IMPROVED MORPHOLOGICAL BACKGROUND DETECTION AND ENHANCEMENT OF IMAGES BASED ON WEBER'S LAW

摘要

Weber's law suggests a logarithmic relationship between perceptual stimuli and human perception. The Weber sampler is an adaptive, non uniform sampling mechanism that exploits Weber's law to sample the signal at a minimum rate without significant perceptual degradation. In this paper, we introduce Morphological transformation and Block analysis is used to detect the background of gray level and color images. These techniques are first implemented in gray scale and then extended to color images by individually enhancing the color components. Some morphological transformations are used to detect the background in color images characterized by poor contrast. Contrast operators are based on the logarithm function in a similar way to Weber's law. The use of the logarithm function avoids abrupt changes in lighting. The first method employs information from image background analysis by blocks, while the second transformation method utilizes the opening operation, closing operation, which is employed to define the multi-background color images. The results of each technique are illustrated for various backgrounds, majority of them in poor lighting condition. The complete image processing is done using JAVA simulation model.