Results 211 to 220 of about 38,175 (260)

Robust registration under large image misalignment using an iterative step-aware transformer with application to corneal confocal microscopy. [PDF]

Biomed Opt Express
Chen Z, Zemborain ZZ, Ruiz-Lozano RE, Quiroga-Garza ME, Ma S, Soifer M, Azar NS, Mousa HM, Perez VL, Farsiu S.   +9 more
europepmc   +1 more source

Block compressive sensing-based image denoising framework using optimized sensing matrix and split Bregman algorithm. [PDF]

Sci Rep
Thomas EN, Theeda P, Praveen T.
europepmc   +1 more source

Advanced channel estimation in OTFS and NOMA using deep bayesian gaussian processes and compressive sensing. [PDF]

Sci Rep
Anilkumar N, Sengan S.
europepmc   +1 more source

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study. [PDF]

J Med Internet Res
Kumar P, Viszolay AD, Upadhayaya R, Moomtaheen F, Greer DR, Bologa CG, Schneider KA, Davis SE, Matheny ME, van der Goes D, Villarreal G, Zhu Y, Tohen M, Malec SA, Yang JJ, Fielstein EM, Lambert CG.   +16 more
europepmc   +1 more source

Temporal Learning with Dynamic Range (TLDR) for modeling recurrent exposure and treatment outcomes. [PDF]

Sci Rep
Cheng J, Hügel J, Tian J, Azhir A, Murphy SN, Klann JG, Estiri H.   +6 more
europepmc   +1 more source

Design of a next-generation conventional scintillator x-ray detector: Improved spatial resolution and fill factor. [PDF]

Med Phys
Hsieh SS.
europepmc   +1 more source

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Discrete Sparse Coding

Neural Computation, 2017
Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions.
Georgios Exarchakis, Jörg Lücke
openaire   +3 more sources

Product Sparse Coding

2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014
Sparse coding is a widely involved technique in computer vision. However, the expensive computational cost can hamper its applications, typically when the codebook size must be limited due to concerns on running time. In this paper, we study a special case of sparse coding in which the codebook is a Cartesian product of two subcodebooks.
Tiezheng Ge, Kaiming He, Jian Sun 0001
openaire   +1 more source

Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework.
Shenghua Gao, Ivor Wai-Hung Tsang, Liang-Tien Chia   +2 more
openaire   +2 more sources