Stereo matching. Given the dictionary D and the test image pair IL and IR, we compute their sparse representations as matching features for dense correspondence. The stereo pair is partitioned into patches YL and YR via an n × n sliding window. Each column in YL and YR corresponds to one image patch.
In this paper, we transform gray image patches into sparse representations over a learned dictionary and then compute the dissimilarity between the sparse ...
People also ask
What is dictionary sparse representation?
What is sparse representation based classification?
Jul 17, 2020 · We propose a stereo matching algorithm based on dictionary learning using discriminative feature and gradient constrain in this paper.
Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset ...
Aug 13, 2021 · A supervised dictionary learning approach for discriminative sparse representation is applied as an automatic feature discovery framework. Based ...
... Sparse Representation over Discriminative Dictionary for Stereo Matching ... Paper. Sparse Representation over Discriminative Dictionary for Stereo Matching.
This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD ...
Sparse representation over discriminative dictionary for stereo matching. Pattern Recognition 71:278-289, 2017. Code. 03/23/17, DSGCA, Q, Williem and In Kyu ...
Feb 8, 2021 · It learns the convolutional filter bank from stereo image pairs in an unsupervised manner, which reduces the redundancy of the convolution.
We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled ...
Missing: stereo | Show results with:stereo