Eye Detection using Helmholtz Principle

Eye Detection is used in many applications like pattern recognition, biometric, surveillance system and many other systems. In this paper, a new method is presented to detect and extract the overall shape of one eye from image depending on two principles Helmholtz & Gestalt. According to the principle of perception by Helmholz, any observed geometric shape is perceptually "meaningful" if its repetition number is very small in image with random distribution. To achieve this goal, Gestalt Principle states that humans see things either through grouping its similar elements or recognize patterns. In general, according to Gestalt Principle, humans see things through general description of these things. This paper utilizes these two principles to recognize and extract eye part from image. Java programming language and OpenCV library for image processing are used for this purpose. Good results are obtained from this proposed method, where 88.89% was obtained as a detection rate taking into account that the average execution time is about 0.23 in seconds.

The Effect of Contour Closure on Shape Recognition

Recent research on the Gestalt principle of closure has focused on how the presence of closure affects the ability to detect contours hidden in cluttered visual arrays. Some of the earliest research on closure, however, dealt with encoding and recognizing closed and open shapes, rather than detection. This research re-addresses the relation between closure and shape memory, focusing on how contour closure affects the ability to learn to recognize novel contour shapes. Of particular interest is whether closed contour shapes are easier to learn to recognize and, if so, whether this benefit is due to better encoding of closed contour shapes or easier comparison of closed contour shapes to already learned shapes. The results show that closed contours are indeed easier to recognize and, further, that this advantage appears to be related to better encoding.