Color Constancy and Image Segmentation Techniques for Applications to Mobile Robotics

Resumen

This Thesis endeavors providing a set of techniques for facing the problem of color variation in images taken from a mobile platform and caused by the change in the conditions of lighting among several views of a certain scene taken at different instants and positions. It also treats the problem of segmenting color images in order to use them in tasks associated with the capacities of a mobile robot, such as object identification or image retrieval from a large database. In order to carry out these goals, first transformation among colors due to light variations is mathematically established. Thus, a continuous model for the generation of color is proposed as a natural generalization of other former models. In this way, conditions for the existence, uniqueness, and good behavior of the solutions can be mathematically studied with a great generality, and any type of applications among colors can be expressed independently of the discretization scheme applied. Thus, the intimate relation among the problem of color invariance and that of spectral recovery is made evident and studied in practice too. The developed model is numerically contrasted with those of a least squares linear regression in terms of prediction errors. Once the general model is established, a simplified linear version is chosen instead for carrying out the practical calculations while lightening the number of them. In particular, the proposed method is based on finding the likeliest transformation between two images from the calculation of a set of feasible transformations and the estimation of the frequency and the effectiveness degree of each of them. Later, the best candidate is selected in accordance with its likelihood. The resulting application is then able to transform the image colors as they would be seen under the canonical light. After keeping the image colors from a scene constant, it is necessary to proceed to their segmentation to extract information corresponding to regions with homogeneous colors. In this Thesis, an algorithm based on the partition of the minimum spanning tree of an image through a local measure of the likelihood of the unions among components is suggested. The idea is to arrive at a segmentation coherent with the real regions, a trade-off between partitions with many component (oversegmented) and those with fewer components (subsegmented). Another goal is that of obtaining an algorithm fast enough to be useful in applications of mobile robotics. This characteristic is attained by a local approach to region growing, even though the result still shows global feature (color). The possible oversegmentation is softened thanks to a probabilistic factor. The segmentation algorithm should also generate stable segmentations through time. Thus, the aforementioned algorithm has been widened by including an intermediate step that allows to relate similar regions in different images and to propagate forwards the regrouping of regions made in previous images. This way, if in some image some regions are grouped forming only one bigger region, the corresponding regions in the following image will also be grouped together. In this way, two correlatives segmentations resemble each other, keeping the whole segmented sequence stabler. Finally, the problem of comparing images via their content is also studied in this Thesis, focusing on the color information and, besides investigating which is for our aims the best distance between segmentation, also showing how color constancy affects segmentations. The results obtained in each of the goals proposed in this Thesis guarantee the exposed points of view, and show the utility of the algorithms suggested, as well as the color model for the spectral recovery and the explicit calculation of the transformations among colors.