Goal Directed Visual Search Based on Color Cues: Co-operative Effectes of Top-Down & Bottom-Up Visual Attention

Focus of Attention plays an important role in perception of the visual environment. Certain objects stand out in the scene irrespective of observers\u27 goals. This form of attention capture, in which stimulus feature saliency captures our attention, is of a bottom-up nature. Often prior knowledge about objects and scenes can influence our attention. This form of attention capture, which is influenced by higher level knowledge about the objects, is called top-down attention. Top-down attention acts as a feedback mechanism for the feed-forward bottom-up attention. Visual search is a result of a combined effort of the top-down (cognitive cue) system and bottom-up (low level feature saliency) system. In my thesis I investigate the process of goal directed visual search based on color cue, which is a process of searching for objects of a certain color. The computational model generates saliency maps that predict the locations of interest during a visual search. Comparison between the model-generated saliency maps and the results of psychophysical human eye -tracking experiments was conducted. The analysis provides a measure of how well the human eye movements correspond with the predicted locations of the saliency maps. Eye tracking equipment in the Visual Perceptual Laboratory in the Center for Imaging Science was used to conduct the experiments

Detection of inconsistent regions in video streams

Humans have a general understanding about their environment. We possess a sense of distinction between what is consistent and inconsistent about the environment based on our prior experience. Any aspect of the scene that does not fit into this definition of normalcy tends to be classified as a novel event. An example of this is a casual observer standing over a bridge on a freeway, tracking vehicle traffic, where the vehicles traveling at or around the same speed limit are generally ignored and a vehicle traveling at a much higher (or lower) speed is subject to one’s immediate attention. In this paper, we extend a computational learning based framework for detecting inconsistent regions in a video sequence detection. The framework extracts low-level features from scenes, based on the focus of attention theory and combines unsupervised learning with habituation theory for learning these features. The paper presents results from our experiments on natural video streams for identifying inconsistency in velocity of moving objects and also the static changes in the scene