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We present a unified framework for dense correspondence estimation, called Homography flow, to handle large photometric and geometric deformations in an efficient manner. Our algorithm is inspired by recent successes of the sparse to dense framework. The main intuition is that dense flows located in same plane can be represented as a single geometric transform. Tailored to dense correspondence task,...
Hyperspectral sparse unmixing is a task to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. The abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. It implies the low rankness...
A key issue for compressed Sensing (CS) is to design the measurement matrix. However, the traditional measurement matrix is not optimal due to its non-adaptability without showing discrimination to different components. In this paper, a prior information directed stage-wise measurement matrix is proposed for block compressed image sensing, leading to a st-BCS method. In the first stage, the measurement...
In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in...
Learned Sparsifying orthogonal transforms (SOTs) have proven to be a powerful tool for image and video processing. In this paper, we propose a variant of SOT, named compact bases SOT, or CB-SOT, which has several promising features for data compression: (i) as an input-adaptive transform, it can sparsely represent the input data very well; (ii) the transform matrix is orthogonal; (iii) unlike SOT,...
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