples of the application of our method to the recognition of hand-drawn circles are also shown. Detection of several circles in a single image is also handled by our method.

Comparative experiment with colour texture classifiers using the CCR feature space

An approach to grey level texture analysis is extended to colour images. Three colour texture classifiers using the CCR feature space are proposed. Textural information is derived from luminance plane by means of coordinated clusters transform along with chrominance features treated separately. The classifiers differ, basically, in the use of RGB and YIQ colour spaces. The main objective of this work is to evaluate the performance of classifiers quantitatively by means of comparative experiment on a set of VisTex and OuTex colour images. The experimental results indicate that the new classifiers are fast and at least as efficient as other texture analysis techniques evaluated on the same set of images.

Automatic selection of color constancy algorithms for dark image enhancement by fuzzy rule-based reasoning

Graphical abstractDisplay Omitted HighlightsA fuzzy rule-based system operates as a selector of color constancy algorithms.The system selects among the White-Patch, Gray-World and Gray-Edge algorithms.The method attains a high rate of correct selection according to the actual scene.Two problems are addressed simultaneously: color constancy and color enhancement.The framework can be used in engineering applications, like video surveillance. This work introduces a fuzzy rule-based system operating as a selector of color constancy algorithms for the enhancement of dark images. In accordance with the actual content of an image, the system selects among three color constancy algorithms, the White-Patch, the Gray-World and the Gray-Edge. These algorithms have been considered because of their accurate remotion of the illuminant, besides showing an outstanding color enhancement on images. The design of the rule-based system is not a trivial task because several features are involved in the selection. Our proposal consists in a fuzzy system, modeling the decision process through simple rules. This approach can handle large amounts of information and is tolerant to ambiguity, while addressing the problem of dark image enhancement. The methodology consists in two main stages. Firstly, a training protocol determines the fuzzy rules, according to features computed from a subset of training images taken from the SFU Laboratory dataset. We choose carefully twelve image features for the formulation of the rules: seven color features, three texture descriptors, and two lighting-content descriptors. In the rules, the fuzzy sets are modeled using Gaussian membership functions. Secondly, experiments are carried out using Mamdani and Larsen fuzzy inferences. For a test image, a color constancy algorithm is selected according to the inference process and the rules previously defined. The results show that our method attains a high rate of correct selection of the most well-suited algorithm for the particular scene.

Path planning using genetic algorithms for mini-robotic tasks

We present a genetic algorithm-based method to optimize trajectory planning for mini-robotic tasks. Codifying a number of motion primitive parameters into computational chromosomes does this. Each trajectory is composed of a fixed number N of straight segments. We search with a genetic algorithm the length and direction parameters of the N path segments that let us to arrive a target position from the current robot position. We show design choices of the genetic operators (selection, mutation and fitness function) used in our genetic algorithm implementation. We present simulations of our method and experimentation on a mini-robotic platform is implemented.

Combining Color Constancy and Gamma Correction for Image Enhancement

In this paper, image enhancement issues are addressed by analyzing the effect of two well-known color constancy algorithms in combination with gamma correction. Those effects are studied applying the algorithms separately and in combination. Images from the Barnard dataset and from the Berkeley dataset are considered for experimental tests. The performance of the approaches is evaluated comparing the Average Power Spectrum Value of the test images and their corresponding outcomes, as a quality measure. According to the experimental results, it is observed that the application of the gamma correction after a color constancy algorithm results in an improved image quality.

Image enhancement using Bi-Histogram Equalization with adaptive sigmoid functions

Among the contrast-enhancement methods, histogram equalization is the most popular. However, its major drawback is that it over-enhances the image and shifts its mean brightness and, consequently, it creates an unnatural look. In this paper, we propose a method that overcomes this problem by splitting the image histogram into two sub-histograms, using the mean as a threshold, and replacing their cumulative distribution functions with two smooth sigmoids with their origins placed on the median of the sub-histograms. Our method has been tested on gray scale images taken from the USC-SIPI database. Experimental results have shown that the proposed method outperforms other state-of-the-art methods in terms of contrast-enhancement and brightness-preservation.

Fast texel size estimation in visual texture using homogeneity cues

In this paper, we address the texel size estimation of periodic and near-periodic texture images. Such a problem has shown to be difficult when corrupted and distorted patterns are analyzed, and the accuracy and robustness are significant. In this study, we propose the use of the homogeneity cues computed using a difference histogram. Varying the displacement vector that relates two pixels and localizing the resultant homogeneity maximum value, we can automatically determine the texel size. Experiments were carried out in order to evaluate the performance of our method. Results on artificially distorted images and on natural near-periodic images, show that the proposed approach is more accurate and robust than other state-of-the-art methods. Furthermore, the computation of homogeneity cues is not intricate nor time-consuming, and hence, it can be considered for practical applications where computation time is critical.

Comparison of perceptual color spaces for natural image segmentation tasks

Color image segmentation largely depends on the color space chosen. Furthermore, spaces that show perceptual uniformity seem to outperform others due to their emulation of the human perception of color. We evaluate three perceptual color spaces, CIELAB, CIELUV, and RLAB, in order to determine their contribution to natural image segmentation and to identify the space that obtains the best results over a test set of images. The nonperceptual color space RGB is also included for reference purposes. In order to quantify the quality of resulting segmentations, an empirical discrepancy evaluation methodology is discussed. The Berkeley Segmentation Dataset and Benchmark is used in test series, and two approaches are taken to perform the experiments: supervised pixelwise classification using reference colors, and unsupervised clustering using k-means. A majority filter is used as a postprocessing stage, in order to determine its contribution to the result. Furthermore, a comparison of elapsed times taken by the required transformations is included. The main finding of our study is that the CIELUV color space outperforms the other color spaces in both discriminatory performance and computational speed, for the average case.

Fuzzy color tracking for robotic tasks

We present in this paper how to track an object by using a color cue. Color is represented as fuzzy membership functions in the CIELab color space. Integration of the fuzzy representation and CIELab color space make our tracking system robust to illumination variability. Target initialization is done in an interactive way, user selects a color target and membership functions for each coordinates are then defined. Target search is done by examining pixel intensities over a test region in the current image using a fuzzy logic rule. We have tested our approach experimentally and our system can track a colored object at rates of about 15Hz on a Pentium computer. A visual servoing system that uses our target tracking system for feature extraction has also been developed.

Natural Image Segmentation Using the CIELab Space

In this work, we have tested a color based segmentation approach that uses the CIELab color space. We use a color reduction approach where the dominant color database is obtained from the analysis of the entire Berkeley natural image database. After experiments using single color components and all their possible combinations as the segmentation basis, we have found that ab is the best color component combination for the segmentation task. F measures and Precision Recall graphs are used as the evidence for this conclusion.