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Pushing edge detection to the limit: towards building semantic features for human emotion recognition
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Umeå: Department of Applied Physics and Electronics, Umeå University , 2013. , 89 p.
Series
Digital Media Lab, ISSN 1652-6295 ; 17
Keyword [en]
edge detection, multi-scale, threshold, hysteresis, non-maximum suppression, redundancy, center of mass, integral image, bottom-up feature
National Category
Computer Systems Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:umu:diva-116137ISBN: 9789174597851OAI: oai:DiVA.org:umu-116137DiVA: diva2:901553
Note

P. 29-74: Papers I-III

Available from: 2016-02-08 Created: 2016-02-08 Last updated: 2016-02-08Bibliographically approved
List of papers
1. Independent Thresholds on Multi-scale Gradient Images
Open this publication in new window or tab >>Independent Thresholds on Multi-scale Gradient Images
2013 (English)In: The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013), Kitakyushu, Japan, 2013, 124-131 p.Conference paper (Refereed)
Abstract [en]

In this paper we propose a multi-scale edge detection algorithm based on proportional scale summing. Our analysis shows that proportional scale summing successfully improves edge detection rate by applying independent thresholds on multi-scale gradient images. The proposed method improves edge detection and localization by summing gradient images with a proportional parameter cn (c < 1); which ensures that the detected edges are as close as possible to the fine scale. We employ non-maxima suppression and thinning step similar to Canny edge detection framework on the summed gradient images. The proposed method can detect edges successfully and experimental results show that it leads to better edge detection performance than Canny edge detector and scale multiplication edge detector.

Place, publisher, year, edition, pages
Kitakyushu, Japan: , 2013
Series
, The Institute of Industrial Applications Engineers, Japan
Keyword
Edge, Detection, Multi-scale
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:umu:diva-82278 (URN)10.12792/icisip2013.027 (DOI)
Conference
The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013)
Projects
INTRO – INteractive RObotics research network
Funder
EU, FP7, Seventh Framework Programme
Available from: 2013-10-29 Created: 2013-10-29 Last updated: 2016-02-23Bibliographically approved
2. Restricted Hysteresis Reduce Redundancy in Edge Detection
Open this publication in new window or tab >>Restricted Hysteresis Reduce Redundancy in Edge Detection
2013 (English)In: Journal of Signal and Information Processing, ISSN 2159-4465, E-ISSN 2159-4481, Vol. 4, no 3B, 158-163 p.Article in journal, Editorial material (Refereed) Published
Abstract [en]

In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around these directions. This is caused by the discrete calculation of non-maximum suppression. Many algorithms use edge points as feature for further task such as line extraction, curve detection, matching and recognition. Redundancy is a very important factor of algorithm speed and accuracy. We find that most edge detection algorithms have redundancy of 50% in the worst case and 0% in the best case depending on the edge direction distribution. The common redundancy rate on natural images is approximately between 15% and 20%. Based on Canny’s framework, we propose a restriction in the hysteresis step. Our experiment shows that proposed restricted hysteresis reduce the redundancy successfully.

Keyword
edge detection, hysteresis, non-maximum suppression, redundancy
National Category
Computer Vision and Robotics (Autonomous Systems) Signal Processing
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:umu:diva-82354 (URN)10.4236/jsip.2013.43B028 (DOI)
Projects
INTRO – INteractive RObotics research network
Funder
EU, FP7, Seventh Framework Programme
Available from: 2013-10-30 Created: 2013-10-30 Last updated: 2016-02-23Bibliographically approved
3. Fast edge detection by center of mass
Open this publication in new window or tab >>Fast edge detection by center of mass
Show others...
2013 (English)In: The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013), Kitakyushu, Japan, 2013, 103-110 p.Conference paper (Refereed)
Abstract [en]

In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.

Place, publisher, year, edition, pages
Kitakyushu, Japan: , 2013
Series
, The Institute of Industrial Applications Engineers, Japan
Keyword
Edge detection, Center of mass, Integral image, Multi-scale, Fast computing.
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:umu:diva-82350 (URN)10.12792/icisip2013.024 (DOI)
Conference
The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013)
Projects
INTRO – INteractive RObotics research network
Funder
EU, FP7, Seventh Framework Programme
Available from: 2013-10-30 Created: 2013-10-30 Last updated: 2016-02-23Bibliographically approved

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