The Augmented Multiresolution Reeb Graph Approach for Content-Based Retrieval of 3d Shapes

Tung, Tony; Schmitt, Francis

doi:10.1142/s0218654305000748

Cited by 124 publications

(102 citation statements)

References 11 publications

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“…(3) Construct Reeb Graphs [5,6] based on geodesic distance on the surface. (4) As initial models, pick up some graphs that appear to be relatively close to the targeted kinematic structure.…”

Section: Overviewmentioning

confidence: 99%

“…(4),(5) are processes for making correlations between shape structure and kinematic structures which enable us to acquire the kinematic structure in process (6). Last of all, by process (7), we can acquire kinematic structure that reflects the diversity of motion in the input visual hull.…”

Section: Overviewmentioning

confidence: 99%

“…On every deformation, we select a frame in which the reeb graph has the highest correlation to the target reeb graph among the frames in which the reeb graph has already deformed, and use its deformed reeb graph as a new initial model. We utilize a correlation computing method of reeb graphs that is proposed in [6].…”

Section: Updating Initial Model Based On Correlation Of Reeb Graphmentioning

confidence: 99%

“…This paper presents a new scheme for acquiring 3D kinematic structure and motion from time-series volume data, in particular, focusing on human body. Our basic strategy is to first represent the shape structure of the target in each frame by using aMRG, augmented Multiresolution Reeb Graph [6], and then deform each of the shape structures so that all of them can be identified as a common kinematic structure throughout the input frames. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent.…”

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confidence: 99%

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Finding Articulated Body in Time-Series Volume Data

Mukasa

Nobuhara

Maki

et al. 2006

Articulated Motion and Deformable Objects

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Abstract. This paper presents a new scheme for acquiring 3D kinematic structure and motion from time-series volume data, in particular, focusing on human body. Our basic strategy is to first represent the shape structure of the target in each frame by using aMRG, augmented Multiresolution Reeb Graph [6], and then deform each of the shape structures so that all of them can be identified as a common kinematic structure throughout the input frames. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent. The only assumption we make is that human body can be approximated by an articulated body with certain number of end-points and branches. We demonstrate the efficacy of the proposed scheme through some experiments.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Updating Initial Model Based On Correlation Of Reeb Graphmentioning

confidence: 99%

mentioning

confidence: 99%

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Finding Articulated Body in Time-Series Volume Data

Mukasa

Nobuhara

Maki

et al. 2006

Articulated Motion and Deformable Objects

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“…However, they are not valid for general purposes since they require dedicated measures and matching schemes. Some examples of graph-based descriptors are the Multi-resolution Reeb graphs [Hilaga 2001] [Tung 2005], which are have the potential of encoding geometrical and topplogical shape properties, but are not applyable to all kind of shapes since they rely on the selection of the appropiate Reeb function [Lyer 2005]. Another example is the Skeletal graphs [Sundar 2003], which are Direct Acyclic graphs (DAG) that describe geometric features and allow parts matching.…”

Section: Shape-based Techniquesmentioning

confidence: 99%

Person Re-Identification Using RGB-Depth Cameras

Moll¹

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Abstract:The presence of surveillance systems in our lives has drastically increased during the last years. Camera networks can be seen in almost every crowded public and private place, which generate huge amount of data with valuable information. The automatic analysis of data plays an important role to extract relevant information from the scene. In particular, the problem of person re-identification is a prominent topic that has become of great interest, specially for the fields of security or marketing. However, there are some factors, such as changes in the illumination conditions, variations in the person pose, occlusions or the presence of outliers that make this topic really challenging. Fortunately, the recent introduction of new technologies such as depth cameras opens new paradigms in the image processing field and brings new possibilities. This Thesis proposes a new complete framework to tackle the problem of person re-identification using commercial rgb-depth cameras. This work includes the analysis and evaluation of new approaches for the modules of segmentation, tracking, description and matching. To evaluate our contributions, a public dataset for person re-identification using rgb-depth cameras has been created.Rgb-depth cameras provide accurate 3D point clouds with color information. Based on the analysis of the depth information, an novel algorithm for person segmentation is proposed and evaluated. This method accurately segments any person in the scene, and naturally copes with occlusions and connected people. The segmentation mask of a person generates a 3D person cloud, which can be easily tracked over time based on proximity.The accumulation of all the person point clouds over time generates a set of high dimensional color features, named raw features, that provides useful information about the person appearance. In this Thesis, we propose a family of methods to extract relevant information from the raw features in different ways. The first approach compacts the raw features into a single color vector, named Bodyprint, that provides a good generalisation of the person appearance over time. Second, we introduce the concept of 3D Bodyprint, which is an extension of the Bodyprint descriptor that includes the angular distribution of the color features. Third, we characterise the person appearance as a bag of color features that are independently generated over time. This descriptor receives the name of Bag of Appearances because its similarity with the concept of Bag of Words. Finally, we use different probabilistic latent variable models to reduce the feature vectors from a statistical perspective. The evaluation of the methods demonstrates that our proposals outperform the state of the art. Re-identificación de personas usando cámaras RGB-profundidadResumen:La presencia de sistemas de vigilancia se ha incrementado notablemente en los últimos años. Las redes de videovigilancia pueden verse en casi cualquier espacio público y privado concurrido, lo cual genera una gran cantidad de datos de gra...

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3D shape retrieval using viewpoint information‐theoretic measures

Bonaventura

Guo

Meng

et al. 2013

Computer Animation & Virtual

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In this paper, we present an information-theoretic framework to compute the shape similarity between 3D polygonal models. Given a 3D model, an information channel between a sphere of viewpoints around the model and its polygonal mesh is defined to compute the specific information associated with each viewpoint. The obtained information sphere can be seen as a shape descriptor of the model. Then, given two models, their similarity is obtained by performing a registration process between the corresponding information spheres. The distance between the information histograms is also defined as a coarse measure of similarity, as well as the scalar value given by the mutual information of the channel. The performance of all these measures is tested using the Princeton Shape Benchmark databaseThis work has been founded in part by grant number TIN2010-21089-C03-01 and BES-2011-045252 of Spanish Government, grant number 2009-SGR-643 of Generalitat de Catalunya (Catalan Government), and National Natural Science Foundation of China (Nos. 61331018, 61372190

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The Augmented Multiresolution Reeb Graph Approach for Content-Based Retrieval of 3d Shapes

Cited by 124 publications

References 11 publications

Finding Articulated Body in Time-Series Volume Data

Finding Articulated Body in Time-Series Volume Data

Person Re-Identification Using RGB-Depth Cameras

3D shape retrieval using viewpoint information‐theoretic measures

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