We propose a biologically motivated computational step, called surround inhibition, which improves the performance of contour detectors. The mechanism of surround inhibition is exhibited by the majority of the orientation selective neurons in the primary visual cortex of macaque monkeys and has been demonstrated to influence the visual perception of man as well. We illustrate the principle and ...

A trademark image retrieval (TIR) system is proposed in this work to deal with the vast number of trademark images in the trademark registration system. The proposed approach commences with the extraction of edges using the Canny edge detector, performs a shape normalization procedure, and then extracts the global and local features. The global features capture the gross essence of the shapes w...

A new method for evaluating edge detection algorithms is presented and applied to measure the relative performance of algorithms by Canny, Nalwa-Binford, Iverson-Zucker, Bergholm, and Rothwell. The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. The process of evaluating edge detection algorithms with this perfo...

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graphbased approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine lear...

Since cell biologists need to use image processing techniques, such as edge detection, to analyze cell images the precision of these techniques is pivotal to their work. Due to the often low quality of cell images, existing edge detectors fail to routinely produce highly accurate results. In this paper, a novel hybrid method based on the canny operator and fuzzy entropy theory is proposed. The ...

Abstract A new directional 3D edge detector designed for anisotropic image data is reported. The detector is based on interpolating the image intensity function in a small neighborhood of every voxel by a tri-cubic polynomial. The analytical approximation of the image intensity function is used to compute the intensity function gradients. The developed edge detector uses a maximum average of di...

This paper proposes the adaptation and optimization of four edge detector algorithms used for feature set extraction in 2D images. The paper compares the performance of Sobel, Canny, Roberts and Prewitt edge detectors and proposed better solution for feature extraction in image processing. It has been shown that the Canny edge detection algorithm performs best among Sobel, Roberts and Prewitt e...

In this paper, two algorithms for MRI segmentation are studied. K-means and canny edge detector. The objective of this paper is to perform a segmentation process on MR images of the human brain, using K-means Algorithm and canny Edge detection algorithm. K-means Clustering algorithm gives us the segmented image of an MRI having the same intensity regions. K-means Clustering segments all the thr...

Canny (1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny’s work suffers from two problems. First, his derivation of localization criterion is incorrec...

Advancing further the theory of the optimal edge detector, as has been developed by Canny and Spacek, we derive the 'definitive' optimal edge operator. We show that the cubic spline approximation is, in practice, as good as the optimal edge detector.