Curvature Scale Space Image Research Articles

Open Curvature Scale Space Matching for Coronary Artery Identification in X-Ray Angiographic Images

Identification of the coronary artery in sequence angiograms is an important issue in the analysis and treatment of coronary angiography data, due to the ambiguities in the coronary vessels resulting from cardiac motion and the differences in the projection angles in angiograms. In this paper, we propose a novel coronary artery identification method based on curvature scale space image matching. First, points on the centerline of each coronary artery branch are convolved with Gaussian kernel functions of different scales to obtain the corresponding curvature scale space (CSS) image during the curve evolution. Then, a CSS image matching method for open curves, which uses a combination of open feature vectors and closed feature vectors, is proposed. Finally, curve matching transitivity is used to solve the problem of the inability to recognize the coronary artery when the difference in the imaging angles is too large. The experimental results demonstrate that the proposed method can recognize every coronary artery in different views, even when the difference in the imaging angles is 29.8°.

Leaf shape recognition system with multiscale shape analysis

The leaf shape recognition system could detect a leaf's boundary through using an improved boundary tracing algorithm,and efficiently extract curvature features of normalized teeth length and curvature scale space image maxima set from the boundary by a technique of multi-scale shape analysis.The system retrieved and classified the leaf by shape matching with the samples in a leaves database by eccentricity,circularity and the curvature features.Experimental results indicate that the method can improve the precision and the recall of leaf shape recognition greatly.

Shape retrieval with eigen-CSS search

Shape retrieval programs are comprised of two components: shape representation and matching algorithm. Building the representation on scale space filtering and the curvature function of a closed boundary curve, curvature scale space (CSS) has been demonstrated to be a robust 2D shape representation. The adoption of the CSS image as the default in the MPEG-7 standard, using a matching algorithm utilizing maxima of the CSS image contours, makes this feature of interest perforce. In this paper, we propose a framework in two stages for a novel approach to both representing and matching the CSS feature. Our contribution consists of three steps, each of which effects a profound speedup on CSS image matching. Each step is a well-known technique in other domains, but the proposed concatenation of steps leads to a novel approach to this subject which captures shape information more efficiently and decreases distracting noise. First, using experience derived from medical imaging, we define a set of marginal-sum features summarizing the CSS image. Second, the standard algorithm using CSS maxima involves a complicated and time-consuming search, since the zero of arc length is not known in any new contour. Here, we obviate this search via a phase normalization transform in the spatial dimension of the reduced marginal-CSS feature. Remarkably, this step also makes the method rotation- and reflection-invariant. Finally, the resulting feature space is amenable to dimension reduction via subspace projection methods, with a dramatic speedup in time, and as well orders of magnitude reduction in space. The first stage of the resultant program, using a general-purpose eigenspace, has class-categorization accuracy compatible with the original contour maxima program. In a second stage, we generate specialized eigenspaces for each shape category, with little extra runtime complexity because search can still be carried out in reduced dimensionality. In a leave-one-out categorization using the MPEG-7 contour database, a classification success rate of 94.1% over 1400 objects in 70 classes is achieved with very fast matching, and 98.6% in the top-2 classes. A leave-all-in test achieves 99.8% correct categorization. The method is rotation invariant, and is simple, fast, and effective.

Feature alignment approach for hand posture recognition based on curvature scale space

One of the most important aspects of gesture recognition is recognizing hand postures. Much research has been devoted to extracting reliable features for hand posture recognition. In this paper, a new feature alignment approach for hand posture recognition based on curvature scale space (CSS) is presented. The basis point for alignment is based on the two-dimensional distribution of a coordinate-peak set of the CSS image instead of on the coordinate with the maximal peak. A convolution operation is performed with the sequence of a coordinate-peak set and a predefined function. The coordinate with the maximal convolution value is designated as a basis point for aligning the CSS features of the hand posture. Results show that the proposed approach performs well in recognizing hand postures. Furthermore, the proposed approach is more accurate than previous methods based on conventional features.

2D Affine-Invariant Contour Matching Using B-Spline Model

This paper presents a new affine-invariant matching algorithm based on B-Spline modeling, which solves the problem of the non-uniqueness of B-Spline in curve matching. This method first smoothes the B-Spline curve by increasing the degree of the curve. It is followed by a reduction of the curve degree using the Least Square Error (LSE) approach to construct the Curvature Scale Space (CSS) image. CSS matching is then carried out. Our method combines the advantages of B-Spline that are continuous curve representation and the robustness of CSS matching with respect to noise and affine transformation. It avoids the need for other matching algorithms that have to use the re-sampled points on the curve. Thus, the curve matching error is reduced. The proposed algorithm has been tested by matching similar shapes from a prototype database. The experimental results showed the robustness and accuracy of the proposed method in B-Spline curve matching.

Adaptive multiple sets of CSS features for hand posture recognition

In this paper, an adaptive feature extraction approach based on curvature scale space (CSS) is presented for translation, scale, and rotation invariant recognition of hand postures. First, hands are segmented from hand posture images into binary silhouettes and then binary hand contours are computed. CSS images are then used to represent the contours of hand postures. In particular, adaptive multiple sets of CSS features are extracted to address the problem of deep concavities in the contours of hand postures. Finally, 1-nearest neighbor techniques are used to perform adaptive multiple sets of CSS feature matching for hand posture identification. Results indicate that the proposed approach performs well in the recognition of hand postures. And, the proposed approach is more accurate than previous methods which were based on conventional features. The proposed technique could be useful in improving the recognition of hand postures.

Robust automatic selection of optimal views in multi-view free-form object recognition

This paper addresses the issue of automatic selection of the best and the optimum number of representative views for each object in a database that can enable the accurate recognition of that object from any single arbitrary view of the object. The object boundary in each view is represented by its curvature scale space (CSS) image. The CSS representation has been selected for MPEG-7 standardisation as a contour shape descriptor. The paper also presents a novel method for fusion of results from combined shape descriptors. The utilisation of this method for multi-view three-dimensional (3-D) object recognition has been explored. The object boundary of each view is represented effectively using the CSS technique, moment invariants and Fourier descriptors. It has been shown that the results obtained from the fusion method are superior to the results obtained from any single technique. The method has been tested on a collection of free-form 3-D objects. Each object has been modelled using an optimal number of silhouette contours obtained from different viewpoints. This number varies depending on the complexity of the object and the measure of expected accuracy. A comprehensive analysis of the performance of the system has been given.

On Using Functions to Describe the Shape

In this paper, a systematic review of various contour functions and methods of their analysis, as applied in the field of shape description and characterization, is presented. Contour functions are derived from planar object outlines and are used as an intermediate representation from which various shape properties can be obtained. All the functions are introduced and analyzed following the same scheme, thus making it possible to compare various representations. Although only a small subset of contour functions is included in the survey (cross-section, radius-vector, support, width, parametric, complex, tangent-angle, curvature, polynomial, and parametric cubic), the paper demonstrates a multitude of techniques for shape description that are based on this approach. Several analysis tools, such as statistics, line moments and invariants, Fourier and other series expansions, curvature scale space image, wavelet, and Radon transform are described.

Shape similarity retrieval under affine transforms

The maxima of curvature scale space (CSS) image have already been used to represent 2-D shapes in different applications. The representation has shown robustness under the similarity transformations. Scaling, orientation changes, translation and even noise can be easily handled by the representation and its associated matching algorithm. In this paper, we examine the robustness of the representation under general affine transforms. We have a database of 1100 images of marine creatures. The contours in this database demonstrate a great range of shape variation. A database of 5000 contours has been constructed using 500 real object boundaries and 4500 contours which are the affine transformed versions of real objects. The CSS representation is then used to find similar shapes from this prototype database. The results provide substantial evidence of stability of the CSS image and its contour maxima under affine transformation. The method is also evaluated objectively through a large classified database and its performance is compared with the performance of two well-known methods, namely Fourier descriptors and moment invariants. The CSS shape descriptor has been selected for MPEG-7 standardization.

Affine Curvature Scale Space with Affine Length Parametrisation

The maxima of Curvature Scale Space (CSS) image have been used to represent 2D shapes under affine transforms. The CSS image is expected to be in the MPEG-7 package of standards. Since the CSS image employs the arc length parametrisation which is not affine invariant, we expect some deviations in the maxima of the CSS image under general affine transforms. Affine length and affine curvature have already been introduced and used as alternatives to arc length and conventional curvature in affine transformed environments. The utility of using these parameters to enrich the CSS representation is addressed in this paper. We use arc length to parametrise the curve prior to computing its CSS image. The parametrisation has been proven to be invariant under affine transformation and has been used in many affine invariant shape recognition methods. Since the organisation of the CSS image is based on curvature zero crossings of the curve, in this paper, we also investigate the advantages and shortcomings of using affine curvature in computation of the CSS image. The enriched CSS representations are then used to find similar shapes from a very large prototype database, and also a small classified database, both consisting of original as well as affine transformed shapes. An improvement is observed over the conventional CSS image.

Affine-similar shape retrieval: application to multiview 3-D object recognition

The feasibility of representing a three-dimensional (3-D) object with a small number of standard views is studied. The object boundary of each view is considered as a two dimensional (2-D) shape and is represented by the locations of the maxima of its curvature scale space (CSS) image contours. The idea is to identify an unknown object from an image taken from a random view by using the stored descriptions of the standard views. The CSS image has been selected for MPEG-7 standardization. The maxima of CSS image have already been used to represent 2-D shapes in different applications under similarity transforms. Since the new application involves affine transforms, we first examine the effects of general affine transforms on the representation and show that the locations of the maxima of the CSS image do not move dramatically even under large affine transformations. Our system for shape-based retrieval from large image databases is then applied to multiview 3-D object representation and recognition. Our collection of 3-D objects consists of 18 aircraft of different shapes. Three silhouette contours corresponding to random views are separately used as input for each object. Results indicate that robust and efficient 3-D free-form object recognition through multiview representation can be achieved using the CSS representation.

Enhancing CSS-based shape retrieval for objects with shallow concavities

Curvature scale space (CSS) image is a multi-scale organisation of the inflection points of a closed planar curve as it is smoothed. It consists of several arch shape contours, each related to a concavity or a convexity of the curve. The maxima of these contours have already been used as shape descriptors to find similar shapes in large image databases. In this article, we address the problem of shallow concavities. These may give rise to large contours in the CSS image. These contours may then match those corresponding to deep and wide concavities during the matching process. The phenomenon can be explained by recalling the fact that Gaussian smoothing leads to an approximation of geometric heat equation deformation. We have introduced a method to enrich the CSS image and create different contours for different types of concavities. We tested the proposed method on a database of 1100 images of marine creatures. A significant improvement was observed in the performance of the system on shapes with shallow segments.

Curvature scale space image in shape similarity retrieval

In many applications, the user of an image database system points to an image, and wishes to retrieve similar images from the database. Computer vision researchers aim to capture image information in feature vectors which describe shape, texture and color properties of the image. These vectors are indexed or compared to one another during query processing to find images from the database. This paper is concerned with the problem of shape similarity retrieval in image databases. Curvature scale space (CSS) image representation along with a small number of global parameters are used for this purpose. The CSS image consists of several arch-shape contours representing the inflection points of the shape as it is smoothed. The maxima of these contours are used to represent a shape. The method is then tested on a database of 1100 images of marine creatures. A classified subset of this database is used to evaluate the method and compare it with other methods. The results show the promising performance of the method and its superiority over Fourier descriptors and moment invariants.

Multiscale curvature-based shape representation using B-spline wavelets

This paper presents a new multiscale curvature-based shape representation technique with application to curve data compression using B-spline wavelets. The evolution of the curve is implemented in the B-spline scale-space, which enjoys a number of advantages over the classical Gaussian scale-space, for instance, the availability of fast algorithms. The B-spline wavelet transforms are used to efficiently estimate the multiscale curvature functions. Based on the curvature scale-space image, we introduce a coarse-to-fine matching algorithm which automatically detects the dominant points and uses them as knots for curve interpolation.

A theory of multiscale, curvature-based shape representation for planar curves

A shape representation technique suitable for tasks that call for recognition of a noisy curve of arbitrary shape at an arbitrary scale or orientation is presented. The method rests on the describing a curve at varying levels of detail using features that are invariant with respect to transformations that do not change the shape of the curve. Three different ways of computing the representation are described. They result in three different representations: the curvature scale space image, the renormalized curvature scale space image, and the resampled curvature scale space image. The process of describing a curve at increasing levels of abstraction is referred to as the evolution or arc length evolution of that curve. Several evolution and arc length evolution properties of planar curves are discussed. >

A matching algorithm of deformed planar curves using multiscale convex/concave structures

AbstractThis paper proposes a new multiscale segment matching method which is applicable to heavily deformed planar shapes. First, multiscale representations are obtained using curvature scale space filtering. Then inflection point correspondence is developed between consecutive smoothed shapes. The representation in this paper, unlike the well‐known curvature scale space image description, ensures that it always satisfies the consistency of hierarchical segment replacement. Moreover, it requires less processing time and memory allocation. Finally, optimum scale segments are matched by a new multiscale segment matching method proposed herein. In this method, the matching problem is formulated as a minimization problem of the total amount of segment dissimilarity. The minimization problem is solved effectively using dynamic programming. The proposed matching method makes it possible to obtain intuitively relevant correspondences even if the shapes have some local heavy deformation.