Odkrivanje objektov nepravilnih oblik v naravnem okolju s pomočjo prostorsko-frekvenčne analize in elastične poravnave slik

slovenian

doktorska dela

2010

Univerza v Mariboru, Fakulteta za elektrotehniko računalništvo in informatiko

In this doctoral dissertation, we describe an innovative procedure for detecting natural objects that have irregular shapes. The procedure begins in a spatial domain in which we portray different images of a given scene. It includes some common image processing techniques such as contrast enhancement,uneven light distribution correction, noise elimination, and color segmentation applied by tresholding an image based on the known color properties of the objects we are trying to detect. The main contribution of this work is to introduce an improved object detection technique that is able to detect objects of irregular shapes both by extending texture analysis procedures to frequency domain, and by applying 3D reconstruction based on multiple-view geometry. The texture analysis was done with the help of a 2D spatial-frequency representation, for which we selected a 2D Wigner-Ville representation. With its help we were able to detect areas that have similar frequency characteristics as the described looked-for objects. In some cases we encountered partly occluded areas, or areas with a similar texture as that of an object. In these cases, we could not rely on texture-analysis alone. In order to improve the results, we collected images of the same scene captured at different viewpoints, and introduced a multiple-view geometry. The main novelty of the proposed reconstruction, then, was the way we determined corresponding ties between pixels, ties that are needed for 3D reconstruction. The reconstruction had to be both reliable and precise in order to be used in the final shape analysis. Thus, a sufficient number of corresponding points needed to be provided, which was achieved with the help of an elastic image registration that produces deformation matrices describingeach pixel and its corresponding ties. By introducing a distance measure, each corresponding pair was evaluated and only the best were selected for 3D reconstruction. The final decision whether detected regions sufficiently described regions of interest was made by considering their spatial-frequency representation as well as their 3D reconstructed shapes. In order to evaluate the efficiency of our purposed solution, we used snapshots of natural scenes depicting real fruit trees. The tests were conducted in such a way to show how fruit detection methods could be improved with the help of spatial-frequency representation and multiple-view geometry. We used four different kinds of image sets: one representing colorful objects (red) with no occlusions; the second representing colorful objects with occlusions; the third less (distinctly) colorful objects with no occlusions; and, finally, the fourth representing less colorful and partly occluded objects. The results showed that the purposed solution does in fact improve object detection techniques, achieving corresponding points accuracy of 86 %, making them more reliable in cases in which we are trying to detect objects of irregular shape in natural environments, such as fruit on a fruit tree. This, in effect, improves automatic, computer-supported inspection, as well as the ability to forecast crop yield in agriculture.

07.08.2023