Detection of moving objects and their classification using interactive segmentation methods

In recent years, the rise of the digital image and video data available has led to an increasing demand for image processing. Moving object detection for a fixed camera has become an active research area. It can be used for many vision-based applications. The main concern is subtracting the moving target object from image sequences and tracking them in the next frames. As the first step in a video analysis, object detection is an important issue in the system. Object detection and classification are two very important tasks for quality control in the industrial process. The first step of a quality algorithm is the detection of the moving object. Afterward, the detected object is classified according to its size and color related properties. In this framework, an interactive image segmentation method will be proposed to detect a moving object. The segmentation method is based on three steps: the calculation of the high contrast gray-level image of a color image, separation of the background and objects using Otsu thresholding, and morphological process to fill holes in segmented images. After the moving object is detected, the class of this object will be determined by using image moments. The proposed method separates the moving object from the background. The algorithm will test two videos that were taken from a high-resolution camera. The performance of the proposed method is assessed a real data and good results were obtained. In the other hand, more recently the quality control is a very important task in industrial systems. When the quality control of a product has been made during production, the manufacturing defects will be minimized. For this purpose, automatic inspection system planned to be developed. In this research, a new vision based method will be proposed for quality control and inspection purposes. The proposed method uses interactive segmentation which the main principle is based on Gaussian mixture models. After the current frame is segmented, some morphological operators will be applied to the segmented image in order to reduce noise. Some geometrical features are supposed to be calculated and the objects will be inspected according to their sizes. The efficiency of the proposed method will be ensuring by using real videos.