A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tri-state median filter for image denoising
IEEE Transactions on Image Processing
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
A cellular coevolutionary algorithm for image segmentation
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Level set analysis for leukocyte detection and tracking
IEEE Transactions on Image Processing
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This paper describes the segmentation of nanoparticles of ZnO obtained by mechanical milling. Segmentation of objects in images is a common application of computer vision methods. In contrast to manual segmentation, these techniques are fast, objective, and accurate. We describe in this paper a method based on such techniques aimed at segmenting the particles in a microscopic image of ZnO in order to obtain an approximation of the grain size, and a measure of the homogeneity, in a non-supervised way. The images are obtained using scanning electron microscopy and then preprocessed to enhance the contrast and to reduce the noise. Next, an edge detection algorithm is applied to obtain the boundaries of the particles. Finally, the particles that satisfy a specific criterion are extracted and measured, and their measure is taken as an approximation of the particle size.