Results 41 to 50 of about 223,248 (273)
Sparse Coding for Alpha Matting [PDF]
IEEE Transactions on Image Processing, 2016 Existing color sampling based alpha matting methods use the compositing equation to estimate alpha at a pixel from pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F,B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives ...
Jubin Johnson +3 more
openaire +3 more sources
Fast Image Super-resolution with Sparse Coding
MATEC Web of Conferences, 2016 In this paper, we introduce a novel fast image reconstruction method for super-resolution (SR) base on sparse coding. This method combine online dictionary learning and a fast sparse coding way, both of which can improve the efficiency of the ...
Yuan Zhi-chao, Li Ben-tu
doaj +1 more source
Kernel locality‐constrained sparse coding for head pose estimation
IET Computer Vision, 2016 In many situations, it would be practical for a computer system user interface to have a model of where a person is looking and what the user is paying attention to.
Hyunduk Kim +3 more
doaj +1 more source
Learning a Deep Representative Saliency Map With Sparse Tensors
IEEE Access, 2019 The past few years have witnessed the prosperity of establishing deep architectures for modeling complex structured data. In this paper, motivated by the hierarchical, multi-scale and sparse characteristics of Human Visual System (HVS), we advance a new ...
Shuyuan Yang, Quanwei Gao, Shigang Wang
doaj +1 more source
Sparse neural codes and convexity [PDF]
Involve, a Journal of Mathematics, 2019 13 pages, 10 ...
Jeffs, R. Amzi +4 more
openaire +3 more sources
Relating sparse and predictive coding to divisive normalization.
PLoS Computational BiologySparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence.
Yanbo Lian, Anthony N Burkitt
doaj +1 more source
Convolutional sparse coding network for sparse seismic time-frequency representation
Artificial Intelligence in GeosciencesSeismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data.
Qiansheng Wei +5 more
doaj +1 more source
Sparse Coding on Stereo Video for Object Detection [PDF]
, 2017 Deep Convolutional Neural Networks (DCNN) require millions of labeled training examples for image classification and object detection tasks, which restrict these models to domains where such datasets are available.
Kenyon, Garrett T. +2 more
core +2 more sources
Molecular Oncology, EarlyView.Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson +9 more
wiley +1 more source
On the Sample Complexity of Predictive Sparse Coding [PDF]
, 2012 The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task.
Gray, Alexander G., Mehta, Nishant A.
core