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- 5Computer Vision and Multimedia Communications
- 1Amyotrophic Lateral Sclerosis (ALS)
- 1Aura Matrix
- 1Brain Parenchymal Fraction (BPF)
- 1Co-occurrence Matrix
Technical report TR05-31. A 3D thinning algorithm erodes a 3D image layer by layer to extract the skeletons. This paper presents an improved fully parallel 3D thinning algorithm which extracts medial lines from a 3D image. This algorithm is based on Ma and Sonka's thinning algorithm, which fails...
Technical report TR05-32. An algorithm for automatic estimation of 3D transformations between two objects is presented in this paper. Skeletons of the 3D objects are created with a fully parallel thinning algorithm and feature point pairs (land markers) are extracted from skeletons automatically,...
Technical report TR11-07. In this paper, we present a structured light method to recover depth maps. Contrary to most temporal coding methods which require projecting a series of patterns, our method needs one color pattern only. Unlike most spatial coding methods which establish correspondence...
This paper presents a novel, simple, yet powerful texture analysis method inspired by the well-known Local Binary Patterns (LBP) method called the Local Frequency Descriptors (LFD). Like LBP, the proposed method is invariant to rotation and linear changes of illumination; however,it does not...
In this paper, we applied texture analysis to evaluate cerebral degeneration in amyotrophic lateral sclerosis (ALS). Two well-known methods, the gray level co-occurrence matrix (GLCM) and the gray level aura matrix (GLAM) were employed to extract texture features from routine T1 and T2 MR images....