Results 51 to 60 of about 114,572 (313)

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

Sensors, 2015
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging ...
Baoxian Wang, Linbo Tang, Jinglin Yang, Baojun Zhao, Shuigen Wang   +4 more
doaj   +1 more source

Establishing an Apheresis Medicine Program in a Resource‐Constrained Setting: A 5‐Year Experience From Lagos, Nigeria

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Establishing a comprehensive apheresis medicine program in a resource‐constrained setting presents significant structural, financial, and logistical challenges. Despite the growing clinical importance of apheresis services globally, published experience from sub‐Saharan Africa remains sparse.
Folasade Adelekan‐Popoola, Shakirat Gold‐Olufadi, Olufunto Olufela Kalejaiye, Abdullateef Ogala, Rasheed A. Balogun   +4 more
wiley   +1 more source

Saliency Detection Using Sparse and Nonlinear Feature Representation

The Scientific World Journal, 2014
An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient ...
Shahzad Anwar, Qingjie Zhao, Muhammad Farhan Manzoor, Saqib Ishaq Khan   +3 more
doaj   +1 more source

Sparse representations for text categorization [PDF]

Interspeech 2010, 2010
Sparse representations (SRs) are often used to characterize a test signal using few support training examples, and allow the number of supports to be adapted to the specific signal being categorized. Given the good performance of SRs compared to other classifiers for both image classification and phonetic classification, in this paper, we extended the ...
Tara N. Sainath, Sameer Maskey, Dimitri Kanevsky, Bhuvana Ramabhadran, David Nahamoo, Julia Hirschberg   +5 more
openaire   +1 more source

Audio Source Separation Using Sparse Representations [PDF]

, 2010
This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W.
Nesbit, Andrew, Plumbley, MD, Jafari, MG, Vincent, Emmanuel, Nesbit, A, Jafari, Maria, G., Plumbley, Mark, D., Vincent, E   +7 more
core   +1 more source

Forecasting the Dialysis Burden in Japan: Validation‐Based Projections of Prevalence and Incidence Through 2050

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin, Berrak Itır Aylı, Nüket Paksoy   +2 more
wiley   +1 more source

Integrated Sparse Coding With Graph Learning for Robust Data Representation

IEEE Access, 2020
Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies.
Yupei Zhang, Shuhui Liu
doaj   +1 more source

Sparse Representation For Image Prediction

, 2007
Publication in the conference proceedings of EUSIPCO, Poznan, Poland ...
Martin, Aurélie, Fuchs, Jean-Jacques, Guillemot, Christine, Thoreau, Dominique   +3 more
openaire   +4 more sources

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Remote Sensing, 2017
Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification.
Fei Tong, Hengjian Tong, Junjun Jiang, Yun Zhang   +3 more
doaj   +1 more source