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Distinctive curves: unified scale-invariant detection of edges, corners, lines and curves
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)
(English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042Article in journal (Refereed) Submitted
Abstract [en]

This paper aims to broaden the scope of shape related features including edges, corners, lines and curves: 1) Edges, corners, lines, curves are all shape related features. In the past, the detection of each type of feature is usually solved independently under certain hypotheses. Our proposed distinctive curve detection method (DICU) solves the detection of all these type of features together. 2) Compared to the development in scale-invariant interest point detectors which have adopted more objective robustness measures using repeatability score, the research in line and curve features is still limited to “true/false positive” measures. DICU detection utilizes the scale-space concept and proves that curve features can be as robust as scale-invariant interest points. DICU has three advantages: 1) DICU outputs multi-type features which can benefit future computer vision applications. At the same time, the computational efficiency is unaffected, after detecting edges, only 5% additional computation is needed to detect corners, lines, and curves. 2) It is robust under various image perturbations and transformations and outperforms state-of-the-art interest point detectors and line detectors. At the same time, all types of detected features are robust. 3) Curve features contains more geometric information than points. Our curve matching test shows that curve matching can outperform interest point matching. 

Keyword [en]
curve, line, corner, feature matching, scale-invariance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Signal Processing
URN: urn:nbn:se:umu:diva-111186OAI: diva2:867849
Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2016-02-23
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.
Digital Media Lab, ISSN 1652-6295 ; 21
scale-invariance, edge, corner, curve, line, matching, object detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Signal Processing
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)
INTRO – INteractive RObotics research network
EU, FP7, Seventh Framework Programme
Available from: 2015-11-06 Created: 2015-11-06 Last updated: 2015-11-10Bibliographically approved

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