Results 261 to 270 of about 1,813,028 (298)

Sparse Representation Shape Models

Journal of Mathematical Imaging and Vision, 2012
It is well-known that, during shape extraction, enrolling an appropriate shape constraint model could effectively improve locating accuracy. In this paper, a novel deformable shape model, Sparse Representation Shape Models (SRSM), is introduced. Rather than following commonly utilized statistical shape constraints, our model constrains shape appearance
Yuelong Li, Jufu Feng, Li Meng, Jigang Wu   +3 more
openaire   +1 more source

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

Gait identification by sparse representation

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
Gait recognition under variations of clothing and carrying condition is still a challenging task. In this paper, we present a gait identification method via sparse representation. We formulate the recognition problem as finding the coefficients of linear combination of the training samples plus an error term and discuss sparse signal representation ...
Minyan Gong, Yi Xu 0001, Xiaokang Yang 0001, Wenjun Zhang 0001   +3 more
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   +2 more sources

An efficient representation for sparse sets

ACM Letters on Programming Languages and Systems, 1993
Sets are a fundamental abstraction widely used in programming. Many representations are possible, each offering different advantages. We describe a representation that supports constant-time implementations of clear-set, add-member, and delete-member . Additionally, it supports an efficient
Preston Briggs, Linda Torczon
openaire   +1 more source

Quantized dictionary for sparse representation

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), 2015
Dictionary learning for sparse representation has drawn considerable attention in recent years. In particular, the K-SVD algorithm is an efficient approach, and various modifications of the K-SVD have been developed for applications such as face recognition. However, the efficient storage of the dictionary has not been studied.
Lei Liu, Jie Liang 0001, Yao Zhao 0001, Chunyu Lin, Huihui Bai 0001   +4 more
openaire   +2 more sources

Dictionaries for Sparse Representation Modeling

Proceedings of the IEEE, 2010
Sparse and redundant representation modeling of data assumes an ability to describe signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the choice of the dictionary that sparsifies the signals is crucial for the success of this model. In general, the choice of a proper dictionary can be done using one of two ways: i)
Ron Rubinstein, Alfred M. Bruckstein, Michael Elad   +2 more
openaire   +2 more sources

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   +2 more sources

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   +2 more sources

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