Improvements to the TCVD method to segment hand-drawn sketches. (March 2017)
- Record Type:
- Journal Article
- Title:
- Improvements to the TCVD method to segment hand-drawn sketches. (March 2017)
- Main Title:
- Improvements to the TCVD method to segment hand-drawn sketches
- Authors:
- Albert, F.
Aleixos, N. - Abstract:
- Abstract: Tangent and Corner Vertices Detection (TCVD) is a method to detect corner vertices and tangent points in sketches using parametric cubic curves approximation, which is capable to detect corners with a high accuracy and a very low false positive rate, and also to detect tangent points far above other methods in literature. In this article, we present several improvements to TCVD method in order to establish mathematical conditions to detect corners and make the obtaining of curves independent from the scale, what increases the success ratio in transitions between lines and curves. The new conditions for obtaining corners use the radius as the inverse of the curvature, and the second derivative of the curvature. For the detection of curves, a new descriptor is presented, avoiding the parameters dependent of scale used in TCVD method. In order to obtain the performance of the implemented improvements, several tests have been carried out using a dataset which contains sketches more complex than those used for validation of TCVD algorithm (sketches with more curves and tangent points and sketches of different sizes). For corners detection, the accuracy obtained was pretty similar to that obtained with the previous TCVD, however, for curves and tangent points detection the accuracy increases significantly. Highlights: This paper presents two important improvements to earlier TCVD method, TCVD-Revised. TCVD-R approximates the stroke to piece wise parametric cubic curves.Abstract: Tangent and Corner Vertices Detection (TCVD) is a method to detect corner vertices and tangent points in sketches using parametric cubic curves approximation, which is capable to detect corners with a high accuracy and a very low false positive rate, and also to detect tangent points far above other methods in literature. In this article, we present several improvements to TCVD method in order to establish mathematical conditions to detect corners and make the obtaining of curves independent from the scale, what increases the success ratio in transitions between lines and curves. The new conditions for obtaining corners use the radius as the inverse of the curvature, and the second derivative of the curvature. For the detection of curves, a new descriptor is presented, avoiding the parameters dependent of scale used in TCVD method. In order to obtain the performance of the implemented improvements, several tests have been carried out using a dataset which contains sketches more complex than those used for validation of TCVD algorithm (sketches with more curves and tangent points and sketches of different sizes). For corners detection, the accuracy obtained was pretty similar to that obtained with the previous TCVD, however, for curves and tangent points detection the accuracy increases significantly. Highlights: This paper presents two important improvements to earlier TCVD method, TCVD-Revised. TCVD-R approximates the stroke to piece wise parametric cubic curves. New mathematical conditions to detect corners using a new discriminatory function. Obtaining of curves independent from the scale by means of two new parameters. TCVD-R improves accuracy in corner and tangent points over earlier TCVD approach. … (more)
- Is Part Of:
- Pattern recognition. Volume 63(2017:Mar.)
- Journal:
- Pattern recognition
- Issue:
- Volume 63(2017:Mar.)
- Issue Display:
- Volume 63 (2017)
- Year:
- 2017
- Volume:
- 63
- Issue Sort Value:
- 2017-0063-0000-0000
- Page Start:
- 416
- Page End:
- 426
- Publication Date:
- 2017-03
- Subjects:
- Corner vertices detection -- Tangent points detection -- Sketch recognition -- Stroke segmentation -- Curvature functions -- Natural interfaces
Pattern perception -- Periodicals
Perception des structures -- Périodiques
Patroonherkenning
006.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00313203 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.patcog.2016.10.024 ↗
- Languages:
- English
- ISSNs:
- 0031-3203
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12847.xml