Skeletonization and classification by Bayesian classifier algorithm for object recognition (original) (raw)

Graph Structuring of Skeleton Object for Its High-Level Exploitation

Lecture Notes in Computer Science, 2015

Skeletonization is a morphological operation that summarizes an object by its median lines while preserving the initial image topology. It provides features used in biometric for the matching process, as well as medical imaging for quantification of the bone microarchitecture. We develop a solution for the extraction of structural and morphometric features useful in biometric, character recognition and medical imaging. It aims at storing object descriptors in a re-usable and hierarchical format. We propose graph data structures to identify skeleton nodes and branches, link them and store their corresponding features. This graph structure allows us to generate CSV files for high level analysis and to propose a pruning method that removes spurious branches regarding their length and mean gray level. We illustrate manipulations of the skeleton graph structure on medical image dedicated to bone microarchitecture characterization.

Recognition of shapes by attributed skeletal graphs

Pattern Recognition, 2004

In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classiÿcation. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of di erent objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and e ective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, re ection and rotation transformations and prove the ability to handle noise and occlusions.

Shock Graph Features and Decision Tree Classifier in Human Posture Recognition

2007

Abstract. In this paper, we endeavour in realizing the potential of our distinct version of shock graph (SG) as features for human shape representation. The developed technique involves the following procedures of skeletonization and thinning. Next, for modelling the human shape, end points and junction points are systematically detected using our novel pruning procedure. Six principal points, which consists of two junction points and four end points for each SG, were determined and preserved.

Shape Recognition by Clustering and Matching of Skeletons

Journal of Computers, 2008

We perform the task of shape recognition using a skeleton based method. Skeleton of the shape is considered as a free tree and is represented by a connectivity graph. Geometric features of the shape are captured using Radius function along the skeletal curve segments. Matching of the connectivity graphs based on their topologies and geometric features gives a distance measure for determining similarity or dissimilarity of the shapes. Then the distance measure is used for clustering and classification of the shapes by employing hierarchical clustering methods. Moreover, for each class, a median skeleton is computed and is located as the indicator of its related class. The resulted hierarchy of the shapes classes and their indicators are used for the task of shape recognition. This is performed for any given shape by a top-down traversing of the resulted hierarchy and matching with the indicators. We evaluate the proposed method by different shapes of silhouette datasets and we show how the method efficiently recognizes and classifies shapes.

Extended Investigations on Skeleton Graph Matching for Object Recognition

Advances in Intelligent Systems and Computing, 2013

Shape similarity estimation of objects is a key component in many computer vision systems. In order to compare two shapes, salient features of a query and target shape are selected and compared with each other, based on a predefined similarity measure. The challenge is to find a meaningful similarity measure that captures most of the original shape properties. One well performing approach called Path Similarity Skeleton Graph Matching has been introduced by Bai and Latecki. Their idea is to represent and match the objects shape by its interior through geodesic paths between skeleton end nodes. Thus it is enabled to robustly match deformable objects. However, insight knowledge about how a similarity measure works is of great importance to understand the matching procedure. In this paper we experimentally evaluate our reimplementation of the Path Similarity Skeleton Graph Matching Algorithm on three 2D shape databases. Furthermore, we outline in detail the strengths and limitations of the described methods. Additionally, we explain how the limitations of the existing algorithm can be overcome.

Object categorization using bone graphs

Computer Vision and Image Understanding, 2011

The bone graph (Macrini et al., in press, 2008)[23] and [25] is a graph-based medial shape abstraction that offers improved stability over shock graphs and other skeleton-based descriptions that retain unstable ligature structure. Unlike the shock graph, the bone graph's edges are attributed, allowing a richer specification of relational information, including how and where two medial parts meet. In this paper, we propose a novel shape matching algorithm that exploits this relational information. Formulating the problem as an inexact ...

Binary shape recognition using the morphological skeleton transform

Pattern Recognition, 1992

Binary image representation by its morphological skeleton transform has been proposed in the past as an information preserving representation. In this paper this skeleton representation is adopted as the start point for the development of a binary shape recognition scheme. A skeleton matching algorithm is presented that efficiently characterizes the similarity between two skeletons as a distance measure. This skeleton matching algorithm resembles the well known from speech processing elastic matching technique.

IJERT-Analysis of Iterative Skeletonization Algorithm in Recognizing Medial Axis of a Binary Image

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/analysis-of-iterative-skeletonization-algorithm-in-recognizing-medial-axis-of-a-binary-image https://www.ijert.org/research/analysis-of-iterative-skeletonization-algorithm-in-recognizing-medial-axis-of-a-binary-image-IJERTV3IS111060.pdf One of the central concerns in many computer vision based recognition systems is to extract skeletons of binary image objects in order to analyse structural similarities among them. Skeletonization of a digital image primarily refers to a special shape-transformation for representing structural properties of the various distinguishable key constituent components of the original image. Thinning is one of the popular approaches to find the skeleton of an image. The output of thinning algorithms is a set of connected digital curves or arcs maintaining overall structural properties of the object. In this paper we have studied two popular iterative skeletonization algorithms, namely Stentiford Thinning Algorithm and Zhang-Suen Thinning Algorithm to present a comparative analytical framework in relation with meaningful skeleton extraction capability.

View-based 3-d object recognition using shock graphs

2002

Abstract The shock graph is an emerging shape representation for object recognition, in which a 2-D silhouette is decomposed into a set of qualitative parts, captured in a directed acyclic graph. Although a number of approaches have been proposed for shock graph matching, these approaches do not address the equally important indexing problem.

A Graph-Based Approach for Shape Skeleton Analysis

2009

This paper presents a novel methodology to shape characterization, where a shape skeleton is modeled as a dynamic graph, and degree measurements are computed to compose a set of shape descriptors. The proposed approach is evaluated in a classification experiment which considers a generic set of shapes. A comparison with traditional shape analysis methods, such as Fourier descriptors, Curvature, Zernike moments and Multi-scale Fractal Dimension, is also performed. Results show that the method is efficient for shape characterization tasks, in spite of the reduced amount of information present in the shape skeleton.