Oriental Journal of Computer Science and Technology

Jalal H. Awad ¹, Amir S. Almallah ¹

Affiliations
1 Department of Computer Science, College of Science, University of Mustansiriyah (UOM), IQ

Abstract

Moving object detection has been widely used in diverse discipline such as intelligent transportation systems, airport security systems, video monitoring systems, and so on. In this paper we proposed an edge segment based statistical background modeling algorithm, which can be implemented for moving edge detection in video surveillance system using static camera. The proposed method is an edge segment based, so it can help to exceed some of the difficulties that face traditional pixel based methods in updating background model or bringing out ghosts while a sudden change occurs in the background.As an edge segment based method it is robust to illumination variation and noise, it is also robust against the traditional difficulties that faces existing pixel based methods like the scattering of the moving edge pixels. Therefore they can’t utilize edge shape information. Some other edge segment based methods treat every edge segment equally creating edge mismatch due to non stationary background. The proposed method found elegant solution to this lake by using a model that uses the statistics of each background edge segment, so that it can model both the static and partially moving background edges using ordinary training images that may even contain moving objects.

Keywords

Background Modeling, Statistical Distribution Map, Moving Edge Segment, Edge Segment Matching.

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Amir S. Almallah ¹, Jalal H. Awad ¹

Affiliations
1 Department of Computer Science, College of Science, University of Mustansiriyah (UOM), IQ

Abstract

Automatic real time video monitoring and object detection is indeed a challenge since there are many criteria that should be taken In mind in designing and implementing algorithms for this sake. The criteria that should be considered for example are processing speed, scene illumination variation and dynamic outdoor environment. In this study we propose a fast, flexible and immune against illumination variation approach for moving object detection based on the combination of edge segment based background modeling and background subtraction techniques. The first technique is used for building robust and flexible statistical background model, while the other technique is used for the prime detection of moving object to be compared later with the flexible background. Thus this combination leads to computational reduction due to the second technique, and then flexible matching and precise detection due to the first technique.

Keywords

Background Subtraction, Movement Detection, Background Modeling, Statistical Distribution.

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Amir S. Almallah ¹, Wafl Hussein Zayer ², Nora Omran Alkaam ³

Affiliations
1 Department of Computer Science, University of Mustansiriyah, IQ
2 Amara Technical Institute, IQ
3 University of Mustansiriyah, IQ

Abstract

A variety of researches Dealt with the iris identification in different ways and Showed different results. A new system for personal identification based on iris patterns is presented in this paper .We propose to use two activation function wavelet neural network for feature extraction and identification process after segments the image into 32 blocks with (128*128) dimension. The proposed method in this paper involves three steps. First reduced image size using wavelet packet 1-level decomposition , second feature extraction using two activation function wavelet neural network and finally identification using trained data and correlation.

Keywords

Iris, Wavelet Packet, Wavelet Neural Network, Two Activation Function Neural Network.

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Amir S. Almallah ¹, Esraa Mohammed Hassoon ¹

Affiliations
1 Department of Computer Science, College of Science, University of Mustansiriyah (UOM), Baghdad, IQ

Abstract

This paper describes effect of Skeleton pruning process on recognition shapes or objects, Pruning is accomplished by removing branches which is redundant or unnecessary branches , The removal of these small branches does not alter the shape information. It is still quite a challenging problem because of the lack of standard measurements for the importance or significance of a branch. A pruning technique is adopted to deal with the large amount of data that results in further improvement in recognition accuracy. Experimental results showed that our method can achieve good retrieval results .The moderate pruning ratio has improved the features of the shape and improved recognition accuracy.

Keywords

Pruning, Skeletonization, KNN Classification, Wavelet.

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