2D Shape Analysis using Geodesic Distance
ثبت نشده
چکیده
Shape analysis is a fundamental and difficult problem in computer vision. It is crucial for recognition, video tracking, image retrieval and other applications. This paper proposes 2D shape analysis by using geodesic distance. It focuses on how to apply geodesic distance for shape matching and shape decomposition. Geodesic Fourier Descriptors is developed as a kind of shape representation for shape matching. Geodesic Fuzzy Cluster is performed to decompose the shape into meaningful parts. Geodesic distance is very suitable for shape analysis due to its robustness under rotation, boundary noisy distortion, and even local shape transformation. This paper also discusses the computation of geodesic distance. An algorithm based on two-scan dilating operation is presented to compute the geodesic distance efficiently in discrete image fields. Finally, experiments are carried out to show the effect of geodesic distance based shape analysis.
منابع مشابه
Riemannian Metrics on the Space of Solid Shapes
We present a new framework for multidimensional shape analysis. The proposed framework represents solid objects as points on an infinite-dimensional Riemannian manifold and distances between objects as minimal length geodesic paths. Intershape distance forms the foundation for shape-based statistical analysis. The proposed method incorporates a metric that naturally prevents self-intersections ...
متن کاملAuthor's Personal Copy Matching 2d and 3d Articulated Shapes Using the Eccentricity Transform
This paper presents a novel method for 2D and 3D shape matching that is insensitive to articulation. It uses the eccentricity transform, which is based on the computation of geodesic distances. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. The eccentricity transform considers the length of the longest geodesics. Histograms of the eccentricity transform characte...
متن کاملMatching 2 D & 3 D Articulated Shapes using Eccentricity
Shape matching should be invariant to the typical intra-class deformations present in nature. The majority of shape descriptors are quite complex and not invariant to the deformation or articulation of object parts. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. The eccentricity transform considers the length of the longest geodesics. It is robust with respect t...
متن کامل2D-Shape Analysis Using Conformal Mapping
The study of 2D shapes and their similarities is a central problem in the field of vision. It arises in particular from the task of classifying and recognizing objects from their observed silhouette. Defining natural distances between 2D shapes creates a metric space of shapes, whose mathematical structure is inherently relevant to the classification task. One intriguing metric space comes from...
متن کاملMatching 2D and 3D articulated shapes using the eccentricity transform
This paper presents a novel method for 2D and 3D shape matching that is insensitive to articulation. It uses the eccentricity transform, which is based on the computation of geodesic distances. Geodesic distances computed over a 2D or 3D shape are articulation insensitive. The eccentricity transform considers the length of the longest geodesics. Histograms of the eccentricity transform characte...
متن کامل