PENGGUNAAN ALGORITMA FAST CONNECTIVE HOUGH TRANSFORM DAN ANALISIS HISTOGRAM UNTUK MENENTUKAN LOKASI PLAT NOMOR

Proses lokalisasi plat nomor adalah tahapan yang sangat menentukan akurasi dari sebuah sistem pembacaan plat nomor karena keakuratan proses lokalisasi menjadi penentu keberhasilan tahapan-tahapan selanjutnya. Pada saat ini telah ada beberapa algoritma yang dapat melakukan proses lokalisasi dengan baik dan salah satunya adalah algoritma analisis histogram. Algoritma analisis histogram akan medeteksi garis menggunakan sobel edge detector lalu menghitung akumulasi nilai piksel pada sumbu x dan y sebagai dasar perhitungan penentuan lokasi plat nomor. Hasil dari pendeteksian garis ini seringkali mengandung banyak noise yang dapat mengganggu proses analisa histogram dan pada akhirnya mengurangi tingkat akurasi. Pada penelitian ini akan dilakukan eliminasi noise tersebut dengan memanfaatkan algoritma Fast Hough Connective Transfrom yang akan melakukan ekstraksi panjang dan sudut orientasi garis dari hasil pendeteksian garis dengan menggunakan kedua hal tersebut sebagai kriteria. Hasil pengujian membuktikan bahwa proses eliminasi noise ini dapat menghilangkan hingga 78% noise dan menghasilkan akurasi hingga 97%. Kata Kunci: ANPR, Pendeteksi garis sobel, FCHT, Analisis histogram. Plate number localization is a crucial step that determines the success rate of an automated plate number recognition because the succession of this step became the determining factor of the next steps in the system. There are several methods that can localize the plate number and one amongst them are histogram analysis. This method will detect the existence of lines using sobel edge detector then accumulate pixel’s value on axis x and y as a base to find the plate. The result of line detection often contains lots of noise that can disturb the process of histogram analysis and causing the algorithm to fail. This research presents a method to eliminate the noise by utilizing fast connective hough transform that will extract the length and orientation of lines from sobel edge detector and using those two information as criteria to eliminate noise. The result of experiment proved that this method can eliminate as much as 78% of noise and can correctly localize 97% of plate number in the testing data. Keyword: ANPR, Sobel edge detector, FCHT, Histogram analisys

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

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

Implementation of a real time Hough transform using FPGA technology

This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

Image feature analysis using the Multiresolution Fourier Transform

The problem of identifying boundary contours or line structures is widely recognised as an important component in many applications of image analysis and computer vision. Typical solutions to the problem employ some form of edge detection followed by line following or, more commonly in recent years, Hough transforms. Because of the processing requirements of such methods and to try to improve the robustness of the algorithms, a number of authors have explored the use of multiresolution approaches to the problem. Non-parametric, iterative approaches such as relaxation labelling and "Snakes" have also been used. This thesis presents a boundary detection algorithm based on a multiresolution image representation, the Multiresolution Fourier Transform (MFT), which represents an image over a range of spatial/spatial-frequency resolutions. A quadtree based image model is described in which each leaf is a region which can be modelled using one of a set of feature classes. Consideration is given to using linear and circular arc features for this modelling, and frequency domain models are developed for them. A general model based decision process is presented and shown to be applicable to detecting local image features, selecting the most appropriate scale for modelling each region of the image and linking the local features into the region boundary structures of the image. The use of a consistent inference process for all of the subtasks used in the boundary detection represents a significant improvement over the adhoc assemblies of estimation and detection that have been common in previous work. Although the process is applied using a restricted set of local features, the framework presented allows for expansion of the number of boundary feature models and the possible inclusion of models of region properties. Results are presented demonstrating the effective application of these procedures to a number of synthetic and natural images

A graph theoretic approach to transputer network design for computer vision

The work in this thesis is concerned with parallel architectures based on the Inmos transputer-type processors and parallelisation of some computer vision tasks chosen from low to high level. The transputer is a microprocessor with a micro-programmed scheduler and four serial communication links. It directly supports parallel processing since several transputers can be connected through their links to co-operate on solving a problem. Also several processes can be run on the same transputer. A major issue in parallel processing is the communication overhead introduced by parallelising a given task. This overhead is not present in sequential processing and must be curbed if the implementation of a task on a parallel machine is to be successful. The interconnection network underlying the architecture of a parallel computer is therefore of the utmost importance. Computer Vision consists of a hierarchy of tasks ranging from low-level operations dealing with large amounts of relatively simple data to high level operations handling increasingly complex structures. In this work a novel edge detector based on adaptive filtering and an edge detector operating on colour images are presented and implemented on a number of transputers. These parallel implementations together with implementations of vector quantisation, Fourier descriptors for shape discrimination, the Hough transform and the Maximum clique algorithm, offer a notable performance increase when compared with sequential implementations. However, every algorithm required the design of a specific network of transputers to take advantage of the parallelism and data dependencies inherent in each. Consequently, attention is focused on the topology of interconnection networks. In particular, the communication requirements of computer vision algorithms as identified by the various computer vision tasks are analysed. These requirements together with graph theoretical considerations are then used to suggest a topology for large transputer networks. The latter is based on sub-graphs, with proven performance when used to implement interconnection networks, combined to form an architecture with improved performance. This architecture consists of a fixed structure supplemented with a dynamically reconfigured network. After describing this topology, a routing algorithm that conveys messages along shortest paths in the network is given and implemented. And finally, some practical issues in the use of transputers are considered and solutions proposed

Object Detection Using Hough Transform

Tato diplomová práce se zabývá problematikou detekce objektů pomocí matematické techniky zvané Houghova transformace. Techniku Houghovy transformace pojímá z obecného hlediska od de facto nejjednoduššího užití pro detekci elementárních analyticky popsatelných útvarů jako jsou přímky, elipsy, kružnice či jednoduché analyticky definovatelné prvky až po sofistikované užití pro detekci komplexních - analyticky prakticky nepopsatelných - objektů. Mezi ně patří například automobily či chodci, kteří se detekují na základě předložených fotografických záznamů těchto objektů a entit. Dokument tedy mapuje definice a použití jednotlivých subtechnik Houghovy transformace spolu s jejich základním členěním na pravděpodobnostní a nepravděpodobnostní metody. Práce následně vrcholí popisem obecné state-of-the-art metody zvané Třídně-specifické Houghovy lesy pro detekci objektů, uvádí její definici, postup trénovaní na základě poskytnutého datasetu a detekce z testovacích obrazců. V závěru této práce je pak navrhnut a implementován obecně trénovatelný detektor objektů využívající tuto techniku. A je experimentálně vyhodnocena jeho úspěšnost.This diploma thesis deals with object detection using mathematical technique called Hough transform. Hough transform technique is conceived in general terms from the de facto simplest use for the detection of elementary analytically describable shapes such as lines, ellipses, circles or simple analytically definable elements to sophisticated use for the detection of complex - analytically virtually indescribable - objects. These include cars or pedestrians who are detected on the basis of the photographic records of these objects and entities. The document thus maps the definition and use of the respective Hough transform subtechniques along with their basic classification on probabilistic and non-probabilistic methods. The work subsequently culminates in describing the general state-of-the-art technique called Class-Specific Hough Forests for Object Detection, introduces its definition, training procedure on a provided dataset and the detection of test patterns. In conclusion of this work,there is designed and implemented generally trainable object detector using this technique. And there is experimental evaluation of its quality.

Personal Identification Based on Live Iris Image Analysis

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