Segmentation Based Mesh Denoising

Feature-preserving mesh denoising has received noticeable attention recently. Many methods often design great weighting for anisotropic surfaces and small weighting for isotropic surfaces, to preserve sharp features. However, they often disregard the fact that small weights still pose negative impacts to the denoising outcomes. Furthermore, it may increase the difficulty in parameter tuning, especially for users without any background knowledge. In this paper, we propose a novel clustering method for mesh denoising, which can avoid the disturbance of anisotropic information and be easily embedded into commonly-used mesh denoising frameworks. Extensive experiments have been conducted to validate our method, and demonstrate that it can enhance the denoising results of some existing methods remarkably both visually and quantitatively. It also largely relaxes the parameter tuning procedure for users, in terms of increasing stability for existing mesh denoising methods

BRUISE DETECTION IN APPLES USING 3D INFRARED IMAGING AND MACHINE LEARNING TECHNOLOGIES

Bruise detection plays an important role in fruit grading. A bruise detection system capable of finding and removing damaged products on the production lines will distinctly improve the quality of fruits for sale, and consequently improve the fruit economy. This dissertation presents a novel automatic detection system based on surface information obtained from 3D near-infrared imaging technique for bruised apple identification. The proposed 3D bruise detection system is expected to provide better performance in bruise detection than the existing 2D systems. We first propose a mesh denoising filter to reduce noise effect while preserving the geometric features of the meshes. Compared with several existing mesh denoising filters, the proposed filter achieves better performance in reducing noise effect as well as preserving bruised regions in 3D meshes of bruised apples. Next, we investigate two different machine learning techniques for the identification of bruised apples. The first technique is to extract hand-crafted feature from 3D meshes, and train a predictive classifier based on hand-crafted features. It is shown that the predictive model trained on the proposed hand-crafted features outperforms the same models trained on several other local shape descriptors. The second technique is to apply deep learning to learn the feature representation automatically from the mesh data, and then use the deep learning model or a new predictive model for the classification. The optimized deep learning model achieves very high classification accuracy, and it outperforms the performance of the detection system based on the proposed hand-crafted features. At last, we investigate GPU techniques for accelerating the proposed apple bruise detection system. Specifically, the dissertation proposes a GPU framework, implemented in CUDA, for the acceleration of the algorithm that extracts vertex-based local binary patterns. Experimental results show that the proposed GPU program speeds up the process of extracting local binary patterns by 5 times compared to a single-core CPU program