Computational model of MST neuron receptive field and interaction effect for the perception of self-motion

Biologically plausible approach is an alternative to conventional engineering approaches when developing algorithms for intelligent systems. It is apparent that biologically inspired algorithms may yield more expensive calculations when comparing its run time to the more commonly used engineering algorithms. However, biologically inspired approaches have great potential in generating better and more accurate outputs as healthy human brains. Therefore more and more new and exciting researches are being experimented everyday in hope to develop better models of our brain that can be utilized by the machines. This thesis work is an effort to design and implement a computational model of neurons from the visual cortex\u27s MST area (medial superior temporal area). MST\u27s primary responsibility is detecting self-motion from optic flow stimulus that are segmented from the visual input. The computational models are to be built with dual Gaussian functions and genetic algorithm as its principle training method, from the data collected through lab monkey\u27s MST neurons. The resulting computational models can be used in further researches as part of motion detection mechanism by machine vision applications, which may prove to be an effective alternative motion detection algorithm in contrast to the conventional computer vision algorithms such as frame differencing. This thesis work will also explore the interaction effect that has been discovered from the newly gathered data, provided by University of Rochester Medical Center, Neurology Department

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label