Figure 2 - uploaded by Sarp Erturk
Content may be subject to copyright.
Source publication
Human head models are used in many applications of computer graphics such as computer animation and cyberspace communications. Spherical harmonic shape representation decomposes the surface into its spatial surface frequency components by 3D multiresolution techniques. This paper demonstrates that spherical harmonic representation is particularly s...
Similar publications
Citations
... Using a harmonic expansion, the similarity of the user's 3D head scan to the scans of subjects in an HRTF database is determined. Following the approach of [16,17] for finding similar 3D objects in a database, the spherical harmonic transform (SHT), well-known in acoustical processing, seems suitable for this task and has been previously used to compress and coarsely model head meshes in computer graphics [18]. However, it is essentially a 2D transform and therefore unable to model complex shapes with parts that are occluded from the origin, such as the pinna or the shoulders. ...
Head-related transfer functions (HRTFs) depend on the shape of the human head and ears, motivating HRTF personalization methods that detect and exploit morphological similarities between subjects in an HRTF database and a new user. Prior work determined similarity from sets of morphological parameters. Here we propose a non-parametric morphological similarity based on a harmonic expansion of head scans. Two 3D spherical transforms are explored for this task, and an appropriate shape similarity metric is defined. A case study focusing on personalisation of interaural time differences (ITDs) is conducted by applying this similarity metric on a database of 3D head scans.
... In the case of face modeling, the shape of a 3-D model before and after the face deformation is represented using a set of SH coefficients. The SH coefficients of the intermediate 3-D face models are then represented as an interpolation [9], [10]. In the case of the vertebra, the SH-based shape approximation before and after the deformation is used to estimate the scaling and torsion movements undergone during the deformation [11]. ...
Medical simulations of lung dynamics promise to be effective tools for teaching and training clinical and surgical procedures related to lungs. Their effectiveness may be greatly enhanced when visualized in an augmented reality (AR) environment. However, the computational requirements of AR environments limit the availability of the central processing unit (CPU) for the lung dynamics simulation for different breathing conditions. In this paper, we present a method for computing lung deformations in real time by taking advantage of the programmable graphics processing unit (GPU). This will save the CPU time for other AR-associated tasks such as tracking, communication, and interaction management. An approach for the simulations of the three-dimensional (3-D) lung dynamics using Green's formulation in the case of upright position is taken into consideration. We extend this approach to other orientations as well as the subsequent changes in breathing. Specifically, the proposed extension presents a computational optimization and its implementation in a GPU. Results show that the computational requirements for simulating the deformation of a 3-D lung model are significantly reduced for point-based rendering.
... With advent of fast algorithms in the last ten years through work of [19] and others, computing the SFT has become feasible. As a result, researchers have used the SFT in a wide variety of applications ranging from compression of human head models [7] and nonrigid shape recovery [17] to surface representations [5]. It is fairly easy to describe the SFT, for it is a projection of functions in L 2 (S 2 ) onto the set of spherical harmonics. ...
This paper is concerned with the group-theoretical background of integral transforms and the role they play in signal processing on the sphere. An overview of topological groups, measure and representation theories, and an overview of the Windowed Fourier Transform and the Continuous Wavelet Transform are presented. A group-theoretical framework within which these transforms arise is presented. The connection between integral transforms and square-integrable group representations is explored. The theory is also generalized beyond groups to homogeneous spaces. The abstract theory is then applied to signal processing on the sphere with a discussion of both global and local methods. A detailed derivation of the continuous spherical harmonic transform is presented. Global methods such as the spherical Fourier transform and convolution are presented in an appendix as well as some background material from group theory and topology.
In this paper we consider 3D object surfaces which can be represented as scalar functions defined on the sphere. These objects can be modeled as series of spherical harmonic functions. A simple progressive transmission scheme could be implemented which transmits the expansion coefficients one by one and thus implements a coarse to fine reconstruction. The buildup of the object according to this scheme is not completely smooth: Wavy patterns appear which disappear in subsequent stages and are replaced by finer spurious patterns and so on. We propose a remedy for this behavior which is based on the simulation of a reversed diffusion process on the sphere.
A method to easily explore an optimum set of haptic rendering parameters is proposed. The haptic technology allows a user
to touch 3D objects in virtual environments and is getting the attention of many researchers. Properly setting the haptic
device parameters, however, is a tedious and non-intuitive task for everyone. Additionally, non-programmer cannot easily use
the haptic devices because they require some level of programming. Therefore, we applied Interactive Evolutionary Computation
(IEC) to ease the haptic parameter setup and optimization without requesting any programming efforts. The proposed method
allows the user to easily customize the use of haptics through the IEC operations such as selection and rating.
A diffusion-based approach to surface smoothing is presented. Surfaces are represented as scalar functions defined on the sphere. The approach is equivalent to Gaussian smoothing on the sphere and is computationally efficient since it does not require iterative smoothing. Furthermore, it does not suffer from the well-known shrinkage problem. Evolution of important shape features (parabolic curves) under diffusion is demonstrated. A nonlinear modification of the diffusion process is introduced in order to improve smoothing behavior of elongated and poorly centered objects.
