Z language based an algorithm for event detection, analysis and classification in machine vision

A common task in various machine learning (ML) application areas involves observing regularly gathered data for `interesting´ events. This mission is predominant in reconnaissance, but also in responsibilities fluctuating from the investigation of scientific data to the observing of unsurprisingly happening events, and from controlling engineering procedures to noticing human behavior. We will refer to this observing procedure with the determination of classifying remarkable manifestations, as event detection, analysis and classification. With the appearance of personal computers (PCs) a lot of efforts have been made to substitute manual investigation by a computerized manner. Data, nevertheless, have become gradually difficult, and the sizes of gathered data have become enormously bulky in latest years. Text documents, JPEG images, MP3, videos and even relational data are now regularly gathered. In this paper, we propose an algorithm for event detection, analysis and classification in machine vision. Till proposed algorithm is deliberated a significant facility, required degree of event detection cannot be achieved. Finally, we use K-means algorithm for classification of incoming events and proposed algorithm has been validated by Z Formal specification language in general. The proposed algorithm has been implemented in Matlab and results have been gathered through a data mining tool. Using the proposed algorithm, the events are easily detected, analyzed and classified in machine vision.