02574nam a2200253 a 4500 vtls000037934 KUKTEM 20251114204418.0 090615t2008 my a f m 000 0 eng|d THE0005496(Local) 201905160940 hanafiah 201107132243 VLOAD 200908141631 VLOAD 200908141605 VLOAD 200906151655 ida84 UMP QA76.87 .P74 2008 rs Bc. Prema Latha Subramaniam Moving detection using cellular neural network (CNN) / Prema Latha Subramaniam Moving detection using cellular neural network (CNN) [electronic resource] Kuantan, Pahang : UMP, 2008 86 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Electrical Engineering (Control and Instrumentation)) -- Universiti Malaysia Pahang - 2008 Detecting moving objects is a key component of an automatic visual surveillance and tracking system. Previous motion-based moving object detection approaches often use background subtraction and inter-frame difference or three-frame difference, which are complicated and takes long time. In this paper, we proposed a simple and fast method to detect a moving object using Cellular Neural Network. The main idea in Cellular Neural Network is that connection is allowed between adjacent units only. This paper comprises the implementation of the basic templates available in Cellular Neural Network. The templates are programmed in MATLAB. There are few rules in Cellular Neural Network that has to be implemented when programming the templates, such as the state equation, output equation, boundary condition and also the initial value. These templates are combined to create the most ideal algorithm to detect a moving object in an image. A video of a bouncing ball is recorded using a static camera. The video then are segmented into images using SC Video Developer. Ten images are selected to be used in this project. The algorithm created is used to detect the ball in the images. This paper also includes the use of Image Processing Toolbox in MATLAB. An analysis is conducted by comparing the ball’s position in each image according to the time. This analysis indicates whether the object has shifted position or moved in the images. The efficiency of the result for this paper is 85%. Image processing Neural networks (Computer science) 0 0 0 1 20000 20000 2019-09-04 0 CD 3261 | QA76.87 .P74 2008 rs Bc. 0000037395 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 QA76.87 .P74 2008 rs Bc. 0000037394 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 1609 1615 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992