207688593
Inverse global illumination: recovering reflectance models of real scenes from photographs
In this paper we present a method for recovering the reflectance properties of all surfaces in a real scene from a sparse set of photographs, taking into account both direct and indirect illumination. The result is a lighting-independent model of the scene’s geometry and reflectance properties, which can be rendered with arbitrary modifications to structure and lighting via traditional rendering methods. Our technique models reflectance with a lowparameter reflectance model, and allows diffuse albedo to vary arbitrarily over surfaces while assuming that non-diffuse characteristics remain constant across particular regions. The method’s input is a geometric model of the scene and a set of calibrated high dynamic range photographs taken with known direct illumination. The algorithm hierarchically partitions the scene into a polygonal mesh, and uses image-based rendering to construct estimates of both the radiance and irradiance of each patch from the photographic data. The algorithm computes the expected location of specular highlights, and then analyzes the highlight areas in the images by running a novel iterative optimization procedure to recover the diffuse and specular reflectance parameters for each region. Lastly, these parameters are used in constructing high-resolution diffuse albedo maps for each surface. The algorithm has been applied to both real and synthetic data, including a synthetic cubical room and a real meeting room. Rerenderings are produced using a global illumination system under both original and novel lighting, and with the addition of synthetic objects. Side-by-side comparisons show success at predicting the appearance of the scene under novel lighting conditions. CR Categories: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding—modeling and recovery of physical attributes I.3.7 [Computer Graphics]: Three-dimensional Graphics and Realism—color, shading, shadowing, and texture I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism— Radiosity I.4.8 [Image Processing]: Scene Analysis—Color, photometry, shading
1542177
Out-of-core tensor approximation of multi-dimensional matrices of visual data
Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.
13369249
Reconstruction of 3-D Symmetric Curves from Perspective Images without Discrete Features
The shapes of many natural and man-made objects have curved contours. The images of such contours usually do not have sufficient distinctive features to apply conventional feature-based reconstruction algorithms. This paper shows that both the shape of curves in 3-D space and the camera poses can be accurately reconstructed from their perspective images with unknown point correspondences given that the curves have certain invariant properties such as symmetry. We show that in such cases the minimum number of views needed for a solution is remarkably small: one for planar curves and two for nonplanar curves (of arbitrary shapes), which is significantly less than what is required by most existing algorithms for general curves. Our solutions rely on minimizing the L 2-distance between the shapes of the curves reconstructed via the “epipolar geometry” of symmetric curves. Both simulations and experiments on real images are presented to demonstrate the effectiveness of our approach.
46180
Efficient visibility processing for projective texture mapping
Abstract Projective texture mapping is a powerful tool for image-based rendering. However, visibility information must be obtained in order to project correct textures onto the geometric sturcture. This paper presents a novel visibility algorithm for determining which polygons are visible from each camera position. It has object-space precision, but operates in both image-space and object-space to get better performance. It generates much less polygons than traditional object-space algorithms to help accelerate the speed of texture mapping. The techniques used to achieve these are conservative image-space occlusion testing and object-space shallow clipping . This algorithm has been successfully used to generate large-scale image-based rendering of a whole university campus, and image-based rendering of architectures under dynamic lighting.
10850316
Modeling realistic virtual hairstyles
The author presents an effective method for modeling realistic curly hairstyles, taking into account both artificial hairstyling processes and natural curliness. The result is a detailed geometric model of hairs that can be rendered and animated via existing methods. Our technique exploits the analogy between hairs and a vector field; it interactively and efficiently models global and local hair flows by superimposing procedurally defined vector field primitives that have local influence. Usually only a very small number of vector field primitives are needed to model a complicated hairstyle. An initial model of hair strands is extracted from the superimposed vector fields by tracing their field lines. Random natural or artificial curliness can be added to the initial model through a parametric hair offset function with a randomized distribution of parameters over the scalp. Techniques for shearing and clustering are also designed to improve the overall appearance of the hair model. Our technique has been successfully applied to generate a variety of realistic hairstyles with different curliness and length distributions.
13948350
Feature matching and deformation for texture synthesis
One significant problem in patch-based texture synthesis is the presence of broken features at the boundary of adjacent patches. The reason is that optimization schemes for patch merging may fail when neighborhood search cannot find satisfactory candidates in the sample texture because of an inaccurate similarity measure. In this paper, we consider both curvilinear features and their deformation. We develop a novel algorithm to perform feature matching and alignment by measuring structural similarity. Our technique extracts a feature map from the sample texture, and produces both a new feature map and texture map. Texture synthesis guided by feature maps can significantly reduce the number of feature discontinuities and related artifacts, and gives rise to satisfactory results.
12607560
Speckle reducing anisotropic diffusion
This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee and Frost filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.
40955801
Polarimetric SAR image segmentation using texture partitioning and statistical analysis
This paper presents a new technique for partitioning polarimetric synthetic aperture radar (POL-SAR) imagery into regions of homogeneous polarimetric backscattering properties. The method consists of two cascaded steps: an initial texture segmentation of one image derived from the POL-SAR data, and a polarimetric statistical region merging process that refines and modifies the initial segmentation. In the first stage, a morphological region-based image partitioning technique, the watershed algorithm, plays the key role. While in the second stage, the region adjacency graph and the segment dissimilarity measures derived from the Wishart model and the K distribution are applied. The overall segmentation algorithm has been tested using real 4-look POL-SAR imagery and sample results are provided in the paper. The main innovation of this work is the utilization of the K distribution in the segmentation process.
36949782
Adaptation of the transfer function of the Hodgkin-Huxley (HH) neuronal model
Abstract The transfer functions of sensory neurons are known to adapt to the statistics of the input signals. It is however unclear whether such adaptation arises from the nonlinear dynamics of the neuron or emerges from the collective interaction among neurons embedded in a network. We investigated the Hodgkin–Huxley (HH) neuronal model's response to Gaussian white noise signals of different variances and found that the recovered kernel adapt its preferred temporal frequency and its energy gains according to noise variance. This adaptation is likely a consequence of the cooperative interaction between the noises and the bifurcation dynamics of the neurons.
13290415
Quality-Based Software Reuse
Work in software reuse focuses on reusing artifacts. In this context, finding a reusable artifact is driven by a desired functionality. This paper proposes a change to this common view. We argue that it is possible and necessary to also look at reuse from a non-functional (quality) perspective. Combining ideas from reuse, from goal-oriented requirements, from aspect-oriented programming and quality management, we obtain a goal-driven process to enable the quality-based reusability.
1353091
A Lightweight Approach to Semantic Web Service Synthesis
Web service technologies are becoming a new paradigm for distributed computing. With increasing number of web services available on the internet, there is an urgent need for information brokers that can autonomously integrate web services on behalf of a user. To this end, we propose web service specification, which defines what the service is but not how the service is implemented, and service synthesizer, which can dynamically synthesize the implementation of the specification from existing web services over the web.