4,321 research outputs found
Automated pavement imaging program (APIP) for pavement cracks classification and quantification – a photogrammetric approach
The evaluation of pavement conditions is an important part of pavement management. Traditionally, pavement condition data are gathered by human inspectors who walk or drive along the road to assess the distresses and subsequently produce report sheets. This visual survey method is not only time consuming and costly but more importantly it compromises the safety of the field personnel. With an automated digital image processing technique, however, pavement distress analysis can be conducted in a swifter and safer manner. Pavement distresses are captured on images which are later automatically analysed. Furthermore, the automated method can improve the objectivity, accuracy, and consistency of the distress survey data. This research is aimed at the development of an Automated Pavement Imaging Program (APIP) for evaluating pavement distress condition. The digital image processing program enables longitudinal, transverse, and alligator cracking to be classified. Subsequently, the program will automatically estimate the crack intensity which can be used for rating pavement distress severity. Advancement in digital photogrammetric technology creates an opportunity to overcome some problems associated with the manual methods. It can provide a low-cost, near real time geometrical imaging through digital photogrammetry without physically touching the surface being measured. Moreover, digital photogrammetry workstation (DPW) is user-friendly, less tedious and enables surface conditions to be represented as ortho-image, overlay contour with ortho-image, as well as digital elevation model. The algorithms developed in this study are found to be capable of identifying type of cracking and its severity level with an accuracy of about 90% when compared to the traditional method. This is to show that the combination of the photogrammetric approach and APIP is a viable system to be used in pavement evaluations
Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform
In this research, off-line handwriting recognition system for Arabic alphabet is
introduced. The system contains three main stages: preprocessing, segmentation and
recognition stage. In the preprocessing stage, Radon transform was used in the design
of algorithms for page, line and word skew correction as well as for word slant
correction. In the segmentation stage, Hough transform approach was used for line
extraction. For line to words and word to characters segmentation, a statistical method
using mathematic representation of the lines and words binary image was used.
Unlike most of current handwriting recognition system, our system simulates the
human mechanism for image recognition, where images are encoded and saved in
memory as groups according to their similarity to each other. Characters are
decomposed into a coefficient vectors, using fast wavelet transform, then, vectors,
that represent a character in different possible shapes, are saved as groups with one
representative for each group. The recognition is achieved by comparing a vector of
the character to be recognized with group representatives.
Experiments showed that the proposed system is able to achieve the recognition task
with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a
single character in a text of 15 lines where each line has 10 words on average
Automatic Rural Road Centerline Extraction from Aerial Images for a Forest Fire Support System
In the last decades, Portugal has been severely affected by forest fires which have caused
massive damage both environmentally and socially. Having a well-structured and precise
mapping of rural roads is critical to help firefighters to mitigate these events. The
traditional process of extracting rural roads centerlines from aerial images is extremely
time-consuming and tedious, because the mapping operator has to manually label the road
area and extract the road centerline.
A frequent challenge in the process of extracting rural roads centerlines is the high
amount of environmental complexity and road occlusions caused by vehicles, shadows, wild
vegetation, and trees, bringing heterogeneous segments that can be further improved. This
dissertation proposes an approach to automatically detect rural road segments as well as
extracting the road centerlines from aerial images.
The proposed method focuses on two main steps: on the first step, an architecture based
on a deep learning model (DeepLabV3+) is used, to extract the road features maps and
detect the rural roads. On the second step, the first stage of the process is an optimization
for improving road connections, as well as cleaning white small objects from the predicted
image by the neural network. Finally, a morphological approach is proposed to extract
the rural road centerlines from the previously detected roads by using thinning algorithms
like the Zhang-Suen and Guo-Hall methods.
With the automation of these two stages, it is now possible to detect and extract road
centerlines from complex rural environments automatically and faster than the traditional
ways, and possibly integrating that data in a Geographical Information System (GIS),
allowing the creation of real-time mapping applications.Nas últimas décadas, Portugal tem sido severamente afetado por fogos florestais, que têm
causado grandes estragos ambientais e sociais. Possuir um sistema de mapeamento de
estradas rurais bem estruturado e preciso é essencial para ajudar os bombeiros a mitigar
este tipo de eventos. Os processos tradicionais de extração de eixos de via em estradas
rurais a partir de imagens aéreas são extremamente demorados e fastidiosos. Um desafio
frequente na extração de eixos de via de estradas rurais é a alta complexidade dos ambientes
rurais e de estes serem obstruídos por veículos, sombras, vegetação selvagem e árvores,
trazendo segmentos heterogéneos que podem ser melhorados.
Esta dissertação propõe uma abordagem para detetar automaticamente estradas rurais,
bem como extrair os eixos de via de imagens aéreas.
O método proposto concentra-se em duas etapas principais: na primeira etapa é utilizada
uma arquitetura baseada em modelos de aprendizagem profunda (DeepLabV3+),
para detetar as estradas rurais. Na segunda etapa, primeiramente é proposta uma otimização
de intercessões melhorando as conexões relativas aos eixos de via, bem como a
remoção de pequenos artefactos que estejam a introduzir ruído nas imagens previstas pela
rede neuronal. E, por último, é utilizada uma abordagem morfológica para extrair os eixos
de via das estradas previamente detetadas recorrendo a algoritmos de esqueletização tais
como os algoritmos Zhang-Suen e Guo-Hall.
Automatizando estas etapas, é então possível extrair eixos de via de ambientes rurais
de grande complexidade de forma automática e com uma maior rapidez em relação aos
métodos tradicionais, permitindo, eventualmente, integrar os dados num Sistema de Informação
Geográfica (SIG), possibilitando a criação de aplicativos de mapeamento em tempo
real
Analysis of Digital Logic Schematics Using Image Recognition
This thesis presents the results of research in the area of automated recognition of digital logic schematics. The adaptation of a number of existing image processing techniques for use with this kind of image is discussed, and the concept of using sets of tokens to represent the overall drawing i s explained in detail. Methods are given for using tokens to describe schematic component shapes, to represent the connections between components, and to provide sufficient information to a parser so that an equation can be generated. A Microsoft Windows-based test program which runs under Windows 95 or Windows NT has been written to implement the ideas presented. This program accepts either scanned images of digital schematics, or computer-generated images in Microsoft Windows bitmap format as input. It analyzes the input schematic image for content, and produces a corresponding logical equation as output. It also provides the functionality necessary to build and maintain an image token library
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
A new approach for centerline extraction in handwritten strokes: an application to the constitution of a code book
International audienceWe present in this paper a new method of analysis and decomposition of handwritten documents into glyphs (graphemes) and their associated code book. The different techniques that are involved in this paper are inspired by image processing methods in a large sense and mathematical models implying graph coloring. Our approaches provide firstly a rapid and detailed characterization of handwritten shapes based on dynamic tracking of the handwriting (curvature, thickness, direction, etc.) and also a very efficient analysis method for the categorization of basic shapes (graphemes). The tools that we have produced enable paleographers to study quickly and more accurately a large volume of manuscripts and to extract a large number of characteristics that are specific to an individual or an era
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