| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 105 === This study proposes an edge extracting technique combining with deep learning and edge detection approaches. Generally, block matching or single feature point matching are used for stereo vision. However, it is difficult to analyze specific objects and to obtain features of specific objects in the image by using those approaches only. To overcome this problem, an edge extracting technique combining with deep learning for stereo vision model was proposed. In this study, the deep learning was used to classify specific objects and to obtain the region of interest of objects in an image frame. Then background subtraction with adaptive threshold was used to extract the object of interest from the image. An edge detector is used to obtain the edge for the object. These edges were clustered based on their characteristics. This edge information is obtained for images from the left and the right cameras. Then a camera calibration approach was used to obtain parameters of both cameras using stereo vision formula. Subsequently the 2D pixel points information of the object was converted to 3D coordinates corresponding to that object. Finally, with the classification result from deep learning approach, this study could filter out noise and reconstruct important object features.
Moreover, a robotic arm was used to evaluate this edge extraction approach. The 3D coordinates of the object of interest obtained from this system was given as the input to the universal type robotic arm. Based on these coordinates, the inverse kinematics for the robotic arm is computed to grasp the object.
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