Improving depth maps of plants by using a set of five cameras

Abstract

Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 12/09/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Abstract. Obtaining high-quality depth maps and disparity maps with the use of a stereo camera is a challenging task for some kinds of objects. The quality of these maps can be improved by taking advantage of a larger number of cameras. The research on the usage of a set of five cameras to obtain disparity maps is presented. The set consists of a central camera and four side cameras. An algorithm for making disparity maps called multiple similar areas (MSA) is introduced. The algorithm was specially designed for the set of five cameras. Experiments were performed with the MSA algorithm and the stereo matching algorithm based on the sum of sum of squared differences (sum of SSD, SSSD) measure. Moreover, the following measures were included in the experiments: sum of absolute differences (SAD), zero-mean SAD (ZSAD), zero-mean SSD (ZSSD), locally scaled SAD (LSAD), locally scaled SSD (LSSD), normalized cross correlation (NCC), and zero-mean NCC (ZNCC). Algorithms presented were applied to images of plants. Making depth maps of plants is difficult because parts of leaves are similar to each other. The potential usability of the described algorithms is especially high in agricultural applications such as robotic fruit harvesting. 1 Introduction Depth maps can be obtained on the basis of two images from a stereo camera. The increase in a depth map precision is often achieved by using more advanced algorithms with higher computational complexity. 1,2 This paper presents a different approach for improving depth map quality. The improvement is achieved by taking advantage of a larger number of cameras. Determining depth map is particularly difficult in case of images of plants. Tan et al. 3 presented a spectrum of plants according to a varying leaf size. Making depth maps of plants is the easiest when plants are small and they have big leaves. Processing stereo images of trees is the most difficult. It is caused by the fact that leaves are similar to each other and they have many areas with the same color. This is problematic in stereo matching algorithms as there are many areas of one image that have multiple candidate matches in the other image. This problem is reduced when a multicamera set is used. This paper presents an application of a set of five cameras to making depth maps of plants. The set consists of a central camera and four cameras around it. This kind of camera arrangement was first described by Park and Inoue. 4 In order to make depth maps, they used a matching measure based on the sum of sum of squared differences (SSSD). This paper describes the result of applying this depth map making method to images of plants. The paper also presents the application of other matching measures to the set of cameras described by Park and Inoue. 4 Moreover, this paper introduces the new algorithm for making depth maps called the multiple similar areas