JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

I. Introduction

COMPUTER vision, when used in open and unstructured environments as in the inspection of crops, requires the use of algorithms prepared for such situations. These algorithms work mainly with images composed of complex objects, textures, shadows and brightness. Several segmentation algorithms proposed in literature [1], [2], [3] were designed to process images originally characterized by the above-mentioned items. Additionally, agricultural automation may take advantage of computer vision resources, which can be applied to a number of different tasks, such as inspection [4], classification of plants [5], [6], [7], estimated production [8], automated collection [9] and guidance of autonomous machines.