No abstract

Principal components analysis (PCA) is one of a family of techniques for taking high-dimensional data, and using the dependencies between the variables to represent it in a more tractable, lower-dimensional form, without losing too much information. PCA is one of the simplest and most robust ways of doing such dimensionality reduction. It is also one of the best, and has been rediscovered many times in many fields, so it is also known as the Karhunen-Lo_eve transformation, the Hotelling transformation, the method of empirical orthogonal functions, and singular value decomposition.

An image is considered as a set of pixels that are connected in such a manner to form a boundary between two disjoints regions. Typically, the edge detection approach goes through the segmentation process by segmenting an image into regions of discontinuity. Hence it is a technique for marking sharp intensity changes. In this paper, it presents the Ant Colony Optimization based mechanism to compensate broken edges. There are various traditional edge detection techniques as Prewitt, Robert, Sobel, Marr Hildrith and Canny operators. On comparing them, it can be seen that Canny edge detector performs better than all other edge detectors on aspects such as it is adaptive in nature, generally performs better for noisy image by giving sharp images. Also it has been seen that remainders of pheromone trail as compensable edges are needed after finite iterations. Experimental results prove that compared to traditional image edge detection operators, the proposed Ant Colony Optimization(ACO) approach is very efficient in broken edges and more efficient than the traditional ones. The proposed ACO-based edge detection approach is to establish particularly a pheromone matrix that represents the edge information presented at each pixel of the image, according to the movements of a number of ants which are supposed to be dispatched in order to move on the image.

Thresholding algorithms are quite easy and effective for bilevel thresholding but in case of multilevel thresholding, the performance becomes unreliable due to complexity in computation because the complexity will exponentially increase. In this approach, multilevel thresholding is done for comparison by taking help of Otsu's clustering method and PSO clustering method. A dendogram of gray levels is created based on histogram of an image. The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multi-level thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected number of clusters.

No abstract