Model-based object recognition from a complex binary imagery using genetic algorithm

This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction

Line Detection Algorithm Using Freeman Criteria

提出了一种简单而高效的在二值图像中检测目标物体直线边界的算法 基于Freeman提出的关于数字直线的准则和数字直线的特征 ,得出线段元是数字直线的组成部分这一性质 基于该性质 ,该算法以线段元为基本单位进行直线的构造 ,从而能高效、准确地检测出图像中物体边界中的直线 此外 ,该算法还可用于检测二值图像中物体边界的拐角This paper proposes a simple and efficient algorithm to detect line edge of objects in a binary image. Based on the criteria and characteristic of digital line suggested by Freeman, we derived that digital line is composed of sets of line segment cell. Derived from this property of line cell, in the algorithm proposed, line cells are used for connection to form line segment. It makes the algorithm very efficient and precise. This algorithm can also be used to detect corner of objects in binary images

The MIN PFS problem and piecewise linear model estimation

AbstractWe consider a new combinatorial optimization problem related to linear systems (MIN PFS) that consists, given an infeasible system, in finding a partition into a minimum number of feasible subsystems. MIN PFS allows formalization of the fundamental problem of piecewise linear model estimation, which is an attractive alternative when modeling a wide range of nonlinear phenomena. Since MIN PFS turns out to be NP-hard to approximate within every factor strictly smaller than 3/2 and we are mainly interested in real-time applications, we propose a greedy strategy based on randomized and thermal variants of the classical Agmon–Motzkin–Schoenberg relaxation method for solving systems of linear inequalities. Our method provides good approximate solutions in a short amount of time. The potential of our approach and the performance of our algorithm are demonstrated on two challenging problems from image and signal processing. The first one is that of detecting line segments in digital images and the second one that of modeling time-series using piecewise linear autoregressive models. In both cases the MIN PFS-based approach presents various advantages with respect to conventional alternatives, including wider range of applicability, lower computational requirements and no need for a priori assumptions regarding the underlying structure of the data