Robust scene recognition using a GA and real-world raw-image

This paper presents a new concept of scene recognition by a genetic algorithm (GA), using the 2-D gray-scale image of a working space, termed here as raw-image, and a model shaping the 2-D top-surface of a target object. In fact here, the problem of object recognition in the raw-image is changed into an optimization problem of a model-based evaluation function. We make use in this research of a GA, as a search and optimization method. This GA employs a model-based fitness function as its objective function to perform the search of a target in the raw-image. In this research, three object models, namely a frame model, a surface model, and a surface-strips model are investigated in order to determine which one is the best for scene recognition in a noisy environment. Also, in order to appraise the recognition performance of each model, a comparative study is performed by analyzing the answers to the following criteria questions: sensitivity, reliability, and speed. The effectiveness of the method has been verified through experiments using real-world raw-images, and the method has shown its robustness of object recognition with the surface-strips model, in spite of the noises in the scene.