Konstantinos Papachristou

Symmetric Subspace Learning for Image Analysis

Subspace learning (SL) is one of the most useful tools for image analysis and recognition. A large number of such techniques have been proposed utilizing a priori knowledge about the data. In this paper, new subspace learning techniques are presented that use symmetry constraints in their objective functions. The rational behind this idea is to exploit the a priori knowledge that geometrical symmetry appears in several types of data, such as images, objects, faces, and so on. Experiments on artificial, facial expression recognition, face recognition, and object categorization databases highlight the superiority and the robustness of the proposed techniques, in comparison with standard SL techniques.

Subspace Learning with Enriched Databases Using Symmetry

Principal Component Analysis and Linear Discriminant Analysis are of the most known subspace learning techniques. In this paper, a way for training set enrichment is proposed in order to improve the performance of the subspace learning techniques by exploiting the a-priori knowledge that many types of data are symmetric. Experiments on artificial, facial expression recognition, face recognition and object categorization databases denote the robustness of the proposed approach.

Stereoscopic video shot classification based on Weighted Linear Discriminant Analysis

In this paper we propose a framework for stereoscopic video shot classification that includes low-level representations exploiting visual and disparity information and determination of optimal discriminant subspaces based on Linear Discriminant Analysis (LDA). Low-level representations are obtained through various color, disparity and texture descriptors which are applied to shot key frames. A new LDA-based subspace representation is proposed aiming at the optimal utilization of both visual and disparity information. The proposed shot classification framework has been evaluated on football stereoscopic videos providing enhanced classification performance and class discrimination, in comparison to using visual information only and standard LDA.

Human-centered 2D/3D video content analysis and description

In this paper, we propose a way of using the AudioVisual Description Profile (AVDP) of the MPEG-7 standard for stereo video and multichannel audio content description. Our aim is to provide means of using AVDP in such a way, that 3D video and audio content can be correctly and consistently described. Since AVDP semantics do not include ways for dealing with 3D audiovisual content, a new semantic framework within AVDP is proposed and examples of using AVDP to describe the results of analysis algorithms on stereo video and multichannel audio content are presented.

Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.

2D/3D AudioVisual content analysis & description

In this paper, we propose a way of using the Audio-Visual Description Profile (AVDP) of the MPEG-7 standard for 2D or stereo video and multichannel audio content description. Our aim is to provide means of using AVDP in such a way, that 3D video and audio content can be correctly and consistently described. Since AVDP semantics do not include ways for dealing with 3D audiovisual content, a new semantic framework within AVDP is proposed and examples of using AVDP to describe the results of analysis algorithms on stereo video and multichannel audio content are presented.

Object motion description in stereoscopic videos

The efficient search and retrieval of the increasing volume of stereoscopic videos drives the need for the semantic description of its content. The derivation of disparity (depth) information from stereoscopic content allows the extraction of semantic information that is inherent to 3D. The purpose of this paper is to propose algorithms for semantically characterizing the motion of an object or groups of objects along any of the X, Y, Z axes. Experimental results are also provided.

A new methodology for the measurement of the root canal curvature and its 3D modification after instrumentation

Abstract Objective: In this study, the three-dimensional (3D) modification of root canal curvature was measured, after the application of Reciproc instrumentation technique, by using cone beam computed tomography (CBCT) imaging and a special algorithm developed for the 3D measurement of the curvature of the root canal. Materials and methods: Thirty extracted upper molars were selected. Digital radiographs for each tooth were taken. Root curvature was measured by using Schneider method and they were divided into three groups, each one consisting of 10 roots, according to their curvature: Group 1 (0°–20°), Group 2 (21°–40°), Group 3 (41°–60°). CBCT imaging was applied to each tooth before and after its instrumentation, and the data were examined by using a specially developed CBCT image analysis algorithm. Results: The instrumentation with Reciproc led to a decrease of the curvature by 30.23% (on average) in all groups. Conclusions: The proposed methodology proved to be able to measure the curvature of the root canal and its 3D modification after the instrumentation.

Stereoscopic video shot clustering into semantic concepts based on visual and disparity information

In this paper, we propose a framework for clustering shots from stereoscopic videos into clusters that correspond to semantic concepts exploiting visual and disparity information. Various color, disparity and texture descriptors are applied to shot key frames for obtaining low-level representations. Self Organizing Maps are subsequently employed upon various combinations of these representations in order to determine a lattice of representative semantic concepts. Experimental results on performances and football stereoscopic videos show that the use of disparity information leads to better clustering compared to using visual information only.

2014 IEEE International Conference on Image Processing (ICIP 2014)

In this paper, we propose a framework for clustering shots from stereoscopic videos into clusters that correspond to semantic concepts exploiting visual and disparity information. Various color, disparity and texture descriptors are applied to shot key frames for obtaining low-level representations. Self Organizing Maps are subsequently employed upon various combinations of these representations in order to determine a lattice of representative semantic concepts. Experimental results on performances and football stereoscopic videos show that the use of disparity information leads to better clustering compared to using visual information only.