Edge detection is one of the most fundamental operations in image processing and scene analysis systems because edges form the outline of an object. Edge detection is the procedure of detecting meaningful discontinuities of the image function and providing an effective means for image segmentation

image fusion

and pattern recognition. Gray image edge detection has been developed with relative saturation; however

the color image edge detection has not received the same attention. Up to now

most of the existing color image edge detection algorithms are monochromatic-based methods

which produce a superior effect than traditional gray-value methods. Both methods do not completely utilize the chromatic information. Meanwhile

vector-valued techniques treat the color information as color vectors in a vector space provided with a vector norm

thus solving such a problem. However

the vector-valued methods have high complexity and large computation. A color image with three components can be represented in quaternion form as pure quaternions

which can well preserve the vector features of the image pixels. Consequently

the edge detection algorithm combining the smallest univalue segment assimilating nucleus and quaternion in RGB space was proposed to deal with several problems in the traditional color image edge detection methods

such as the insufficient use of chromatic information in color images

the large amount of time and space consumption in the process of nonlinear transformation between color models

and the complex algorithm implementation. For a preferable color image edge detection result

we consider the algebraic operation and spatial characteristics regarding the quaternion

as well as the simple and effective edge detection performance of the SUSAN algorithm in our method. The steps of this approach can be summarized as follows. First

the color images are represented with pure quaternions and normalize each pixel. Second

edge detection is performed using the SUSAN operator

which generates a thick edge because of the constraint of the fixed geometry threshold ; hence

the Otsu algorithm is applied to adaptively capture the double geometry thresholds. Third

we make the edge growth on the weak edge set and determine the local edge direction according to the center of gravity and the longest axis of symmetry of SUSAN. Finally

we perform the local non-maximum suppression operation to obtain the final thinned edge image. Three classic color images and a synthetic color image with four blocks for specific colors are selected to make a comparison with other edge detection algorithms

including the color Canny algorithm

the SUSAN algorithm

and our method with a fixed threshold to demonstrate the effectiveness and robustness of our method. Two different forms of the geometric threshold are established in our method is to verify whether the selection of the threshold is influential to the final effect of the edge image. We used the Pratt quality factor to conduct a quantitative evaluation of edge positioning accuracy. Experiment results show that our method with less lost edges can detect the edges of different color regions with similar brightness

and the extracted edges are continuous and meticulous. In addition

for the color images with weak noise

our method is very robust that it still can effectively detect the real edge points. Compared with the color Canny algorithm which has the preferable effect of edge detection in color images

the quality factor of our method improved by 0.012 0

and the operation time was reduced by 2.527 9 s. In this thesis

we proposed an edge detection algorithm combining the smallest univalue segment assimilating nucleus and quaternion

realizing the effective fusion of quaternion and the SUSAN operator. Setting several different comparative experiments

subjective and objective evaluations show that our method effectively suppressed the weak noise and improved the accuracy of edge localization

which is really suitable for low-level color image processing with low demand in real-time.