Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images

We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes

Feedback Based Architecture for Reading Check Courtesy Amounts

In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either on intra-organization basis or on inter-organization basis, including paper intensive applications in the check processing application. In many countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. Such process of manual reading of each check involves significant time and cost. In this research, a new approach is introduced to read the numerical amount field on the check; also known as the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: segmentation, normalization, and the recognition of each character using a neural network classifier, with results better than many other methods in the literaratu

Handwritten Bank Check Recognition of Courtesy Amounts

In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates

Corrección de distorsión geométrica y detección de carriles en imágenes de geles de electroforesis para la caracterización molecular de organismos por computador

Proyecto de Graduación (Licenciatura en Ingeniería Electrónica). Instituto Tecnológico de Costa Rica. Escuela de Ingeniería Electrónica, 2009.El procesamiento y análisis digital de imágenes consiste en el uso de sistemas computarizados para la manipulación de imágenes digitales, ya sea para mejorar su calidad (procesamiento) o para la extracción de información (análisis). En este trabajo se presentan algoritmos propios del procesamiento y análisis digital de imágenes y su aplicación a la caracterización molecular de organismos en dos áreas específicas: la corrección de distorsión geométrica y la extracción de información en imágenes de geles de electroforesis. Para tal efecto se realiza un análisis de diferentes enfoques existentes para solucionar ambos problemas con el fin de implementar dos algoritmos: uno para la corrección de distorsión óptica y el otro para la detección de carriles. La corrección de distorsión geométrica es realizada mediante un algoritmo basado en la distribución de probabilidad descrita por las líneas verticales y horizontales de una rejilla de calibrado. En el caso de la detección de carriles de imágenes de geles de electroforesis, el algoritmo tiene como objetivo la detección de sus contornos, empleando el gradiente de la imagen de gel y dos parámetros que permiten controlar la tolerancia para la detección: v y λ. Los algoritmos implementados poseen características diferentes a los encontrados en el análisis bibliográfico, brindando resultados satisfactorios tanto en tiempo de ejecución como en eficacia, para diferentes imágenes de prueba

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation

Negatif bağlantılı öğrenme algoritmalı yapay sinir ağları ile mobil cihazlarda optik karakter tanıma uygulaması

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır

Visual pattern recognition using neural networks

Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks. In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance. We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations. Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results

A novel off-line character recognition: an MLP approach

The purpose of this thesis work is to explore the possibility of efficient man-machine communication through printed documents. An attempt has been made to show the pattern recognition techniques i.e., KNN classifier helpful in recognition of machine printed characters and artificial neural networks may be used to represent and recognize printed English characters of any font and size. In our current work the machine printed document images are scanned by a front end video scanner and are applied to noise removal techniques using smoothing and sharpening filters. The noiseless images are digitized into a bi-level image using Ni-Black proposed binarization technique and proposed adaptive thresholding algorithm using Laplacian sign. Our work is split into three parts. The first part deals with segmentation and thinning. The output of this phase is thinned character image. The second part involves features are extracted from thinned image. The third part deals with KNN classifiers and training of the multilayer perceptron and recognizing characters after the system is trained. Automatic character recognition system promises to hold great future in Automatic office information processing system by integrating with multimedia, like Graphics, image and voice, into a single work station

Adding feedback to improve segmentation and recognition of handwritten numerals

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Susan A. Dey.S.B.and M.Eng