A new object detection and classification method for quality control based on segmentation and geometric features - Aydin, Ilhan; Karakose, Mehmet; Hamsin, G. Ghazi; Sarimaden, Alisan; Akin, Erhan (Ieee, 2017)
Object detection and classification are two very important tasks for quality control in industrial process. The first step of a quality algorithm is the detection of the moving object. Afterwards, the detected object is classified according to its size and color related properties. In this study, an interactive image segmentation method is proposed to detect a moving object. The segmentation method is based on three steps: the calculation of the high contrast grey-level image of a color image, separation of the background and objects by 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 background. The algorithm was tested on two videos that taken from a high resolution camera. The performance of the proposed method was assessed a real data and good results were obtained.
2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
Keywords: Classification; image analysis; image moments; segmentation