This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

/pdf/smote-for-learning-from-imbalanced-data-progress-and-3slcu62yo6.pdf

SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

Assisted by recent advances in programmable graphics hardware, fast rasterization-based techniques have made significant progress in photorealistic rendering, but still only render a subset of the effects possible with ray tracing. We are closing this gap with the implementation of ray-triangle intersection as a pixel shader on existing hardware. This GPU ray-intersection implementation reconfigures the geometry engine into a ray engine that efficiently intersects caches of rays for a wide variety of host-based rendering tasks, including ray tracing, path tracing, form factor computation, photon mapping, subsurface scattering and general visibility processing.

The ray engine

Point sets become an increasingly popular shape representation. Most shape processing and rendering tasks require the approximation of a continuous surface from the point data. We present a surface approximation that is motivated by an efficient iterative ray intersection computation. On each point on a ray, a local normal direction is estimated as the direction of smallest weighted co-variances of the points. The normal direction is used to build a local polynomial approximation to the surface, which is then intersected with the ray. The distance to the polynomials essentially defines a distance field, whose zero-set is computed by repeated ray intersection. Requiring the distance field to be smooth leads to an intuitive and natural sampling criterion, namely, that normals derived from the weighted co-variances are well defined in a tubular neighborhood of the surface. For certain, well-chosen weight functions we can show that well-sampled surfaces lead to smooth distance fields with non-zero gradients and, thus, the surface is a continuously differentiable manifold. We detail spatial data structures and efficient algorithms to compute ray-surface intersections for fast ray casting and ray tracing of the surface.

/pdf/approximating-and-intersecting-surfaces-from-points-27ddt7aav9.pdf

Approximating and intersecting surfaces from points

Efficient shallow water simulations on GPUs: Implementation, visualization, verification, and validation

An octree is a well known hierarchical spatial structure which is widely used in Computer Graphics algorithms.
One of the most frequent operations is the computation of the octree voxels intersected by a
straight line. This has a number of applications, such as ray-object intersection tests speed-up and visualisation
of hierarchical density models by ray-casting. Several methods have been proposed to achieve this
goal, which differ in the order in which intersected voxels are visited. In this paper we introduce a new
top-down parametric method. The main difference with previously proposed methods is related to descent
movements, that is, the selection of a child sub-voxel from the current one. This selection, as the algorithm,
is based on the parameter of the ray and comprises simple comparisons. The resulting algorithm is easy to
implement, and efficient when compared to other related top-down and bottom-up algorithms for octrees.
Finally, a comparison with Kelvin’s method for binary trees is presented.

/pdf/an-efficient-parametric-algorithm-for-octree-traversal-k86p1sj0jm.pdf

An efficient parametric algorithm for octree traversal

Fingerprints are the biometric features most used for identification. They can be characterized through some particular elements called minutiae. The identification of a given fingerprint requires the matching of its minutiae against the minutiae of other fingerprints. Hence, fingerprint matching is a key process. The efficiency of current matching algorithms does not allow their use in large fingerprint databases; to apply them, a breakthrough in running performance is necessary. Nowadays, the minutia cylinder-code (MCC) is the best performing algorithm in terms of accuracy. However, a weak point of this algorithm is its computational requirements. In this paper, we present a GPU fingerprint matching system based on MCC. The many-core computing framework provided by CUDA on NVIDIA Tesla and GeForce hardware platforms offers an opportunity to enhance fingerprint matching. Through a thorough and careful data structure, computation and memory transfer design, we have developed a system that keeps its accuracy and reaches a speed-up up to 100.8× compared with a reference sequential CPU implementation. A rigorous empirical study over captured and synthetic fingerprint databases shows the efficiency of our proposal. These results open up a whole new field of possibilities for reliable real time fingerprint identification in large databases.

A High Performance Fingerprint Matching System for Large Databases Based on GPU

Simulation of shallow-water systems using graphics processing units

A high performance memetic algorithm for extremely high-dimensional problems

https://eprints.ncl.ac.uk/file_store/production/227732/1F71F3CA-18A3-4C2D-84F4-C31EA219C6FC.pdf

Miguel Lastra

Papers

An efficient parametric algorithm for octree traversal

A High Performance Fingerprint Matching System for Large Databases Based on GPU

Simulation of shallow-water systems using graphics processing units

A high performance memetic algorithm for extremely high-dimensional problems

GPU-SME-kNN