We consider the problem of improving contour detection by filling gaps between collinear contour pieces. A fast algorithm is proposed which takes into account local edge orientation and local curvature. Each edge point is replaced by a curved elongated patch, whose orientation and curvature match the local edge orientation and edge. The proposed contour completion algorithm is integrated in a multiresolution framework for contour detection. Experimental results show the superiority of the proposed method to other well-established approaches.
What visually distinguishes a painting from a photograph is often the absence of texture and the sharp edges: in many
paintings, edges are sharper than in photographic images while textured areas contain less detail. Such artistic effects can
be achieved by filters that smooth textured areas while preserving, or enhancing, edges and corners. However, not all
edge preserving smoothers are suitable for artistic imaging. This study presents a generalization of the well know
Kuwahara filter aimed at obtaining an artistic effect. Theoretical limitations of the Kuwahara filter are discussed and
solved by the new nonlinear operator proposed here. Experimental results show that the proposed operator produces
painting-like output images and is robust to corruption of the input image such as blurring. Comparison with existing
techniques shows situations where traditional edge preserving smoothers that are commonly used for artistic imaging fail
while our approach produces good results.
Canny edge detector is based both on local and global image analysis, present in the gradient computation and
connectivity-related hysteresis thresholding, respectively. This contribution proposes a generalization of these ideas.
Instead of the sole gradient magnitude, we consider several local statistics, to take into account how much texture is
present around each pixel. This information is used in biologically inspired surround inhibition of texture. Global
analysis is generalized by introducing a long range connectivity analysis. We demonstrate the effectiveness of our
approach by extensive experimentation.
In this paper we propose a multiscale biologically motivated technique for contour detection by texture suppression.
Standard edge detectors react to all the local luminance changes, irrespective whether they are due to the contours of the
objects represented in the scene, rather than to natural texture like grass, foliage, water, etc. Moreover, edges due to
texture are often stronger than edges due to true contours. This implies that further processing is needed to discriminate
true contours from texture edges. In this contribution we exploit the fact that, in a multiresolution analysis, at coarser
scales, only the edges due to object contours are present while texture edges disappear. This is used in combination with
surround inhibition, a biologically motivated technique for texture suppression, in order to build a contour detector which
is insensitive to texture. The experimental results show that our approach is also robust to additive noise.