Duplicate document detection is the problem of finding all document-pairs rapidly whose similarities are equal to or greater than a given threshold. There is a method proposed recently called prefix-filter that finds document-pairs whose similarities never reach the threshold based on the number of uncommon terms (words/characters) in a document-pair and removes them before similarity calculati...

Edge detection is a fundamental procedure in image processing, machine vision, and computer vision. Its application area ranges from astronomy to medicine in which isolating the objects of interest in the image is of a significant importance. However, performing edge detection is a non-trivial task for which a large number of techniques have been proposed to solve it. This paper investigates th...

Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image’s intensity val...

This survey focuses on the problem of parameters selection in image edge detection by ant colony optimization (ACO) algorithm. By introducing particle swarm optimization (PSO) algorithm to optimize parameters in ACO algorithm, the fitness function based on connectivity of image edge is proposed to evaluate the quality of parameters in ACO algorithm. And the ACO-PSO algorithm is applied to image...

Boundary detection in two-dimensional images is an important problem in computer vision. There are a wide variety of algorithms to accomplish this task, but none have come close to human proficiency. We explore a variety of ways to use machine learning algorithms to combine existing boundary detection algorithms with the goal of exceeding the performance of any particular algorithm. We present ...

We consider a general notion of the Laplacian of a graph. The weight of an edge reflects both the width and the length of an edge. Further, we allow the edge weights to vary in order to minimize the maximum eigenvalue, and using this minimum we construct the so-called σ−function of a graph. We consider a geometric interpretation of the σ−function, in particular as it applies to the detection of...

This paper proposed an edge detection scheme which is deduced from Fresnel diffraction. Analysis in this paper shows that Fresnel convolution kernel function performs well on edge enhancement when images are transformed into complex functions. Due to its mathematical complexity, the method is simplified into a linear convolution filter. The new edge detector is designed based on the simplified ...

Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CF...

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do no...

A wavelet-based multiscale edge detection scheme is presented in this paper. By multiplying the wavelet coefficients at two adjacent scales to magnify significant structures and suppress noise, we determined edges as the local maxima directly in the scale product after an efficient thresholding instead of first forming the edge maps at several scales and then synthesizing them together, which w...