In this study, the authors learn a similarity measure that discriminates between inter‐class and intra‐class samples based on a statistical inference perspective. A non‐linear combination of Mahalanobis is proposed to reflect the properties of a likelihood ratio test. Since an object's appearance is influenced by the identity of the object and variations in the capturing process, the authors represent the feature vector, which is the difference between two samples in the differences space, as a sample that is drawn from a mixture of many distributions. This mixture consists of the identities distribution and other distributions of the variations in the capturing process, in case of dissimilar samples. However, in the case of similar samples, the mixture consists of the variations in the capturing process distributions only. Using this representation, the proposed similarity measure accurately discriminates between inter‐class and intra‐class samples. To highlight the good performance of the proposed similarity measure, it is tested on different computer vision applications: face verification and person re‐identification. To illustrate how the proposed learning method can easily be used on large scale datasets, experiments are conducted on different challenging datasets: labelled faces in the wild (LFW), public figures face database, ETHZ and VIPeR. Moreover, in these experiments, the authors evaluate different stages, for example, features detector, descriptor type and descriptor dimension, which constitute the face verification pipeline. The experimental results confirm that the learning method outperforms the state‐of‐the‐art.

While traditional dental fillings are molded during a dental visit, dental restoration (e.g. inlays and onlays) are fabricated in a dental lab to offer a long lasting reparative solution to tooth decay or similar structural damage. Such process requires dental technicians who are highly trained experts in tooth anatomy to pick an appropriate standard tooth model from a tooth database. The success of a restoration process primarily relies on the acquisition and modeling of an accurate 3D shape of the occlusal surface of interest for manufacturing purposes. Based on a single optical image, this paper provides an economical and automated solution for tooth restoration where user intervention is kept at the minimal. The inherit relation between the photometric information and the underlying 3D shape is formulated as a coupled statistical model where the effect of illumination is modeled using Spherical Harmonics. Moreover, shape and texture alignment is accomplished using a proposed definition of anatomical jaw landmarks which are automatically detected. The system is evaluated on database of 32 jaws for crown, inlay, and onlay restoration. Results shows a promising performance for using the proposed approach in clinical application.

Image formation involves understanding sensor characteristics and object reflectance. In dentistry, an accurate 3-D representation of the human jaw may be used for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost effective solution for accurate 3-D representation of the human teeth, which can be used for diagnostic and treatment purposes. Nonetheless there are several challenges, such as the non-friendly image acquisition environment inside the human mouth, problems with lighting and errors due to the data acquisition sensors. In this paper, we focus on the 3D surface reconstruction aspect for human teeth based on a single image. We introduce a more realistic formulation of the shape-from-shading (SFS) problem by considering the image formation components; the camera, the light source, and the surface reflectance. We propose a non-Lambertian SFS algorithm under perspective projection which benefits from camera calibration parameters. We take into account the attenuation of illumination due to near-field imaging. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case. Our experiments provide promising quantitative metric results for the proposed approach.

In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.