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Restricted Hysteresis Reduce Redundancy in Edge Detection
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (DML, i2lab)
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (DML, i2lab)
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (i2lab)
KTH.
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.

Place, publisher, year, edition, pages
2013. Vol. 4, no 3B, 158-163 p.
Keyword [en]
edge detection, hysteresis, non-maximum suppression, redundancy
National Category
Computer Vision and Robotics (Autonomous Systems) Signal Processing
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:umu:diva-82354DOI: 10.4236/jsip.2013.43B028OAI: oai:DiVA.org:umu-82354DiVA: diva2:660776
Projects
INTRO – INteractive RObotics research network
Funder
EU, FP7, Seventh Framework Programme
Available from: 2013-10-30 Created: 2013-10-30 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Interest Curves: Concept, Evaluation, Implementation and Applications
Open this publication in new window or tab >>Interest Curves: Concept, Evaluation, Implementation and Applications
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Image features play important roles in a wide range of computer vision applications, such as image registration, 3D reconstruction, object detection and video understanding. These image features include edges, contours, corners, regions, lines, curves, interest points, etc. However, the research is fragmented in these areas, especially when it comes to line and curve detection. In this thesis, we aim to discover, integrate, evaluate and summarize past research as well as our contributions in the area of image features. This thesis provides a comprehensive framework of concept, evaluation, implementation, and applications for image features.

Firstly, this thesis proposes a novel concept of interest curves. Interest curves is a concept derived and extended from interest points. Interest curves are significant lines and arcs in an image that are repeatable under various image transformations. Interest curves bring clear guidelines and structures for future curve and line detection algorithms and related applications.

Secondly, this thesis presents an evaluation framework for detecting and describing interest curves. The evaluation framework provides a new paradigm for comparing the performance of state-of-the-art line and curve detectors under image perturbations and transformations.

Thirdly, this thesis proposes an interest curve detector (Distinctive Curves, DICU), which unifies the detection of edges, corners, lines and curves. DICU represents our state-of-the-art contribution in the areas concerning the detection of edges, corners, curves and lines. Our research efforts cover the most important attributes required by these features with respect to robustness and efficiency.

Interest curves preserve richer geometric information than interest points. This advantage gives new ways of solving computer vision problems. We propose a simple description method for curve matching applications. We have found that our proposed interest curve descriptor outperforms all state-of-the-art interest point descriptors (SIFT, SURF, BRISK, ORB, FREAK). Furthermore, in our research we design a novel object detection algorithm that only utilizes DICU geometries without using local feature appearance. We organize image objects as curve chains and to detect an object, we search this curve chain in the target image using dynamic programming. The curve chain matching is scale and rotation-invariant as well as robust to image deformations. These properties have given us the possibility of resolving the rotation-variance problem in object detection applications. In our face detection experiments, the curve chain matching method proves to be scale and rotation-invariant and very computational efficient.

Abstract [sv]

Bilddetaljer har en viktig roll i ett stort antal applikationer för datorseende, t.ex., bildregistrering, 3D-rekonstruktion, objektdetektering och videoförståelse. Dessa bilddetaljer inkluderar kanter, konturer, hörn, regioner, linjer, kurvor, intressepunkter, etc. Forskningen inom dessa områden är splittrad, särskilt för detektering av linjer och kurvor. I denna avhandling, strävar vi efter att hitta, integrera, utvärdera och sammanfatta tidigare forskning tillsammans med vår egen forskning inom området för bildegenskaper. Denna avhandling presenterar ett ramverk för begrepp, utvärdering, utförande och applikationer för bilddetaljer.

För det första föreslår denna avhandling ett nytt koncept för intressekurvor. Intressekurvor är ett begrepp som härrör från intressepunkter och det är viktiga linjer och bågar i bilden som är repeterbara oberoende av olika bildtransformationer. Intressekurvor ger en tydlig vägledning och struktur för framtida algoritmer och relaterade tillämpningar för kurv- och linjedetektering.

För det andra, presenterar denna avhandling en utvärderingsram för detektorer och beskrivningar av intressekurvor. Utvärderingsramverket utgör en ny paradigm för att jämföra resultatet för de bästa möjliga teknikerna för linje- och kurvdetektorer vid bildstörningar och bildtransformationer.

För det tredje presenterar denna avhandling en detektor för intressekurvor (Distinctive curves, DICU), som förenar detektering av kanter, hörn, linjer och kurvor. DICU representerar vårt främsta bidrag inom området detektering av kanter, hörn, kurvor och linjer. Våra forskningsinsatser täcker de viktigaste attribut som krävs av dessa funktioner med avseende på robusthet och effektivitet.

Intressekurvor innehåller en rikare geometrisk information än intressepunkter. Denna fördel öppnar för nya sätt att lösa problem för datorseende. Vi föreslår en enkel beskrivningsmetod för kurvmatchningsapplikationer och den föreslagna deskriptorn för intressekurvor överträffar de bästa tillgängliga deskriptorerna för intressepunkter (SIFT, SURF, BRISK, ORB, och FREAK). Dessutom utformar vi en ny objektdetekteringsalgoritm som bara använder geometri för DICU utan att använda det lokala utseendet. Vi organiserar bildobjekt som kurvkedjor och för att upptäcka ett objekt behöver vi endast söka efter denna kurvkedja i målbilden med hjälp av dynamisk programmering. Kurvkedjematchningen är oberoende av skala och rotationer samt robust vid bilddeformationer. Dessa egenskaper ger möjlighet att lösa problemet med rotationsberoende inom objektdetektering. Vårt ansiktsigenkänningsexperiment visar att kurvkedjematchning är oberoende av skala och rotationer och att den är mycket beräkningseffektiv.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2015. 206 p.
Series
Digital Media Lab, ISSN 1652-6295 ; 21
Keyword
scale-invariance, edge, corner, curve, line, matching, object detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Signal Processing
Identifiers
urn:nbn:se:umu:diva-111175 (URN)978-91-7601-353-3 (ISBN)
Public defence
2015-11-27, MIT-huset, MA121, Umeå universitet, Umeå, 13:00 (English)
Opponent
Supervisors
Projects
INTRO – INteractive RObotics research network
Funder
EU, FP7, Seventh Framework Programme
Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2015-11-10Bibliographically approved
2. Pushing edge detection to the limit: towards building semantic features for human emotion recognition
Open this publication in new window or tab >>Pushing edge detection to the limit: towards building semantic features for human emotion recognition
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
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:nbn:se:umu:diva-116137 (URN)9789174597851 (ISBN)
Note

P. 29-74: Papers I-III

Available from: 2016-02-08 Created: 2016-02-08 Last updated: 2016-02-08Bibliographically approved

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