Determining maximum k-width-connectivity on meshes

AbstractLet I be a n × n binary image stored in a n × n mesh of processors with one pixel per processor. Image I is k-width-connected if, informally, between any pair of 1-pixels there exists a path of width k (composed of 1-pixels only). We consider the problem of determining the largest integer k such that I is k-width-connected, and present an optimal O(n) time algorithm for the mesh architecture

Training cellular automata for image processing

Abstract — Experiments were carried out to investigate the possibility of training cellular automata (CA) to perform several image processing tasks. Even if only binary images are considered the space of all possible rule sets is still very large, and so the training process is the main bottleneck of such an approach. In this paper the sequential floating forward search method for feature selection was used to select good rule sets for a range of tasks, namely: noise filtering (also applied to gray scale images using threshold decomposition), thinning, and convex hulls. Various objective functions for driving the search were considered. Several modifications to the standard CA formulation were made (the B-rule and 2-cycle CAs) which were found in some cases to improve performance. I

Computing convexity properties of images on a pyramid computer

We present efficient parallel algorithms for using a pyramid computer to determine convexity properties of digitized black/white pictures and labeled figures. Algorithms are presented for deciding convexity, identifying extreme points of convex hulls, and using extreme points in a variety of fashions. For a pyramid computer with a base of n simple processing elements arranged in an n 1/2 × n 1/2 square, the running times of the algorithms range from Θ(log n ) to find the extreme points of a convex figure in a digitized picture, to Θ( n 1/6 ) to find the diameter of a labeled figure, Θ( n 1/4 log n ) to find the extreme points of every figure in a digitized picture, to Θ( n 1/2 ) to find the extreme points of every labeled set of processing elements. Our results show that the pyramid computer can be used to obtain efficient solutions to nontrivial problems in image analysis. We also show the sensitivity of efficient pyramid-computer algorithms to the rate at which essential data can be compressed. Finally, we show that a wide variety of techniques are needed to make full and efficient use of the pyramid architecture.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41351/1/453_2005_Article_BF01759066.pd

Image Understanding by Hierarchical Symbolic Representation and Inexact Matching of Attributed Graphs

We study the symbolic representation of imagery information by a powerful global representation scheme in the form of Attributed Relational Graph (ARG), and propose new techniques for the extraction of such representation from spatial-domain images, and for performing the task of image understanding through the analysis of the extracted ARG representation. To achieve practical image understanding tasks, the system needs to comprehend the imagery information in a global form. Therefore, we propose a multi-layer hierarchical scheme for the extraction of global symbolic representation from spatial-domain images. The proposed scheme produces a symbolic mapping of the input data in terms of an output alphabet, whose elements are defined over global subimages. The proposed scheme uses a combination of model-driven and data-driven concepts. The model- driven principle is represented by a graph transducer, which is used to specify the alphabet at each layer in the scheme. A symbolic mapping is driven by the input data to map the input local alphabet into the output global alphabet. Through the iterative application of the symbolic transformational mapping at different levels of hierarchy, the system extracts a global representation from the image in the form of attributed relational graphs. Further processing and interpretation of the imagery information can, then, be performed on their ARG representation. We also propose an efficient approach for calculating a distance measure and finding the best inexact matching configuration between attributed relational graphs. For two ARGs, we define sequences of weighted error-transformations which when performed on one ARG (or a subgraph of it), will produce the other ARG. A distance measure between two ARGs is defined as the weight of the sequence which possesses minimum total-weight. Moreover, this minimum-total weight sequence defines the best inexact matching configuration between the two ARGs. The global minimization over the possible sequences is performed by a dynamic programming technique, the approach shows good results for ARGs of practical sizes. The proposed system possesses the capability to inference the alphabets of the ARG representation which it uses. In the inference phase, the hierarchical scheme is usually driven by the input data only, which normally consist of images of model objects. It extracts the global alphabet of the ARG representation of the models. The extracted model representation is then used in the operation phase of the system to: perform the mapping in the multi-layer scheme. We present our experimental results for utilizing the proposed system for locating objects in complex scenes

A quadtree approach to parallel image processing

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 93-95).by Hany S. Saleeb.M.Eng