Automated Fragmentary Bone Matching

Identification, reconstruction and matching of fragmentary bones are basic tasks required to accomplish quantification and analysis of fragmentary human remains derived from forensic contexts. Appropriate techniques for three-dimensional surface matching have received great attention in computer vision literature, and various methods have been proposed for matching fragmentary meshes; however, many of these methods lack automation, speed and/or suffer from high sensitivity to noise. In addition, reconstruction of fragementary bones along with identification in the presence of reference model to compare with in an automatic scheme have not been addressed. In order to address these issues, we used a multi-stage technique for fragment identification, matching and registration. The study introduces an automated technique for matching of fragmentary human skeletal remains for improving forensic anthropology practice and policy. The proposed technique involves creation of surfaces models for the fragmentary elements which can be done using computerized tomographic scans followed by segmentation. Upon creation of the fragmentary elements models, the models go through feature extraction technique where the surface roughness map of each model is measured using local shape analysis measures. Adaptive thesholding is then used to extract model features. A multi-stage technique is then used to identify, match and register bone fragments to their corresponding template bone model. First, extracted features are used for matching with different template bone models using iterative closest point algorithm with different positions and orientations. The best match score, in terms of minimum root-mean-square error, is used along with the position and orientation and the resulting transformation to register the fragment bone model with the corresponding template bone model using iterative closest point algorithm

Robust feature-based 3D mesh segmentation and visual mask with application to QIM 3D watermarking

The last decade has seen the emergence of 3D meshes in industrial, medical and entertainment applications. Many researches, from both the academic and the industrial sectors, have become aware of their intellectual property protection arising with their increasing use. The context of this master thesis is related to the digital rights management (DRM) issues and more particularly to 3D digital watermarking which is a technical tool that by means of hiding secret information can offer copyright protection, content authentication, content tracking (fingerprinting), steganography (secret communication inside another media), content enrichment etc. Up to now, 3D watermarking non-blind schemes have reached good levels in terms of robustness against a large set of attacks which 3D models can undergo (such as noise addition, decimation, reordering, remeshing, etc.). Unfortunately, so far blind 3D watermarking schemes do not present a good resistance to de-synchronization attacks (such as cropping or resampling). This work focuses on improving the Spread Transform Dither Modulation (STDM) application on 3D watermarking, which is an extension of the Quantization Index Modulation (QIM), through both the use of the perceptual model presented, which presents good robustness against noising and smoothing attacks, and the the application of an algorithm which provides robustness noising and smoothing attacks, and the the application of an algorithm which provides robustness against reordering and cropping attacks based on robust feature detection. Similar to other watermarking techniques, imperceptibility constraint is very important for 3D objects watermarking. For this reason, this thesis also explores the perception of the distortions related to the watermark embed process as well as to the alterations produced by the attacks that a mesh can undergo

Identification automatisée des espèces d'arbres dans des scans laser 3D réalisés en forêt

The objective of the thesis is the automatic recognition of tree species from Terrestrial LiDAR data. This information is essential for forest inventory. As an answer, we propose different recognition methods based on the 3D geometric texture of the bark.These methods use the following processing steps: a preprocessing step, a segmentation step, a feature extraction step and a final classification step. They are based on the 3D data or on depth images built from 3D point clouds of tree trunks using a reference surface.We have investigated and tested several segmentation approaches on depth images representing the geometric texture of the bark. These approaches have the disadvantages of over segmentation and are quite sensitive to noises. For this reason, we propose a new 3D point cloud segmentation approach inspired by the watershed technique that we have called «Burst Wind Segmentation». Our approach succeed in extracting in most cases the characteristic scars that are next compared to those stored in a dictionary («ScarBook») in order to determine the tree species.A large variety of characteristics is extracted from the regions segmented by the different methods proposed. These characteristics are the roughness, the global shape of the segmented regions, the saliency and the curvature of the contour, the distribution of the contour points, the distribution of the shape according to the different orientations.Finally, for the classification of the visual characteristics, the Random Forest method by Leo Breiman and Adèle Cutler is used in a two steps approach: selection of the most important variables and cross classification with the selected variables.The bark of the tree changes with the trunk diameter. We have thus studied different natural variability criteria and we have tested our approaches on a test set that includes this variability. The accuracy rate is over 96% for all the proposed segmentation approaches but the best result is obtained with the «Burst Wind Segmentation» one due to the fact that this approach can better extract the scars, it uses a dictionary of scars for recognition, and it has been evaluated on a greater variety of shapes, curvatures, saliency and roughness.L’objectif de ces travaux de thèse est la reconnaissance automatique des espèces d’arbres à partir de scans laser terrestres, information indispensable en inventaire forestier. Pour y répondre, nous proposons différentes méthodes de reconnaissance d’espèce basées sur la texture géométrique 3D des écorces.Ces différentes méthodes utilisent la séquence de traitement suivante : une étape de prétraitement, une étape de segmentation, une étape d’extraction des caractéristiques et une dernière étape de classification. Elles sont fondées sur les données 3D ou bien sur des images de profondeur extraites à partir des nuages de points 3D des troncs d’arbres en utilisant une surface de référence.Nous avons étudié et testé différentes approches de segmentation sur des images de profondeur représentant la texture géométrique de l'écorce. Ces approches posent des problèmes de sur-Segmentation et d'introduction de bruit. Pour cette raison, nous proposons une nouvelle approche de segmentation des nuages de points 3D : « Burst Wind Segmentation », inspirée des lignes de partage des eaux. Cette dernière réussit, dans la majorité des cas, à extraire des cicatrices caractéristiques qui sont ensuite comparées à un dictionnaire des cicatrices (« ScarBook ») pour discriminer les espèces d’arbres.Une grande variété de caractéristiques est extraite à partir des régions segmentées par les différentes méthodes proposées. Ces caractéristiques représentent le niveau de rugosité, la forme globale des régions segmentées, la saillance et la courbure du contour, la distribution des points de contour, la distribution de la forme selon différents angles,...Enfin, pour la classification des caractéristiques visuelles, les forêts aléatoires (Random Forest) de Leo Breiman et Adèle Cutler sont utilisées dans une approche à deux étapes : sélection des variables importantes, puis classification croisée avec les variables retenues, seulement.L’écorce de l’arbre change avec l'accroissement en diamètre ; nous avons donc étudié différents critères de variabilité naturelle et nous avons testé nos approches sur une base qui présente cette variabilité. Le taux de bonne classification dépasse 96% dans toutes les approches de segmentation proposées mais les meilleurs résultats sont atteints avec la nouvelle approche de segmentation « Burst Wind Segmentation » étant donné que cette approche réussit mieux à extraire les cicatrices, utilise un dictionnaire de cicatrices et a été évaluée sur une plus grande variété de caractéristiques de forme, de courbure, de saillance et de rugosité

A Roughness Measure for 3D Mesh Visual Masking

International audience3D models are subject to a wide variety of processing operations such as compression, simplification or watermarking, which introduce slight geometric modifications on the shape. The main issue is to maximize the compression/simplification ratio or the watermark strength while minimizing these visual degradations. However few algorithms exploit the human visual system to hide these degradations, while perceptual attributes could be quite relevant for this task. Particularly, the Masking Effect defines the fact that a signal can be masked by the presence of another signal with similar frequency or orientation. In this context we introduce the notion of roughness for a 3D mesh, as a local measure of geometric noise on the surface. Indeed, a textured (or rough} region is able to hide geometric distortions much better than a smooth one. Our measure is based on curvature analysis on local windows of the mesh and is independent of the resolution/connectivity of the object. An application to Visual Masking is presented and discussed

A Roughness Measure for 3D Mesh Visual Masking

Figure 1: The Armadillo 3D model and its roughness map. Warmer colors (reds, yellows, greens) illustrate high roughness values while cooler colors (dark blue) illustrate rather smooth regions. 3D models are subject to a wide variety of processing operations such as compression, simplification or watermarking, which introduce slight geometric modifications on the shape. The main issue is to maximize the compression/simplification ratio or the watermark strength while minimizing these visual degradations. However few algorithms exploit the human visual system to hide these degradations, while perceptual attributes could be quite relevant for this task. Particularly, the Masking Effect defines the fact that a signal can be masked by the presence of another signal with similar frequency or orientation. In this context we introduce the notion of roughness for a 3D mesh, as a local measure of geometric noise on the surface. Indeed, a textured (or rough) region is able to hide geometric distortions much better than a smooth one. Our measure is based on curvature analysis on local windows of the mesh and is independent of the resolution/connectivity of the object. An application to Visual Masking is presented and discussed

A Roughness Measure for 3D Mesh Visual Masking

International audience3D models are subject to a wide variety of processing operations such as compression, simplification or watermarking, which introduce slight geometric modifications on the shape. The main issue is to maximize the compression/simplification ratio or the watermark strength while minimizing these visual degradations. However few algorithms exploit the human visual system to hide these degradations, while perceptual attributes could be quite relevant for this task. Particularly, the Masking Effect defines the fact that a signal can be masked by the presence of another signal with similar frequency or orientation. In this context we introduce the notion of roughness for a 3D mesh, as a local measure of geometric noise on the surface. Indeed, a textured (or rough} region is able to hide geometric distortions much better than a smooth one. Our measure is based on curvature analysis on local windows of the mesh and is independent of the resolution/connectivity of the object. An application to Visual Masking is presented and discussed