Cosmin Grigorescu

Contour detection based on nonclassical receptive field inhibition

We propose a biologically motivated method, called nonclassical receptive field (non-CRF) inhibition (more generally, surround inhibition or suppression), to improve contour detection in machine vision. Non-CRF inhibition is exhibited by 80% of the orientation-selective neurons in the primary visual cortex of monkeys and has been shown to influence human visual perception as well. Essentially, the response of an edge detector at a certain point is suppressed by the responses of the operator in the region outside the supported area. We combine classical edge detection with isotropic and anisotropic inhibition, both of which have counterparts in biology. We also use a biologically motivated method (the Gabor energy operator) for edge detection. The resulting operator responds strongly to isolated lines, edges, and contours, but exhibits weak or no response to edges that are part of texture. We use natural images with associated ground truth contour maps to assess the performance of the proposed operator for detecting contours while suppressing texture edges. Our method enhances contour detection in cluttered visual scenes more effectively than classical edge detectors used in machine vision (Canny edge detector). Therefore, the proposed operator is more useful for contour-based object recognition tasks, such as shape comparison, than traditional edge detectors, which do not distinguish between contour and texture edges. Traditional edge detection algorithms can, however, also be extended with surround suppression. This study contributes also to the understanding of inhibitory mechanisms in biology.

Contour and boundary detection improved by surround suppression of texture edges

We propose a computational step, called surround suppression, to improve detection of object contours and region boundaries in natural scenes. This step is inspired by the mechanism of non-classical receptive field inhibition that is exhibited by most orientation selective neurons in the primary visual cortex and that influences the perception of groups of edges or lines. We illustrate the principle and the effect of surround suppression by adding this step to the Canny edge detector. The resulting operator responds strongly to isolated lines and edges, region boundaries, and object contours, but exhibits a weaker or no response to texture edges. Additionally, we introduce a new post-processing method that further suppresses texture edges. We use natural images with associated subjectively defined desired output contour and boundary maps to evaluate the performance of the proposed additional steps. In a contour detection task, the Canny operator augmented with the proposed suppression and post-processing step achieves better results than the traditional Canny edge detector and the SUSAN edge detector. The performance gain is highest at scales for which these latter operators strongly react to texture in the input image.

Distance sets for shape filters and shape recognition

We introduce a novel rich local descriptor of an image point, we call the (labeled) distance set, which is determined by the spatial arrangement of image features around that point. We describe a two-dimensional (2D) visual object by the set of (labeled) distance sets associated with the feature points of that object. Based on a dissimilarity measure between (labeled) distance sets and a dissimilarity measure between sets of (labeled) distance sets, we address two problems that are often encountered in object recognition: object segmentation, for which we formulate a distance sets shape filter, and shape matching. The use of the shape filter is illustrated on printed and handwritten character recognition and detection of traffic signs in complex scenes. The shape comparison procedure is illustrated on handwritten character classification, COIL-20 database object recognition and MPEG-7 silhouette database retrieval.

Improved Contour Detection by Non-classical Receptive Field Inhibition

We propose a biologically motivated computational step, called nonclassical receptive field (non-CRF) inhibition, to improve the performance of contour detectors. We introduce a Gabor energy operator augmented with non-CRF inhibition, which we call the bar cell operator. We use natural images with associated ground truth edge maps to assess the performance of the proposed operator regarding the detection of object contours while suppressing texture edges. The bar cell operator consistently outperforms the Canny edge detector.

Contour detection operators based on surround inhibition

We propose a biologically motivated computational step, called non-classical receptive field (non-CRF) inhibition, to improve contour detection in images of natural scenes. We augment a Gabor energy operator with non-CRF inhibition. The resulting contour operator responds strongly to isolated lines, edges, and contours but exhibits a weaker or no response to edges that make part of texture. As such, the contour operator is more useful for contour-based object recognition tasks than traditional edge detectors, which do not make such a distinction. The contour operator consistently outperforms the Canny edge detector on natural images with associated ground truth contour maps.

Graph-based features for texture discrimination

Graph-based features, such as the number of connected components, edges of a given orientation and vertices per unit area, and the number of vertices and pixels per connected component, are proposed for the analysis of textures which consist of structural elements. The proposed set of features is compared with features obtained by a typical filter-based scheme which makes use of Gabor filters. The discrimination properties of the two types of features are assessed by evaluating the separability of sets of feature vectors which are derived from different types of texture using the Mahalanobis distance. The graph-based features are shown to be superior to the filter-based features for the class of concerned textures. They are particularly suited for discrimination between textures which have the same spatial and orientation regularity but consist of elements of different forms.