Assessing relative performance of edge detection algorithms
Edge detection is one of the important parts of image processing. It is essentially involved in the re-processing stage of image analysis and computer vision. It generally
detects the contour of an image and thus provides important details about an image. So, it reduces the content to process for the high-level processing tasks like object recognition and image segmentation. The most important step in the edge detection, on which the success of generation of true edge map depends, lies on the determination of threshold. In this work, purpose of edge detection, inspired from Ant Colonies, is fulfilled by Ant Colony
Optimisation (ACO). For the determination of threshold calculation, a novel technique of Fisher ratio (F-ratio) is used. The success of the work done is tested visually with the help of test images and empirically tested on the basis of several statistical parameter of comparison. De-noising is the process of extracting the important features present in an image, keeping the unnecessary or unimportant information present in the form of noise out as much as possible. There are many Denoising methods that have been developed in these field, but the most trustworthy and used among them is the wavelet thresholding denoising method with hard thresholding. The proposed novel method presented in this thesis is tested on the denoised images. The Edge detected images obtained on the denoised images are showing better results than the other conventional edge detectors.
In this thesis, we formulate the edge-detection problem in a ..
(2016)Intruder detection using histogram and edge detection algorithms. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang.
The results demonstrated that the use of these criteria can be utilized as an aid for further analysis and arbitration to find the best edge detector for a given image.
“A Wav elet Approach to Edge Detection, ” Thesis
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This paper describes edge detection as a composition of four steps: conditioning, feature extraction, blending, and scaling. We examine the role of geometry in determining good features for edge detection and in setting parameters for functions to blend the features. We find that: (1) statistical features such as the range and standard deviation of window intensities can be as effective as more traditional features such as estimates of digital gradients; (2) blending functions that are roughly concave near the origin of feature space ran provide visually better edge images than traditional choices such as the city-block and Euclidean norms; (3) geometric considerations ran be used to specify the parameters of generalized logistic functions and Takagi-Sugeno input-output systems that yield a rich variety of edge images; and (4) understanding the geometry of the feature extraction and blending functions is the key to using models based on computational learning algorithms such as neural networks and fuzzy systems for edge detection. Edge images derived from a digitized mammogram are given to illustrate various facets of our approach.
Image edge detection using ant colony optimization algorithm
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