A collaborative network of correlation filters for object recognition

2003 ◽  
Author(s):  
Abhijit Mahalanobis ◽  
Alan J. Van Nevel
Keyword(s):  
Object Recognition ◽  
Collaborative Network ◽  
Correlation Filters

scholarly journals Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 24495-24502 ◽  
Author(s):  
Sara Tehsin ◽  
Saad Rehman ◽  
Muhammad Omer Bin Saeed ◽  
Farhan Riaz ◽  
Ali Hassan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Object Recognition ◽  
Particle Swarm ◽  
Correlation Filters ◽  
Swarm Optimization ◽  
Self Organizing

scholarly journals Zero-Aliasing Correlation Filters for Object Recognition

2015 ◽  
Vol 37 (8) ◽  
pp. 1702-1715 ◽  
Author(s):  
Joseph A. Fernandez ◽  
Vishnu Naresh Boddeti ◽  
Andres Rodriguez ◽  
B. V. K. Vijaya Kumar
Keyword(s):  
Object Recognition ◽  
Correlation Filters

Object recognition in subband transform-compressed images by use of correlation filters

2003 ◽  
Vol 42 (32) ◽  
pp. 6474 ◽  
Author(s):  
Cindy Daniell ◽  
Abhijit Mahalanobis ◽  
Rod Goodman
Keyword(s):  
Object Recognition ◽  
Correlation Filters ◽  
Compressed Images

Performance of composite correlation filters for object recognition

2011 ◽  
Author(s):  
Everardo Santiago-Ramirez ◽  
J. A. González-Fraga ◽  
J. I. Ascencio-Lopez ◽  
Olimpia Buenrostro
Keyword(s):  
Object Recognition ◽  
Correlation Filters

The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum
Keyword(s):  
Rare Diseases ◽  
Treatment Options ◽  
Castleman Disease ◽  
High Impact ◽  
Collaborative Network ◽  
Network Approach ◽  
Grant Applications ◽  
Research Questions ◽  
Novel Strategy ◽  
Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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