Distinctive curve features
2016 (English)In: Electronics Letters, ISSN 0013-5194, E-ISSN 1350-911X, Vol. 52, no 3, 197-198 p.Article in journal (Other academic) Published
Curves and lines are geometrical, abstract features of an image. Whereas interest points are more limited, curves and lines provide much more information of the image structure. However, the research done in curve and line detection is very fragmented. The concept of scale space is not yet fused very well into curve and line detection. Keypoint (e.g. SIFT, SURF, ORB) is a successful concept which represent features (e.g. blob, corner etc.) in scale space. Stimulated by the keypoint concept, a method which extracts distinctive curves (DICU) in scale space, including lines as a special form of curve features is proposed. A curve feature can be represented by three keypoints (two end points, and one middle point). A good way to test the quality of detected curves is to analyse the repeatability under various image transformations. DICU using the standard Oxford benchmark is evaluated. The overlap error is calculated by averaging the overlap error of three keypoints on the curve. Experiment results show that DICU achieves good repeatability comparing with other state-of-the-art methods. To match curve features, a relatively uncomplicated way is to combine local descriptors of three keypoints on each curve.
Place, publisher, year, edition, pages
2016. Vol. 52, no 3, 197-198 p.
curve detection, line detection, feature matching
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject Signal Processing
IdentifiersURN: urn:nbn:se:umu:diva-111184DOI: 10.1049/el.2015.3495ISI: 000369674000014OAI: oai:DiVA.org:umu-111184DiVA: diva2:867829