By Yue Deng
This thesis essentially specializes in how you can perform clever sensing and comprehend the high-dimensional and low-quality visible info. After exploring the inherent constructions of the visible facts, it proposes a few computational versions overlaying an intensive variety of mathematical issues, together with compressive sensing, graph concept, probabilistic studying and data idea. those computational versions also are utilized to deal with a couple of real-world difficulties together with biometric acceptance, stereo sign reconstruction, ordinary scene parsing, and SAR photograph processing.
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Additional info for High-Dimensional and Low-Quality Visual Information Processing: From Structured Sensing and Understanding
C1 = C1 + μk bk1 ; k−1 k k 5 C2 = C2 + μk b2 ; 3 Ak Ak−1 + μ−1 Ck1 )]; To show LHR ideally represents low-rank structures from data, experiments on subspace clustering are conducted on two datasets. In this part, we apply LHR to the task of motion segmentation in Hopkins155 dataset . Hopkins155 database is a benchmark platform to evaluate general subspace clustering algorithms, which contains 156 video sequences, and each of them has been summarized to be a matrix recoding 39–50 data vectors.
36 3 Sparse Structure for Visual Signal Sensing . . as three kinds: Gaussian, bias, and outliers. Since we suppose that one point can be seen by n cameras, so the corresponding n entries (randomly selected) in the kth row of the fusion matrix will be denoted as a known value, while the remaining entries are unknown. From the observed data in the constructed matrix, we calculate the mean μc and standard deviation σc . In this experiment, Gaussian noises are independently generated following N (0, σc2 /100) and are added to all the observed entries in the fusion matrix.
3. From both the visual comparisons and the completion accuracies, it is observed that the LPC method achieves the best completion result. In this section, we added noise to the incomplete fusion matrix where known entries are obtained from ground truth data. This experiment is designed to emphasize the power of the proposed LPC model for noise removal and low rank matrix completion. However, it cannot demonstrate the effectiveness of the whole fusion framework which contains two critical steps: (1) incomplete matrix construction and (2) LPC method for noisy matrix completion.
High-Dimensional and Low-Quality Visual Information Processing: From Structured Sensing and Understanding by Yue Deng