Automatic Image Segmentation and its Applications to Human Detection and Depth Estimation

• Main Authors: Tseng, Hsiao-Chun, 曾筱君
• Other Authors: Chang, Jyh-Yeong
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/12476334944768056585

碩士 === 國立交通大學 === 電機與控制工程系所 === 97 === Image segmentation plays an essential role in applications such as medicine image processing, traffic flow magnitude monitored, human detection, multimedia applications, and many others. Depth estimation is one of the important topics in multimedia and service robot applications. It is a very challenging task to extract human or other objects of interested from scenes without any background information, and then to estimate the human depths from single camera view. To solve this, we adopt the method which combines the feature-based, shape, and space information of an image to recognize different segmented regions. Then we estimate the human depth based on vanishing line and point, or based on camera’s depth look-up table. In the thesis, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically, and the remaining pixels are classified to the nearest region. After the region growing procedure, two neighboring regions with high similarity are merged. The human body is determined by confining semantic human body region in segmented regions, and those belonging to the human face and human body are merged afterward. The human is extracted and the human position is also decided. Lastly, we will detect the human vertical y-coordinate values in the image, and the depths can then be estimated according to the cross-ratio formula or the depth look-up tables of the camera.