INTELLIGENT MACHINE VISION SYSTEM FOR ROAD TRAFFIC SIGN RECOGNITION

Abstract We proposed an intelligent machine vision system to recognize traffic signs captured from a video camera installed in a vehicle. By recognizing the traffic signs automatically, it helps the driver to recognize the signs properly when drivig, to avoid accidents caused by mis-recognized the traffic signs.The system is divided into two stages : detection stage to localize signs from a whole image, and classification stage that classifies the detected sign into one of the reference signs. A geometric fragmentation technique, a method somewhat similar to Genetic Algorithm (GA) is employed to detect circular sign. Then a ring partitioned method that divides an image into several ring-shaped areas is used to classify the signs. From the experimental results, the proposed techniques are able to recognize traffic sign images under the problems of illumination changes, rotation, and occlusion efficiently. Keywords : Machine vision, traffic sign recognition, geometric fragmentation, ring partitioned matching

Fitting an Origin-Displaced Logarithmic Spiral to Empirical Data by Differential Evolution Method of Global Optimization

Logarithmic spirals are abundantly observed in nature. Gastropods/cephalopods (such as nautilus, cowie, grove snail, thatcher, etc.) in the mollusca phylum have spiral shells, mostly exhibiting logarithmic spirals vividly. Spider webs show a similar pattern. The low-pressure area over Iceland and the Whirlpool Galaxy resemble logarithmic spirals.Many materials develop spiral cracks either due to imposed torsion (twist), as in the spiral fracture of the tibia, or due to geometric constraints, as in the fracture of pipes. Spiral cracks may, however, arise in situations where no obvious twisting is applied; the symmetry is broken spontaneously. It has been found that the rank size pattern of the cities of USA approximately follows logarithmic spiral. The usual procedure of curve-fitting fails miserably in fitting a spiral to empirical data. The difficulties in fitting a spiral to data become much more intensified when the observed points z = (x, y) are not measured from their origin (0, 0), but shifted away from the origin by (cx, cy). We intend in this paper to devise a method to fit a logarithmic spiral to empirical data measured with a displaced origin. The optimization has been done by the Differential Evolution method of Global Optimization. The method is also be tested on numerical data. It appears that our method is successful in estimating the parameters of a logarithmic spiral. However, the estimated values of the parameters of a logarithmic spiral (a and b in r = a*exp(b(theta+2*pi*k) are highly sensitive to the precision to which the shift parameters (cx and cy) are correctly estimated. The method is also very sensitive to the errors of measurement in (x, y) data. The method falters when the errors of measurement of a large magnitude contaminate (x, y). A computer program (Fortran) is appended

Fast and Robust Traffic Sign Detection

This paper deals with the fast and robust detection of the traffic sign images. A new technique called geometric fragmentation is proposed to detect the red circular traffic signs. It detects the outer ellipses of the signs by combining the left and right fragments of the ellipse objects. A search based on the geometric fragmentation is used to find the ellipse fragments. This search is somewhat similar to genetic algorithm (GA) in the sense that it employs the terms of individual, population, crossover, and objective function usually used in GA. To increase the accuracy and reduce the computational time, a new objective function is introduced for evaluating the individuals. The algorithm was tested for detecting the red circular traffic signs from the real scene image. The experimental results show that the proposed algorithm has a higher detection rate with a lower computational cost compared with the referential genetic algorithm-based ellipse detection

Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs