Christian Feinen

Shape-based object retrieval by contour segment matching

In this paper we introduce an approach for object retrieval that uses contour segment matching for shape similarity computation. The object contour is partitioned into segments by skeleton endpoints. Each contour segment is represented by a rotation and scale invariant, 12-dimensional feature vector. The similarity of two objects is determined by matching their contour segments using the Hungarian algorithm. Our method is insensitive to object deformation and outperforms existing shape-based object retrieval algorithms. The most significant scientific contributions of this paper include (i) the introduction of a new feature extraction technique for contour segments as well as (ii) a new similarity measure for contour segments cleverly modelling the human perception and easily adapting to concrete application domains, and (iii) the impressive robustness of the method in an object retrieval scenario.

Shape-based Object Matching Using Point Context

This paper proposes a novel object matching algorithm based on shape contours. In order to ensure low computational complexity in shape representation, our descriptor is composed by a small number of interest points which are generated by considering both curvatures and the overall shape trend. To effectively describe each point of interest, we introduce a simple and highly discriminative point descriptor, namely Point Context, which represents its geometrical and topological location. For shape matching, we observed that the correspondences are not only dependent on the similarities between these single points in different objects, but they are also related to the geometric relations between multiple points of interest in the same object. Therefore, a high-order graph matching formulation is introduced to merge the single point similarities and the similarities between point triangles. The main contributions of this paper include (i) the introduction of a novel shape descriptor with robust shape points and their descriptors and (ii) the implementation of a high-order graph matching algorithm that solves the shape matching problem. Our method is validated through a series of object retrieval experiments on four datasets demonstrating its robustness and accuracy.

3D object retrieval by 3D curve matching

In this paper, we introduce a novel approach to 3D object retrieval by 3D curve matching. First, we project 2D object edges obtained from a depth image into 3D space. Second, we find distinctive feature points on the object. Third, we represent the shortest paths between the features by robust descriptors invariant to rotation, scaling, and translation. Finally, we match two 3D objects using the Maximum Weight Subgraph search. The most important contribution of this paper is the powerful object representation by 3D curves together with the corresponding matching algorithm. Excellent retrieval results achieved with our method show its benefits compared to the state-of-the-art.

Shape-based object matching using interesting points and high-order graphs

The introduction of a novel shape descriptor with robust shape interesting points and their descriptors.The implementation of a high-order graph matching algorithm for solving the shape matching problem.The proposed method can significantly improve the traditional correspondence-based shape matching methods.The proposed method is very robust in an object retrieval scenario. In shape-based object matching, it is important how to fuse similarities between points on a shape contour and the ones on another contour into the overall similarity. However, existing methods face two critical problems. Firstly, since most contour points are involved for possible matchings without taking into account the usefulness of each point, it causes high computational costs for point matching. Secondly, existing methods do not consider geometrical relations characterised by multiple points. In this paper, we propose a shape-based object matching method which is able to overcome these problems. To counteract the first problem mentioned, we devise a shape descriptor using a small number of interesting points which are generated by considering both curvatures and the overall shape trend. We also introduce a simple and highly discriminative point descriptor, namely Point Context, which represents the geometrical and topological location of each interesting point. For the second problem, we employ high-order graph matching which examines similarities for singleton, pairwise and triple relations of points. We validate the robustness and accuracy of our method through a series of experiments on six datasets.

Shape-based Object Retrieval and Classification with Supervised Optimisation

In order to enhance the performance of shape retrieval and classification, in this paper, we propose a novel shape descriptor with low computation complexity that can be easily fused with other meaningful descriptors like shape context, etc. This leads to a significant increase in descriptive power of original descriptors without adding to much computation complexity. To make the proposed shape descriptor more practical and general, a supervised optimisation strategy is introduced. The most significant scientific contributions of this paper includes the introduction of a new and simple feature descriptor with supervised optimisation strategy leading to the impressive improvement of the accuracy in object classification and retrieval scenario.

Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Vascular diseases are one of the most challenging health problems in developed countries. Past as well as ongoing research activities often focus on efficient, robust and fast aorta segmentation, and registration techniques. According to this needs our study targets an abdominal aorta registration method. The investigated algorithms make it possible to efficiently segment and register abdominal aorta in pre- and post-operative Computed Tomography (CT) data. In more detail, a registration technique using the Path Similarity Skeleton Graph Matching (PSSGM), as well as Maximum Weight Cliques (MWCs) are employed to realise the matching based on Computed Tomography data. The presented approaches make it possible to match characteristic voxels belonging to the aorta from different Computed Tomography (CT) series. It is particularly useful in the assessment of the abdominal aortic aneurysm treatment by visualising the correspondence between the pre- and post-operative CT data. The registration results have been tested on the database of 18 contrast-enhanced CT series, where the cross-registration analysis has been performed producing 153 matching examples. All the registration results achieved with our system have been verified by an expert. The carried out analysis has highlighted the advantage of the MWCs technique over the PSSGM method. The verification phase proves the efficiency of the MWCs approach and encourages to further develop this methods.

Shape Matching Using Point Context and Contour Segments

This paper proposes a novel method to generate robust contour partition points and applies them to produce point context and contour segment features for shape matching. The main idea is to match object shapes by matching contour partition points and contour segments. In contrast to typical shape context method, we do not consider the topological graph structure since our approach is only considering a small number of partition points rather than full contour points. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and contour deformations. The most significant scientific contributions of this paper include (i) the introduction of a novel partition point extraction technique for point context and contour segments as well as (ii) a new fused similarity measure for object matching and recognition, and (iii) the impressive robustness of the method in an object retrieval scenario as well as in a real application for environmental microorganism recognition.

Extended Investigations on Skeleton Graph Matching for Object Recognition

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.

A New Aortic Aneurysm CT Series Registration Algorithm

Nowadays, vascular diseases are the most challenging health problems in developed countries. Despite the fast development of modern contrast-enhanced Computed Tomography (CT), providing complex 3D datasets, the tremendous amount of problems still remain unsolved. The vascular segmentation as well as registration techniques are the topics of past and on-going research activities. In this work we focus on an abdominal aortic aneurysm registration technique. The developed approach makes it possible to match all voxels belonging to the aorta from pre- and post-operative CT data. The presented technique is based on aorta lumen segmentation and graph matching method. To segment the lumen area a hybrid level-set active contour approach is used. The matching step is performed based on a path similarity skeleton graph matching procedure. The registration results have been tested on the database of 8 patients, for which two different contrast-enhanced CT series were acquired. All registration results achieved with our system and verified by an expert prove the efficiency of the approach and encourage to further develop this method.

Skeleton-based abdominal aorta registration technique

Vascular diseases are the most challenging health problems in developed countries. The vascular segmentation as well as registration techniques are the topics of past and ongoing research activities. In this work we target an abdominal aorta registration technique. The developed methodology is useful in the assessment of abdominal aortic aneurysm treatment by visualizing the correspondence between pre- and postoperative Computed Tomography (CT) data. The presented approach makes it possible to match all voxels belonging to the aorta from different CT series. It is based on aorta lumen segmentation and graph matching method. To segment the lumen area a hybrid level-set active contour approach is used. The matching step is performed based on a path similarity skeleton graph matching procedure. The registration results have been tested on the database of 8 patients, for which two different contrast-enhanced CT series were acquired. All registration results achieved with our system and verified by an expert prove the efficiency of the approach and encourage to further develop this method.