Catálogo de publicaciones - libros

Diffraction, interference, dispersive refraction and scattering are four wavelength-dependent mechanisms that produce iridescent colors. Wavelength-dependent functions need to be sampled at discrete wavelengths in the visible spectrum, which increases the computational intensity of rendering iridescence. Furthermore, diffraction requires careful sampling since its response function varies at a higher frequency variation with sharper peaks than interference or dispersive refraction. Consequently, rendering physically accurate diffraction has previously either been approximated using simplified color curves, or been limited to offline rendering techniques such as ray tracing. We propose a technique for real-time rendering of physically accurate diffraction on programmable hardware. Our technique adaptively samples the diffraction BRDF and precomputes it to Spherical Harmonic (SH) basis that preserves the peak intensity of the reflected light. While previous work on diffraction used low dynamic range lights, we preserve the full dynamic range of the incident illumination and the diffractive response over the entire hemisphere of incoming light directions. We defer conversion from a wavelength representation to a tone mapped RGB triplet until display.

Three dimensional cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents, molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in scale and size of subcompartments of interest. A technique has been developed to bound the solution of the problem by first localizing centers of mass for each cell and then partitioning clump of cells along minimal intersecting surfaces. An approximate solution to the center of mass is realized through iterative spatial voting, which is tolerant to variation in shape morphologies and overlapping compartments and is shown to have an excellent noise immunity. These centers of mass are then used to partition a clump of cells along minimal intersecting surfaces that are estimated by Radon transform. Examples on real data and performance of the system over a large population of data are evaluated. Although proposed strategies have been developed and tested on data collected through fluorescence microscopy, they are applicable to other problems in low level vision and medical imaging.

In this paper, we present a novel viewpoint selection framework for angiographic volume data. We propose several view descriptors based on typical concerns of clinicians for the view evaluation. Compared with conventional approaches, our method can deliver a more representative global optimal view by sampling at a much higher rate in the view space. Instead of performing analysis on sample views individually, we construct a solution space to estimate the quality of the views. Descriptor values are propagated to the solution space where an efficient searching process can be performed. The best viewpoint can be found by analyzing the accumulated descriptor values in the solution space based on different visualization goals.

There is biological evidence that human actions are composed out of action primitives, similarly to words and sentences being composed out of phonemes. Given a set of action primitives and an action composed out of these primitives we present a Hidden Markov Model-based approach that allows to recover the action primitives in that action. In our approach, the primitives may have different lengths, no clear “divider” between the primitives is necessary. The primitive detection is done online, no storing of past data is necessary. We verify our approach on a large database. Recognition rates are slightly smaller than the rate when recognizing the singular action primitives.

In recent years the problem of obtaining a reversible discrete surface polyhedrization (DSP) is attracting an increasing interest within the discrete geometry community. In this paper we propose the first algorithm for obtaining a reversible polyhedrization with a , i.e., together with a bound on the ratio of the number of facets of the obtained polyhedron and one with a minimal number of facets. The algorithm applies to the case of a DSP when a discrete surface is determined by a convex body in ℝ. The performance estimation is based on a new lower bound (in terms of the diameter of ) on the number of 2-facets of an optimal polyhedrization. That bound easily extends to an arbitrary dimension . We also discuss on approaches for solving the general 3D DSP.

The goal of our research is to robustly reconstruct general 3D scenes from 2D images, with application to automatic model generation in computer graphics and virtual reality. In this paper we aim at producing relatively dense and well-distributed 3D points which can subsequently be used to reconstruct the scene structure. We present novel camera calibration and scene reconstruction using scale-invariant feature points. A generic high-dimensional vector matching scheme is proposed to enhance the efficiency and reduce the computational cost while finding feature correspondences. A framework for structure and motion is also presented that better exploits the advantages of scale-invariant features. In this approach we solve the “phantom points” problem and this greatly reduces the possibility of error propagation. The whole process requires no information other than the input images. The results illustrate that our system is capable of producing accurate scene structure and realistic 3D models within a few minutes.

An algorithm for surface fitting to curves with energy control is proposed in this paper. Given four boundary curves and a set of unorganized curves, we impose the constrained energy on the desired surface, and covert the minimum energy problem into a linear equation system of the control points of the surface. We prove that there is one unique solution of this equation system. The proposed algorithm is independent of the coordinate system, and experience shows that the resultant surface is fair.

We propose a novel color based tracking framework in which an object configuration and color feature are simultaneously determined via scale space filtration. The tracker can automatically select discriminative color feature that well distinguishes foreground from background. According to that feature, a likelihood image of the target is generated for each incoming frame. The target’s area turns into a blob in the likelihood image. The scale of this blob can be determined based on the local maximum of differential scale-space filters. We employ the QP_TR trust region algorithm to search for the local maximum of multi-scale normalized Laplacian filter of the likelihood image to locate the target as well as determine its scale. Based on the tracking results of sequence examples, the proposed method has been proven to be resilient to the color and lighting changes, be capable of describing the target more accurately and achieve much better tracking precision.

In this paper, we present a self-calibration strategy to estimate camera intrinsic and extrinsic parameters using the scale-invariant feature transform (SIFT). The accuracy of the estimated parameters depends on how reliably a set of image correspondences is established. The SIFT employed in the self-calibration algorithms plays an important role in accurate estimation of camera parameters, because of its robustness to changes in viewing conditions. Under the assumption that the camera intrinsic parameters are constant, experimental results show that the SIFT-based approach using two images yields more competitive results than the existing Harris corner detector-based approach using two images.

In this paper, we present an improved face recognition algorithm using extended modular principal component analysis (PCA). The proposed method, when compared with a regular PCA-based algorithm, has significantly improved recognition rate with large variations in pose, lighting direction, and facial expression. The face images are divided into multiple, smaller blocks based on the Gaussian model and we use the PCA approach to these combined blocks for obtaining two eyes, nose, mouth, and glabella. Priority for merging blocks is decided by using fuzzy logic. Some of the local facial features do not vary with pose, lighting direction, and facial expression. The proposed technique is robust against these variations.