Contour and boundary detection improved by surround suppression of texture edges

Abstract

Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

Motion detection, noise reduction, texture suppression, and contour enhancement by spatiotemporal Gabor filters with surround inhibition

“…It is a straightforward generalization of a model that was used in the case of a purely spatial filter Grigorescu et al 2003Grigorescu et al , 2004. The classical receptive field (CRF) of a model simple cell is defined as the area in which the (moving) Gaussian envelope of the corresponding 3D Gabor function g v,θ,ϕ (x, y, t) is substantial.…”

Image Segmentation Using a Sparse Coding Model of Cortical Area V1

“…For this reason, numerous neural network approaches have been proposed that are inspired by the physiology of the primary visual cortex (Ben-Shahar and Zucker, 2004;Grigorescu et al, 2003Grigorescu et al, , 2004Hansen and Neumann, 2008;Huang et al, 2009;Li, 1998;Mundhenk and Itti, 2005;Papari et al, 2007;Papari and Petkov, 2011;Petkov and Westenberg, 2003;Tang et al, 2007a,b;Ursino and La Cara, 2004;Vonikakis et al, 2006;Zeng et al, 2011a,b). A neuron in such a model has a classical receptive field (cRF), often defined using a Gabor function, that receives input from the image.…”

Detection of incomplete enclosures of rectangular shape in remotely sensed images

“…This technique extracts linear features, while suppressing texture elements of cluttered background. We also experimented with other approaches [20,10,28], but these are either not sensitive enough to extract faint edges of enclosures, or generate lots of clutter edges depending on the parameters used. The parameterless line segment detector [11], which is known to provide robust results for a large range of images, misses faint edges of enclosures.…”