Distance Measurement Systems and Their Applications Based on Image Understanding

major approaches. In the first approach, the depth between CCD and object can be calculated by the focus length and the physical dimension of the grids of CCD pixels, which are tinny in size and hard to measure. As a result, measuring error of the system is generally unconquerable because of the micrometer scale of the grids of CCD pixels. The stereo vision based methods, on the other hand, provide better measuring accuracy, but difficulties arise to solve the matching problem, which is the foundation of these methods.
It is found that investigation on the depth of the image is not highly regarded in the realm of image processing and pattern recognition. As an attempt to solve this problem, I have researched and obtained several image understanding based approaches for solving the depth retrieval problems. The main difference between the proposed systems and the abovementioned (existing) systems is the mechanism structure adopted and information from image understanding. There are three types of
depth retrieval systems that I have developed so far, including: 1. Distance measurement based on laser-projected CCD Images, 2. Distance measurement based on arbitrarily designated two points in 3-D space, and 3. Distance measurement based
on pixel variation of CCD images due to camera movement. These three types of depth retrieval systems serve different purposes and occasions. Type one is generally used in three-dimensional localization and long-distance landslide surveillance. Type-two has advantages for use in intelligent transportation systems, where I have applied-the method on smart vehicles by designating taillights as the main feature for distance-measuring. Type three could provide great capabilities for 3-D space reconstruction and robot navigation. The proposed methods might provide new applications for digital image processing.