English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Sparse representation-based classifiers have shown outstanding accuracy androbustness in image classification tasks even with the presence of intensenoise and occlusion. However, it has been discovered that the performancedegrades significantly either when test image is not aligned with thedictionary atoms or the dictionary atoms themselves are not aligned with eachother, in which cases the sparse linear representation assumption fails. Inthis paper, having both training and test images misaligned, we introduce anovel sparse coding framework that is able to efficiently adapt the dictionaryatoms to the test image via large displacement optical flow. In the proposedalgorithm, every dictionary atom is automatically aligned with the input imageand the sparse code is then recovered using the adapted dictionary atoms. Acorresponding supervised dictionary learning algorithm is also developed forthe proposed framework. Experimental results on digit datasets recognitionverify the efficacy and robustness of the proposed algorithm.

Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow