Results 281 to 290 of about 114,572 (313)

Sparse representation shape model

2010 IEEE International Conference on Image Processing, 2010
This paper introduces a novel shape model, Sparse Representation Shape Model (SRSM). Rather than for modeling specific deformable shapes, this model is specially designed for shape segmentation and matching. This model is utilized under the framework of Active Shape Models (ASM).
Yuelong Li, Jufu Feng
openaire   +1 more source

Face Aging by Sparse Representation

2010
Face aging aims at synthesizing one's face at different ages which interests many researchers in fields of cartoon animation, age estimation, face recognition, etc. However, modelling aging process is still challenging due to lack of robust features and a reasonable training set.
Heng Huang, Yuxu Lin, Mingli Song, Jiajun Bu, Chun Chen 0001   +4 more
openaire   +1 more source

Sparse reconstruction of ISOMAP representations

Journal of Intelligent & Fuzzy Systems, 2019
Isometric feature mapping (ISOMAP) is one of the classical methods of nonlinear dimensionality reduction (NLDR) and seeks for low dimensional (LD) structure of high dimensional (HD) data. However, the inverse problem of ISOMAP has never been investigated, which recovers the HD sample from the related LD sample, and its application prospect in data ...
Honggui Li, Maria Trocan
openaire   +1 more source

Discriminative sparse representations with applications

2013 American Control Conference, 2013
Significant advances in compressive sensing and sparse signal encoding have provided a rich set of mathematical tools for signal analysis and representation. In addition to novel formulations for enabling sparse solutions to underdetermined systems, exciting progress has taken place in efficiently solving these problems from an optimization theoretic ...
Vishal Monga, Trac D. Tran
openaire   +1 more source

Sparse Representation for Machine Learning

2013
Sparse representation is a parsimonious principle that a signal can be approximated by a sparse superposition of basis functions. The main topic of my thesis research is to apply this principle in the machine learning fields including classification, feature extraction, feature selection, and optimization.
openaire   +1 more source

Sparse Representations with Cone Atoms

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Denis C. Ilie-Ablachim, Andra Baltoiu, Bogdan Dumitrescu   +2 more
openaire   +1 more source

On Linguistic Variables and Sparse Representations

2015
Linguistic variables can be seen as dictionaries to represent data. In fields as Signal Processing or Machine Learning is usual to use or to search redundant dictionaries to promote sparse representations. This kind of representations present several interesting properties as a high generalization capacity, simplification and economy, among others.
openaire   +1 more source

Sparse Representations

2014
Jayaraman J. Tiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias   +3 more
openaire   +2 more sources

Image Super-Resolution Via Sparse Representation

IEEE Transactions on Image Processing, 2010
Jianchao Yang, John Wright, Yi Ma
exaly  

Nonlocally Centralized Sparse Representation for Image Restoration

IEEE Transactions on Image Processing, 2013
Weisheng Dong, Guangming Shi
exaly