Automatic lineament analysis techniques for remotely sensed imagery

Imperial Users onl

Detector de contorns basat en el domini transformat

RESUM En aquest document es presenta un detector de contorns d’imatges basat en el domini transformat. A partir de la interpretació de la transformada de Fourier de la imatge i la seva formulació matricial en termes dels diferents modes, es realitza una selecció de les components passa baixes a partir de les quals es reconstrueix la component de baixa freqüència que es resta de la imatge original per tal d’obtenir el detector. Aquest detector de contorns no és esbiaixat. L’algorisme pot ser aplicat utilitzant diferents mides del bloc de processament, que pot anar de la imatge sencera a blocs de reduïdes dimensions: 36X36, 16x16 o 8x8, per fer un seguiment de les propietats locals de la imatge quan aquesta és presenta característiques espacials poc uniformes.En este documento se presenta un detector de contornos de imágenes basado en el dominio transformado. A partir de la interpretación de la transformada de Fourier de la imagen y su formulación matricial en términos de los diferentes modos, se realiza una selección de las componentes paso-bajas a partir de las cuales se reconstruye la componente de baja frecuencia que se restará a la imagen original pora obtener el detector. Este detector de contornos no es sesgado. El algoritmo puede ser aplicado utilizando diferentes medidas del bloque de procesado, que puede ir de la imagen entera a bloques de reducidas dimensiones: 36x36, 16x16 o 8x8, que permiten hacer un seguimiento de las propiedades locales de la imagen cuando ésta presenta características sectoriales muy diversas.In this document an image contour detector based on the transformed domain is presented. Following the interpretation of the image Fourier transform and its matrix formulation in terms of its different modes, we select the base-band ones from which we reconstruct the low frequency image component. This component is subtracted to the original image in order to obtain the contours. This contour detector is not biased. The algorithm can be implemented using different block processing sizes, which can range from the entire image to blocks of smaller dimensions: 36x36, 16x16 or 8x8. Small blocks improve the contour detector performance when the local properties of the image are not uniform

Виділення контурів об'єктів в відеопотоці за допомогою адаптивного алгоритму Кенні

Розроблено модифікований алгоритм виділення контурів у режимі реального часу на основі класичного алгоритму Кенні. Запропонований підхід дозволяє отримувати чітко виділені контури без розмиття. Алгоритм має високу стійкість до імпульсних шумів. Об'єктом дослідження є методи і засоби виявлення об'єктів інтересу в режимі реального часу Зроблено аналіз цих методів, виявлені недоліки.A modified algorithm for selecting contours in real-time mode based on the classical Canny algorithm is developed. The proposed approach allows you to receive clearly distinguished contours without blurring. The algorithm has high resistance to pulse noise. The object of research is the methods and means of identifying objects of interest in real time. An analysis of these methods has been made, deficiencies have been identified

Automatic Main Road Extraction from High Resolution Satellite Imagery

Road information is essential for automatic GIS (geographical information system) data acquisition, transportation and urban planning. Automatic road (network) detection from high resolution satellite imagery will hold great potential for significant reduction of database development/updating cost and turnaround time. From so called low level feature detection to high level context supported grouping, so many algorithms and methodologies have been presented for this purpose. There is not any practical system that can fully automatically extract road network from space imagery for the purpose of automatic mapping. This paper presents the methodology of automatic main road detection from high resolution satellite IKONOS imagery. The strategies include multiresolution or image pyramid method, Gaussian blurring and the line finder using 1-dimemsional template correlation filter, line segment grouping and multi-layer result integration. Multi-layer or multi-resolution method for road extraction is a very effective strategy to save processing time and improve robustness. To realize the strategy, the original IKONOS image is compressed into different corresponding image resolution so that an image pyramid is generated; after that the line finder of 1-dimemsional template correlation filter after Gaussian blurring filtering is applied to detect the road centerline. Extracted centerline segments belong to or do not belong to roads. There are two ways to identify the attributes of the segments, the one is using segment grouping to form longer line segments and assign a possibility to the segment depending on the length and other geometric and photometric attribute of the segment, for example the longer segment means bigger possibility of being road. Perceptual-grouping based method is used for road segment linking by a possibility model that takes multi-information into account; here the clues existing in the gaps are considered. Another way to identify the segments is feature detection back-to-higher resolution layer from the image pyramid

Image analysis using multiscale boundary extraction algorithm

The complete analysis and interpretation of the information in image data is a complex process. This dissertation presents 3 major contributions to image analysis, namely, global multiscale detection, local scale analysis, and boundary extraction. Global scale analysis is related to identification of the various scales presented in the image. A new approach for global scale analysis is developed based on the differential power spectrum normalized variance ratio (DPSNVR). The DPSNVR is the ratio of the second order normalized central moment of the power spectrum of the image to that of the multiscale differential mask. Local maxima in DPSNVR graph directly indicate the global scales in the image. Local scale analysis performs a more detailed analysis of the edges to eliminate effects of blurring. A method based on mutilscale feature matching has been proposed. Details obtained at all scales are treated using a scale invariant normalization scheme. Besides local scale analysis, a multiscale data fusion algorithm has been implemented which leads to the new concept of multiple scale differential masks. The multiple scale differential mask generated using a range of scale values possesses the remarkable shape preservation property which makes it superior to traditional multiscale masks. Finally the complete sequential boundary extraction algorithm based on particle motion in a velocity field is presented. The boundary extraction algorithm incorporates edge localization, boundary representation, and automated selection of boundary extraction parameters. The global scale analysis techniques in conjunction with the boundary extraction algorithm provide a multiscale image segmentation algorithm

Automatic Car Registration Plate Recognition Using the Hough Transform

The development of automatic car registration plate recognition systems will provide greater efficiency for vehicle monitoring in automatic access control, and will avoid the need to equip vehicles with special RF tags for identification since all vehicles possess a unique registration plate. Thus this study is an attempt to introduce an automatic car registration plate recognition system based on identifying the plate characters by using the Hough transform. However, the proposed recognition system can be used in conjunction with a tag system for higher security access control. The automatic registration plate recognition could also have considerable potential in a wide range of applications especially in the identification of vehicle-based offences and with law enforcement. Recent advances in computer vision technology and the falling price of the related devices has contributed in making it practical to build an automatic, registration plate recognition systems. There have been a number of Optical Character Recognition (OCR) techniques, which have been used in the recognition of car registration plate characters. These systems include the character details matching process (Lotufo, et al. 1990), BAM (Bi-directional Associative Memories) neural network (Fahmy 1994) neural network (Tindall, 1995) and cross correlation pattern matching character matching techniques (Cornelli, et al. 1995). All of these systems recognized the characters by matching the full image of every character with a character\u27s template database which requires considerable processing time and large memory for the database. The purpose of this study is to explore the potential for using Hough transform (Hough 1962) in vehicle registration plate recognition. The OCR technique used in this project is unlike the other systems where the character recognition was based on matching the character\u27s full image; However the OCR technique in this system used Hough transform to identify the characters, where the recognition of a character is based on matching its identification array to the database. To validate the research, a car registration plate recognition system was developed to locate the registration plate from the full image of a vehicle and then extrar.t the plate characters by using image processing techniques. A Hough transform algorithm was applied to every character within the registration plate image to produce an identification array for these characters, and the plate characters were recognized by matching their identification array to the database. The system has been applied to a number of video recorded car images to recognize their registration plates. The rate of correctly recognized characters was 82.7% of the extracted characters, but improvement can be granted by using a faster digital camera and taking some precautions in the registration plate frames. However, the research indicated that the optical character recognition technique used in the study is an efficient and simple algorithm to identify characters, without requiring a relatively large processing memory

Biomimetic Design for Efficient Robotic Performance in Dynamic Aquatic Environments - Survey

This manuscript is a review over the published articles on edge detection. At first, it provides theoretical background, and then reviews wide range of methods of edge detection in different categorizes. The review also studies the relationship between categories, and presents evaluations regarding to their application, performance, and implementation. It was stated that the edge detection methods structurally are a combination of image smoothing and image differentiation plus a post-processing for edge labelling. The image smoothing involves filters that reduce the noise, regularize the numerical computation, and provide a parametric representation of the image that works as a mathematical microscope to analyze it in different scales and increase the accuracy and reliability of edge detection. The image differentiation provides information of intensity transition in the image that is necessary to represent the position and strength of the edges and their orientation. The edge labelling calls for post-processing to suppress the false edges, link the dispread ones, and produce a uniform contour of objects

Intelligent laser scanning for computer aided manufacture.

Reverse engineering requires the acquisition of large amounts of data describing the surface of an object, sufficient to replicate that object accurately using appropriate fabrication techniques. This is important within a wide range of commercial and scientific fields where CAD models may be unavailable for parts that must be duplicated or modified, or where a physical model is used as a prototype. The three-dimensional digitisation of objects is an essential first step in reverse engineering. Optical triangulation laser sensors are one of the most popular and common non-contact methods used in the data acquisition process today. They provide the means for high resolution scanning of complex objects. Multiple scans of the object are usually required to capture the full 3D profile of the object. A number of factors, including scan resolution, system optics and the precision of the mechanical parts comprising the system may affect the accuracy of the process. A single perspective optical triangulation sensor provides an inexpensive method for the acquisition of 3D range image data

Development of an image matching scheme using feature- and area based matching techniques

Image matching is widely considered to be one of the most difficult tasks of a digital photogrammetric system. Traditionally image matching has been approached from either an area based or a feature based point of view. In recent years significant progress has been made in Area Based Matching (ABM) techniques such as Multiphoto Geometrically Constrained Least Squares Matching. Also in the field of Feature Based Matching (FBM) improvements have been made in extracting and matching image features, using for example the Forstner Operator followed by feature matching. Generally, area- and feature based matching techniques have been developed independently from each other. The aim of this research project was to design an automated image matching scheme that combines aspects of Feature Based Matching (FBM) and Area Based Matching (ABM). The reason for taking a hybrid approach is to encapsulate only the advantages of each matching scheme while cancelling out the disadvantages. The approach taken was to combine traditional aspects of ABM in digital photogrammetry with image analysis techniques found more commonly in the area of image processing and specifically machine vision