This paper presents a multi tier web architecture that integrates web technology, Database system, and Batch processing tools for the development of a real time threat detection system. Four data repository models are...
详细信息
ISBN:
(纸本)9781424465880
This paper presents a multi tier web architecture that integrates web technology, Database system, and Batch processing tools for the development of a real time threat detection system. Four data repository models are introduced for effective data storage and retrieval. The baseline feature vectors are introduced and stored in the database table using a batch job. The batch job performs load balancing by calculating the new feature vector using the offline server and updates the online database server. The illustrative application uses the Hierarchical Multi level HVS segmentation, ratio based edge detection, and support vector machine for threat recognition and detection. The 64 bit edge based feature vector is generated for the baseline images and the input test object images using the cell edge distribution approach. The experimental results demonstrate that the presented framework is efficient in facilitating accurate threat detection and support the development of portable, reusable and scalable object recognition applications for heterogeneous distributed environment.
In this paper, an integrated framework comprising of computer vision algorithms, Database system and Batch processing techniques has been developed to facilitate effective automatic threat recognition and detection fo...
详细信息
ISBN:
(纸本)9780819481726
In this paper, an integrated framework comprising of computer vision algorithms, Database system and Batch processing techniques has been developed to facilitate effective automatic threat recognition and detection for security applications. The proposed approach is used for automatic threat detection. The novel features of this structure include utilizing the humanvisual System model for segmentation, and a new ratio based edge detection algorithm that includes a new adaptive hysteresis thresholding method. The feature vectors of the baseline images are generated and stored in a relational database system using a batch window. The batch window is a special process where image processing tasks with similar needs are grouped together and effectively processed to save computing and memory requirements. The feature vectors of the segmented objects are generated using the CED method and are classified using a support vector machine (SVM) based classifier to identify threat objects. The experimental results demonstrate the presented framework efficiency in reducing the classification time and provide accurate detection.
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