An Application of Image Processing Techniques to Camera Vibration Detection

• Main Authors: MENG-TSE TAI, 戴孟哲
• Other Authors: Shin-Hsung Twu
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/am7695

碩士 === 中原大學 === 通訊工程碩士學位學程 === 102 === Abstract In this thesis, we propose a new way to detect shake on DSLR camera. Our main propose in this thesis is to use digital image processing to discriminate the blurred images caused by shake, even the shake is very light that is difficult to detect on the DSLR camera's LCD screen. In this thesis, the methods we propose have two parts. One is applying image processing to make camera vibration detection. The other part is the experimental results. On the first part, we shoot two photos, one is clear and another is blurred to detect the difference after digital image processing between them. We use the Sobel filter to find the edges of the two images. After that, we use dilation and erosion to these two images filter to reduce the edges we don’t want. Last we use skeletonization and pruning to make the edges become clear to display the difference between clear photo and blurred photo. On the second part, we will use the purposed methods on the first part to do same experiments. We take two kinds of photos: one kind is to shoot blurred photos of the same objects in different conditions, and another kind is to shoot blurred photos of different objects in the same conditions. Finally, we take these photos to be processed according to our proposed methods to see if they display the blurred edges to prove the effectness of our methods, what photos are clear, what photos are blurred. In this thesis, the contributions of our research are as follow: (1) Promote beginner’s discriminate speed: It is not easy for beginners to discriminate if photos blurred or not. This method can help the beginners to discriminate the blurred photos. (2)Reduce the probability of missing perfect shooting time: Using this method, one can discriminate blurred photo or clear photos quickly. Hence you don’t need to zoom in to check if photos blurred or not. In other words, this method can reduce the probability of missing perfect shooting time when you shot moving objects. (3) Prevent shooting light blurred photos: Light blurred photos are not obvious on camera’s LCD screen. By using our methods, these light blurred photos will very obvious on camera’s LCD screen, so you can delete these photos and prevent shooting light blurred photos. (4)Add the accuracy of discriminate photos: Form simulation results, it tells us that the accuracy of discrimination of blurred photos increases from 66.67% to 79.17%.