Recognition of License Plates and Optical Nerve Pattern Detection Using Hough Transform

The global technique of detection of the features is Hough transform used in image processing, computer vision and image analysis. The detection of prominent line of the object under consideration is the main purpose of the Hough transform which is carried out by the process of voting. The first part of this work is the use of Hough transform as feature vector, tested on Indian license plate system, having font of UK standard and UK standard 3D, which has ten slots for characters and numbers.So tensub images are obtained.These sub images are fed to Hough transform and Hough peaks to extract the Hough peaks information. First two Hough peaks are taken into account for the recognition purposes. The edge detection along with image rotation is also used prior to the implementation of Hough transform in order to get the edges of the gray scale image. Further, the image rotation angle is varied; the superior results are taken under consideration. The second part of this work makes the use of Hough transform and Hough peaks, for examining the optical nerve patterns of eye. An available database for RIM-one is used to serve the purpose. The optical nerve pattern is unique for every human being and remains almost unchanged throughout the life time. So the purpose is to detect the change in the pattern report the abnormality, to make automatic system so capable that they can replace the experts of that field. For this detection purpose Hough Transform and Hough Peaks are used and the fact that these nerve patterns are unique in every sense is confirmed

Subpixel Detection of Circular Objects using Geometric Property

In this paper, we propose a method for detecting circular shapes with subpixel accuracy. First, the geometric properties of circles have been used to find the diameters as well as the circumference pixels. The center and radius are then estimated by the circumference pixels. Both synthetic and real images have been tested by the proposed method. The experimental results show that the new method is efficient

Automatic Detection of Circular Objects by Ellipse Growing

We present a new method for automatically detecting circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space is restricted to one and two dimensions for efficiency, and special weighting schemes are introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3-D object shape reconstruction

Pattern Recognition and Event Reconstruction in Particle Physics Experiments

This report reviews methods of pattern recognition and event reconstruction used in modern high energy physics experiments. After a brief introduction into general concepts of particle detectors and statistical evaluation, different approaches in global and local methods of track pattern recognition are reviewed with their typical strengths and shortcomings. The emphasis is then moved to methods which estimate the particle properties from the signals which pattern recognition has associated. Finally, the global reconstruction of the event is briefly addressed.Comment: 101 pages, 58 figure

Using FPGA Co-processors for Improving the execution Speed of Pattern Recognition Algorithms in ATLAS LVL2 Trigger

In the scope of this thesis one of the possible approaches to acceleration the tracking algorithms using the hybrid FPGA/CPU systems has been investigated. The TRT LUT-Hough algorithm - one of the tracking algorithms for ATLAS Level2 trigger - is selected for this purpose. It is a Look-Up Table (LUT) based Hough transform algorithm for Transition Radiation Tracker (TRT). The algorithm was created keeping in mind the B-physic's tasks: fast search for low-pT tracks in entire TRT volume. Such a full subdetector scan requires a lot of computational power. Hybrid implementation of the algorithm (when the most time consuming part of algorithm is accelerated by FPGA co-processor and all other parts are running on a general purpose CPU) is integrated in the same software framework as a C++ implementation for comparison. Identical physical results are obtained for both the CPU and the Hybrid implementations. Timing measurements results show that a critical part, is implemented in VHDL runs on the FPGA co-processor ~4 times faster than on the more or less modern CPU (Intel Xeon 2.4 GHz ) and the whole algorithm runs ~2 times faster