NDLI: Fast Ray features for learning irregular shapes

Content Provider	IEEE Xplore Digital Library
Author	Smith, K. Carleton, A. Lepetit, V.
Copyright Year	2009
Description	Author affiliation: Department of Basic Neurosciences, Medical Faculty, University of Geneva, CH-1211 Genève, Switzerland (Carleton, A.) \|\| Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, CH-1015, Switzerland (Smith, K.; Lepetit, V.)
Abstract	We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to represent with standard feature sets. This property allows Ray features to efficiently and robustly recognize deformable or irregular shapes, such as cells in microscopic imagery. Experiments show Ray features clearly outperform other powerful features including Haar-like features and Histograms of Oriented Gradients when applied to detecting irregularly shaped neuron nuclei and mitochondria. Ray features can also provide important complementary information to Haar features for other tasks such as face detection, reducing the number of weak learners and computational cost. Ray features can be efficiently precomputed to reduce cost, just as precomputing integral images reduces the overall cost of Haar features. While Rays are slightly more expensive to precompute, their computational cost is less than that of Haar features for scanning an AdaBoost-based detector window across an image at run-time.
Starting Page	397
Ending Page	404
File Size	2216886
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Computational efficiency Costs Robustness Image recognition Microscopy Histograms Neurons Face detection Detectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Isophote properties as features for object detection

Joint Haar-like features for face detection

Integrated learning of saliency, complex features, and object detectors from cluttered scenes

Hierarchical face parsing via deep learning

Image matching using local symmetry features

Implicit elastic matching with random projections for pose-variant face recognition

Fast and robust Earth Mover's Distances

Fast polygonal integration and its application in extending haar-like features to improve object detection

Dynamic Cascades for Face Detection

Fast Ray features for learning irregular shapes

Similar Documents

Isophote properties as features for object detection

Joint Haar-like features for face detection

Integrated learning of saliency, complex features, and object detectors from cluttered scenes

Hierarchical face parsing via deep learning

Image matching using local symmetry features

Implicit elastic matching with random projections for pose-variant face recognition

Fast and robust Earth Mover's Distances

Fast polygonal integration and its application in extending haar-like features to improve object detection

Dynamic Cascades for Face Detection

Fast Ray features for learning irregular shapes