This thesis describes an integrated framework that brings together the fields of Digital Image processing (DIP), web technology, Database system and batch processing to address object recognition and classification problems in computer vision. With the recent terrorist attacks across the globe places such as ports of entry have become important places to enforce stronger security measures. Security scanners work in tandem with image processing algorithms to prevent terrorist attacks by identifying threat objects like guns, chemical liquids in bottles and explosives in passenger baggage and cargo containers. In this thesis, an integrated web based object recognition and classification framework is proposed and demonstrated for automatic threat detection. The novel features of this framework include utilizing the Human Visual 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 feature vectors of the segmented objects are generated using the Cell edge distribution (CED) method and are classified using a support vector machine (SVM) based classifier to identify threat objects. The framework leverages the strength of a database and batch processing system with web technology to facilitate the development of reusable, portable and scalable real time threat detection application. The experimental results demonstrate the proposed framework efficiency in reducing the classification time and provide accurate detection.