Results 241 to 250 of about 524,735 (281)

Grassmannian sparse representations

Journal of Electronic Imaging, 2015
We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory ...
Sherif Azary, Andreas Savakis
openaire   +1 more source

Sparse Representation in Kernel Machines

IEEE Transactions on Neural Networks and Learning Systems, 2015
We study the properties of least square kernel regression with l1 coefficient regularization. The kernels can be flexibly chosen to be either positive definite or indefinite. Asymptotic learning rates are deduced under smoothness condition on the kernel. Sparse representation of the solution is characterized theoretically.
Hongwei, Sun, Qiang, Wu
openaire   +2 more sources

Kernel Sparse Representation-Based Classifier

IEEE Transactions on Signal Processing, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Li, Zhou, Wei-Da, Chang, Pei-Chann, Liu, Jing, Yan, Zhe, Wang, Ting, Li, Fan-Zhang   +6 more
openaire   +1 more source

Sparse-representation-based clutter metric

Applied Optics, 2011
Background clutter is becoming one of the most important factors affecting the target acquisition performance of electro-optical imaging systems. A novel clutter metric based on sparse representation is proposed in this paper. Based on sparse representation, the similarity vector is defined to describe the similarity between the background and the ...
Cui, Yang, Jie, Wu, Qian, Li, Jian-Qi, Zhang   +3 more
openaire   +2 more sources

Discriminative Sparse Representations

2017
In recent years, Sparse Representation (SR) and Dictionary Learning (DL) have emerged as powerful tools for efficient processing image and video data in non-traditional ways. An area of promise for these theories is object recognition. In this chapter, we review the role of algorithms based on SR and DL for object recognition. In particular, supervised,
He Zhang, Vishal M. Patel
openaire   +1 more source

Traffic sign representation using sparse-representations

2013 International Conference on Intelligent Systems and Signal Processing (ISSP), 2013
Automatic Traffic Sign Recognition has gained significant impetus among the research community in recent times. Increasing demands in the arenas of Autonomous Vehicle Navigation and Driver Assistance Systems is making this field of research more attractive.
B. M. Chandrasekhar, V. S. Babu, S. S. Medasani   +2 more
openaire   +1 more source

Image Super-Resolution Via Sparse Representation

IEEE Transactions on Image Processing, 2010
This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary.
Yang, Jianchao, Wright, John, Huang, Thomas S., Ma, Yi   +3 more
openaire   +2 more sources

Autogrouped Sparse Representation for Visual Analysis

IEEE Transactions on Image Processing, 2012
In image classification, recognition or retrieval systems, image contents are commonly described by global features. However, the global features generally contain noise from the background, occlusion, or irrelevant objects in the images. Thus, only part of the global feature elements is informative for describing the objects of interest and useful for
Jiashi, Feng, Xiao-Tong, Yuan, Zilei, Wang, Huan, Xu, Shuicheng, Yan   +4 more
openaire   +2 more sources

Sparse Representation Using SVR

2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017
This paper represents super resolution technique for higher level of scale. It describes categorization like if lower the resolution, the lesser details are captured and for higher resolution the image details are higher and with greater image quality. Image sensors (e.g.
Abhijit S. Shinde, Sachin D. Ruikar, Ashwini Ashok Patil   +2 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