Eric Paquet

Nefertiti: a query by content system for three-dimensional model and image databases management

Abstract This paper presents a new approach for the classification and retrieval of three-dimensional images and models from databases. A set of retrieval algorithms is introduced. These algorithms are content-based, meaning that the input is not made out of keywords but of three-dimensional models. Tensors of inertia, distribution of normals, distribution of cords and multiresolution analysis are used to describe each model. The database can be searched by scale, shape or color or any combination of these parameters. A user-friendly interface makes the retrieval operation simple and intuitive and makes it possible to edit reference models according to the specifications of the user. Experimental results using a database of more than 400 range images and 1000 VRML models are presented

Content-Based Access of VRML Libraries

This paper presents a new approach for the classification and retrieval of three-dimensional images and models from databases. A set of retrieval algorithms is introduced. These algorithms are content-based, meaning that the input is not made out of keywords but of three-dimensional models. Tensor of inertia, distribution of normals, distribution of cords and multiresolution analysis are used to describe each model. The database can be searched by scale, shape or color or any combination of these parameters. A user friendly interface makes the retrieval operation simple and intuitive and make it possible to edit reference models according to the specifications of the user. Experimental results using a database of more than 1000 VRML models are presented

Invariant pattern recognition for range images using the phase Fourier transform and a neural network

A method for invariant pattern recognition of range images by means of the phase Fourier transform is introduced. The phase Fourier transform may be used for the segmentation of connected planar and quadric surfaces. The method is generalized to nonconnected planar surfaces through the use of the concept of the characteristic normal. An invariant representation under changes of position, scale, and orientation for the characteristic normals is defined. This representation is used as the input for a feedforward neural network. Examples of applications are given, and finally the method is applied to the problems of classification and occlusion.

Range image segmentation using the Fourier transform

We introduce a new approach for the segmentation of planes and quadrics of a 3-D range image using the Fourier transform of the phase image. We show how it is possible to get a spectrum that contains the planes only and another one that contains the quadrics only. We determine the invariant properties of these spectra and show how our method is suitable for real-time applications.

Invariant Robust 3-D Face Recognition based on the Hilbert Transform in Spectral Space

One of the main objectives of face recognition is to determine whether an acquired face belongs to a reference database and to subsequently identify the corresponding individual. Face recognition has application in, for instance, forensic science and security. A face recognition algorithm, to be useful in real applications, must discriminate in between individuals, process data in real-time and be robust against occlusion, facial expression and noise.A new method for robust recognition of three-dimensional faces is presented. The method is based on harmonic coding, Hilbert transform and spectral analysis of 3-D depth distributions. Experimental results with three-dimensional faces, which were scanned with a laser scanner, are presented. The proposed method recognises a face with various facial expressions in the presence of occlusion, has a good discrimination, is able to compare a face against a large database of faces in real-time and is robust against shot noise and additive noise.

Effect of Subject Pose for 3D Database Management

Cleopatra is a 3D database management software to classify the digitized 3D shape of the human bodies contained in the CAESAR database. The classification process is based on the geometric characteristics of the digitized data, along with a filtering process based on text information contained in both, the traditional anthropometric and the extracted measurements files. The information contained in the demographic file is also available for filtering. This paper discusses the experimental results of tests related to the subjects pose while the scan is made. This is a repeatability test where the subjects (a male and a female) are repeatedly scanned ten times. Each scan is made at different times and the subject attempts to assume the same pose each time.

Three-dimensional Polychromatic Objects Classification Based on Amplitude Modulation and Amplitude-shift Keying

Algorithms allowing the classification of three-dimensional (3D) polychromatic images based on amplitude modulation and amplitude-shift keying are introduced. For this purpose, the 3D information is likened to a `carrier wave?, while the color information is likened to a `modulating signal?. The first algorithm is based on the amplitude modulation of the range by the hue. On the amplitude-shift keying platform, two algorithms are designed based on a three-channels red, green and blue modulation and a six-channels hue modulation. In the three-channels approach, the modulation of the three-dimensional image is performed on the basis of the relative ordination of the red, green and blue components, while in the six-channels approach, modulation is performed by partitioning the hue interval into six subintervals. The cross-angle distributions of the modulated range images are calculated in order to obtain invariant representations. Shift keying modulation is also applied to the cross-angle distributions in order to obtain a chromatic sampling of cross-angles. From these cross-angles representations a set of histograms are built. The classification is performed by comparing the unknown histograms to a reference set. The classified object corresponds to the smallest error.

Invariant recognition of hybrid-coded polychromatic range images

Correlation methods for polychromatic range image recognition are presented. First, the range and the color are compared and the use of the hue images is discussed. The advantages and disadvantages in combining the range and the color information are analyzed. Single-channel and double-channel approaches are presented. The ability of both recognition methods to be invariant under translation in the xy plane and along the z axis are demonstrated. The codification of the range and the hue images using phase-coding, sine-coding and hybrid coding make possible the fully translation invariance. The double-channel is introduced using sine-coding images in the correlation process. Finally, the combination of those two channels is performed in order to improve the discrimination capability of the system. Digital results are shown.

Segmentation of range images using phase Fourier transform

We introduce a method for segmentation of planes and quadrics of a three-dimensional range image using the phase Fourier transform. We extend our previous method for contour determination and simultaneous detection of edges and quadrics. We consider optical and electronic implementations. Using the phase Fourier transform, we address the issue of invariant pattern recognition for segmented and non-segmented three-dimensional images. We show results obtained with a parallel computer.