309 research outputs found
Polygonal Approximation of Digital Planar Curve Using Novel Significant Measure
This chapter presents an iterative smoothing technique for polygonal approximation of digital image boundary. The technique starts with finest initial segmentation points of a curve. The contribution of initially segmented points toward preserving the original shape of the image boundary is determined by computing the significant measure of every initial segmentation point that is sensitive to sharp turns, which may be missed easily when conventional significant measures are used for detecting dominant points. The proposed method differentiates between the situations when a point on the curve between two points on a curve projects directly upon the line segment or beyond this line segment. It not only identifies these situations but also computes its significant contribution for these situations differently. This situation-specific treatment allows preservation of points with high curvature even as revised set of dominant points are derived. Moreover, the technique may find its application in parallel manipulators in detecting target boundary of an image with varying scale. The experimental results show that the proposed technique competes well with the state-of-the-art techniques
A novel framework for making dominant point detection methods non-parametric
Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves
Thinning-free Polygonal Approximation of Thick Digital Curves Using Cellular Envelope
Since the inception of successful rasterization of curves and objects in the digital space, several algorithms have been proposed for approximating a given digital curve. All these algorithms, however, resort to thinning as preprocessing before approximating a digital curve with changing thickness. Described in this paper is a novel thinning-free algorithm for polygonal approximation of an arbitrarily thick digital curve, using the concept of "cellular envelope", which is newly introduced in this paper. The cellular envelope, defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons, is constructed using a combinatorial technique. This envelope, in turn, is analyzed to determine a polygonal approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve=curve-shaped object with varying thickness, unexpected disconnectedness, noisy information, etc., is unsuitable for the existing algorithms on polygonal approximation, the curve is encapsulated by the cellular envelope to enable the polygonal approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results that include output polygons for different values of the approximation parameter corresponding to several real-world digital curves, a couple of measures on the quality of approximation, comparative results related with two other well-referred algorithms, and CPU times, have been presented to demonstrate the elegance and efficacy of the proposed algorithm
A multiscale approach to decompose a digital curve into meaningful parts
International audienceA multi-scale approach is proposed for polygonal repre- sentation of a digital curve by using the notion of blurred seg- ment and a split-and-merge strategy. Its main idea is to de- compose the curve into meaningful parts that are represented by detected dominant points at the appropriate scale. The method uses no threshold and can automatically decompose the curve into meaningful parts
Parameter-free method for polygonal representation of the noisy curves
International audienceWe propose a parameter-free method for the detection of dominant points and polygonal representation of possibly noisy curves. Based on \cite{ND07,NguyenDebled09}, this work aims at a parameter-free method through a multiscale approach. We propose a new evaluation criterion to automatically determine the most appropriate width parameter for each input curve. Thanks to a recent result \cite{FF08} on the decomposition of a curve into a sequence of maximal blurred segments, the complexity of this algorithm is
Contribuciones sobre métodos óptimos y subóptimos de aproximaciones poligonales de curvas 2-D
Esta tesis versa sobre el an álisis de la forma de objetos 2D. En visión articial existen
numerosos aspectos de los que se pueden extraer información. Uno de los más usados es la
forma o el contorno de esos objetos. Esta característica visual de los objetos nos permite,
mediante el procesamiento adecuado, extraer información de los objetos, analizar escenas, etc.
No obstante el contorno o silueta de los objetos contiene información redundante. Este
exceso de datos que no aporta nuevo conocimiento debe ser eliminado, con el objeto de agilizar
el procesamiento posterior o de minimizar el tamaño de la representación de ese contorno, para
su almacenamiento o transmisión. Esta reducción de datos debe realizarse sin que se produzca
una pérdida de información importante para representación del contorno original. Se puede
obtener una versión reducida de un contorno eliminando puntos intermedios y uniendo los
puntos restantes mediante segmentos. Esta representación reducida de un contorno se conoce
como aproximación poligonal.
Estas aproximaciones poligonales de contornos representan, por tanto, una versión comprimida
de la información original. El principal uso de las mismas es la reducción del volumen
de información necesario para representar el contorno de un objeto. No obstante, en los últimos años estas aproximaciones han sido usadas para el reconocimiento de objetos. Para ello los algoritmos
de aproximaci ón poligonal se han usado directamente para la extracci ón de los vectores
de caracter ísticas empleados en la fase de aprendizaje.
Las contribuciones realizadas por tanto en esta tesis se han centrado en diversos aspectos de
las aproximaciones poligonales. En la primera contribución se han mejorado varios algoritmos
de aproximaciones poligonales, mediante el uso de una fase de preprocesado que acelera estos algoritmos permitiendo incluso mejorar la calidad de las soluciones en un menor tiempo. En la segunda contribución se ha propuesto un nuevo algoritmo de aproximaciones poligonales que obtiene soluciones optimas en un menor espacio de tiempo que el resto de métodos que aparecen en la literatura. En la tercera contribución se ha propuesto un algoritmo de aproximaciones que
es capaz de obtener la solución óptima en pocas iteraciones en la mayor parte de los casos. Por último, se ha propuesto una versi ón mejorada del algoritmo óptimo para obtener aproximaciones poligonales que soluciona otro problema de optimización alternativo.This thesis focus on the analysis of the shape of objects. In computer vision there are
several sources from which we can extract information. One of the most important source of
information is the shape or contour of objects. This visual characteristic can be used to extract
information, analyze the scene, etc.
However, the contour of the objects contains redundant information. This redundant data
does not add new information and therefore, must be deleted in order to minimize the processing
burden and reducing the amount of data to represent that shape. This reduction of data
should be done without losing important information to represent the original contour. A
reduced version of a contour can be obtained by deleting some points of the contour and linking
the remaining points by using line segments. This reduced version of a contour is known as
polygonal approximation in the literature.
Therefore, these polygonal approximation represent a compressed version of the original
information. The main use of polygonal approximations is to reduce the amount of information
needed to represent the contour of an object. However, in recent years polygonal approximations
have been used to recognize objects. For this purpose, the feature vectors have been extracted
from the polygonal approximations.
The contributions proposed in this thesis have focused on several aspects of polygonal approximations.
The rst contribution has improved several algorithms to obtain polygonal approximations,
by adding a new stage of preprocessing which boost the whole method. The
quality of the solutions obtained has also been improved and the computation time reduced.
The second contribution proposes a novel algorithm which obtains optimal polygonal approximations
in a shorter time than the optimal methods found in the literature. The third contribution
proposes a new method which may obtain the optimal solution after few iterations
in most cases. Finally, an improved version of the optimal polygonal approximation algorithm
has been proposed to solve an alternative optimization problem
Decomposition of a curve into arcs and line segments based on dominant point detection
International audienceA new solution is proposed to decompose a curve into arcs and straight line segments in time. It is a combined solution based on arc detection \cite{Nguyen10a_} and dominant point detection \cite{Nguyen10f} to strengthen the quality of the segmentation results. Experimental results show the fastness of the proposed method
Dominant points detection for shape analysis
The growing interest in recent years towards the multimedia and the large amount of information exchanged across the network involves the various fields of research towards the study of methods for automatic identification. One of
the main objectives is to associate the information content of images, using techniques for identifying composing objects. Among image descriptors, contours reveal are very important because most of the information can be extracted
from them and the contour analysis offers a lower computational complexity also. The contour analysis can be restricted to the study of some salient points with high curvature from which it is possible to reconstruct the original contour. The thesis is focused on the polygonal approximation of closed digital curves. After an overview of the most common shape descriptors, distinguished between
simple descriptors and external methods, that focus on the analysis of boundary points of objects, and internal methods, which use the pixels inside the object also, a description of the major methods regarding the extraction of dominant points studied so far and the metrics typically used to evaluate the goodness of the polygonal approximation found is given. Three novel approaches to the
problem are then discussed in detail: a fast iterative method (DPIL), more suitable for realtime processing, and two metaheuristics methods (GAPA, ACOPA) based on genetic algorithms and Ant Colony Optimization (ACO), more com-
plex from the point of view of the calculation, but more precise. Such techniques are then compared with the other main methods cited in literature, in order to
assess the performance in terms of computational complexity and polygonal approximation error, and measured between them, in order to evaluate the robustness with respect to affine transformations and conditions of noise. Two
new techniques of shape matching, i.e. identification of objects belonging to the same class in a database of images, are then described. The first one is based on the shape alignment and the second is based on a correspondence by ACO, which puts in evidence the excellent results, both in terms of computational time and recognition accuracy, obtained through the use of dominant points. In the first matching algorithm the results are compared with a selection of dominant points generated by a human operator while in the second the dominant points are used instead of a constant sampling of the outline typically used for
this kind of approach
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