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Abstract: Image segmentation is one of the fundamental and important steps that is needed to prepare an image for further processing in many computer vision applications. Over the last few decades, many image segmentation methods have been proposed, as accurate image segmentation is vitally important for many image, video and computer vision applications. A common approach is to look at the grey level colours of the image to perform multi-level-thresholding. The ability to quantify and compare the resulting segmented images is of vital importance even though it can be a major challenge. One measure used here computes the total distances of the pixels to its centroid for each region to provide a quantifiable measure of the segmented images. We also suggest an improved Zhang''s entropy measure for image segmentation based on computing the entropy of the image and segmented regions. In this paper, we will present the results from both of these approaches. [Copyright &y& Elsevier]

Automated Teller Machine (ATM) offers great convenience to many people by allowing quick bank transactions and cash withdrawal. However, ATM machines are also vulnerable to attacks when they are unattended during non-office hours and public holidays. Recently, many ATM machines were reported being removed from the premises or damaged using various methods in order to steal the cash inside. Due to this reason, many ATM sites are actually equipped with video surveillance systems to monitor the environment for crime prevention. However, it is difficult for security personnel to pin-point the crime scene in real time when the number of surveillance cameras increases. In this paper, a real time security expert video surveillance system was proposed to detect the suspicious behaviour by utilizing image processing techniques. The proposed expert system, hereafter referred to as ArchCam, is capable in detecting suspicious behaviours that attempt to remove or attack the ATM machines and provide early warning to the centralized video surveillance system. The suspicious behaviour that ArchCam detects include squatting/climbing (attempt to remove security alarm system or place a bomb) and carrying “belt shape” object (attempt to remove the ATM). The squatting/climbing activity is detected through novel technique to estimate the height of the moving object(s) in front of ATM. On the other hand, the “belt shape” object is detected through estimation of object width by using region splitting and merging technique. With the intelligence of detecting suspicious behaviour, the proposed expert system can effectively alert the security personnel to take proactive actions before the cash is being robbed from the ATM machines. This greatly reduces the effort for security personnel as they only need to observe the camera videos with suspicious behaviour, which on the other hand help to improve the possibility of detecting actual crime scene in real time. ArchCam was implemented in an embedded system with GPU platform and has been verified in a simulated ATM setup with good detection accuracy and fast computational timing performance. [ABSTRACT FROM AUTHOR]