Approximations by Smooth Transitions in Binary Space Partitions Marcos Lage, Alex Bordignon, Fabiano Petronetto, Álvaro Veiga, Geovan Tavares, Thomas Lewiner,

Slides:

Advertisements

Similar presentations

Random Walks for Vector Field Denoising João Paixão, Marcos Lage, Fabiano Petronetto, Alex Laier, Sinésio Pesco, Geovan Tavares, Thomas Lewiner, Hélio.

Advertisements

CS Fall 2012, Lab 08 Haohan Zhu. Boston University Slideshow Title Goes Here CS Fall 2012, Lab /17/2015 Tree - Data Structure  Basic.

1 Non-Linear and Smooth Regression Non-linear parametric models: There is a known functional form y=  x,  derived from known theory or from previous.

1/23 Vector field reconstruction from sparse samples with applications Marcos Lage, Fabiano Petronetto, Afonso Paiva, Hélio Lopes, Thomas Lewiner and Geovan.

Exploratory visualization based on multidimensional transfer functions and star coordinates Alex Laier Bordignon, Rener Castro, Hélio Lopes, Thomas Lewiner.

Yoshiharu Ishikawa (Nagoya University) Yoji Machida (University of Tsukuba) Hiroyuki Kitagawa (University of Tsukuba) A Dynamic Mobility Histogram Construction.

Classic Subdivision Schemes. Schemes Catmull-Clark (1978) Doo-Sabin (1978) Loop (1987) Butterfly (1990) Kobbelt (1996) Mid-edge (1996 / 1997)

Quadtrees, Octrees and their Applications in Digital Image Processing

Spatial Indexing I Point Access Methods. PAMs Point Access Methods Multidimensional Hashing: Grid File Exponential growth of the directory Hierarchical.

Asst. Prof. Yusuf Sahillioğlu

GATE D Object Representations (GATE-540) Dr.Çağatay ÜNDEĞER Instructor Middle East Technical University, GameTechnologies & General Manager SimBT.

Point-set compression through BSP quantization A. Bordignon, T. Lewiner, H. Lopes, G. Tavares and R. Castro Departamento de Matemática – PUC-Rio.

Robust Adaptive Meshes for Implicit Surfaces Afonso Paiva Hélio Lopes Thomas Lewiner Matmidia - Departament of Mathematics – PUC-Rio Luiz Henrique de Figueiredo.

Quadtrees, Octrees and their Applications in Digital Image Processing

Vieira et al. - Fast Stellar Mesh Simplification 1 Fast Stellar Mesh Simplification Antônio W. Vieira 1,2 Luiz Velho 3 Hélio Lopes 1 Geovan Tavares 1 Thomas.

2003/02/14Chapter 2 1頁1頁 Chapter 2 : The Complex Function & Its Derivative 2.1 Introduction.

Arc-Length Based Curvature Estimator Thomas Lewiner, João D. Gomes Jr., Hélio Lopes, Marcos Craizer { tomlew, jgomes, lopes, craizer mat.puc-rio.br.

Comp 775: Deformable models: snakes and active contours Marc Niethammer, Stephen Pizer Department of Computer Science University of North Carolina, Chapel.

Gait Recognition Simon Smith Jamie Hutton Thomas Moore David Newman.

Statistical Learning: Pattern Classification, Prediction, and Control Peter Bartlett August 2002, UC Berkeley CIS.

Spatial Indexing I Point Access Methods. Spatial Indexing Point Access Methods (PAMs) vs Spatial Access Methods (SAMs) PAM: index only point data Hierarchical.

Contributed Talk at the International Workshop on VISUALIZATION and MATHEMATICS 2002 Thomas Lewiner, Hélio Lopes, Geovan Tavares Math&Media Laboratory,

Spatial Indexing I Point Access Methods. Spatial Indexing Point Access Methods (PAMs) vs Spatial Access Methods (SAMs) PAM: index only point data Hierarchical.

Particle-Based non-Newtonian Fluid Animation for Melting Objects Afonso Paiva Fabiano P. do Carmo Thomas Lewiner Geovan Tavares Matmidia - Departament.

FLANN Fast Library for Approximate Nearest Neighbors

Numerical Schemes for Advection Reaction Equation Ramaz Botchorishvili Faculty of Exact and Natural Sciences Tbilisi State University GGSWBS,Tbilisi, July.

Classification and Prediction: Regression Analysis

Sensitivity Analysis, Multidisciplinary Optimization, Robustness Evaluation, and Robust Design Optimization with optiSLang 3.2.

Data Structures for Computer Graphics Point Based Representations and Data Structures Lectured by Vlastimil Havran.

July 11, 2001Daniel Whiteson Support Vector Machines: Get more Higgs out of your data Daniel Whiteson UC Berkeley.

Graphics Graphics Korea University cgvr.korea.ac.kr Creating Virtual World I 김 창 헌 Department of Computer Science Korea University

Kansas State University Department of Computing and Information Sciences CIS 736: Computer Graphics Wednesday, February 23, 2000 William H. Hsu Department.

Graphics Graphics Korea University cgvr.korea.ac.kr Solid Modeling 고려대학교 컴퓨터 그래픽스 연구실.

CSC418 Computer Graphics n BSP tree n Z-Buffer n A-buffer n Scanline.

Monday, October 12, 2015Monday, October 12, 2015Monday, October 12, 2015Monday, October 12, 2015Sibgrapi Natal1 CHF: A Scalable Topological Data.

Total Variation and Euler's Elastica for Supervised Learning

Logistic Regression Debapriyo Majumdar Data Mining – Fall 2014 Indian Statistical Institute Kolkata September 1, 2014.

Quadtrees, Octrees and their Applications in Digital Image Processing.

Basis Expansions and Regularization Part II. Outline Review of Splines Wavelet Smoothing Reproducing Kernel Hilbert Spaces.

The Haar + Tree: A Refined Synopsis Data Structure Panagiotis Karras HKU, September 7 th, 2006.

Optimal Malliavin weighting functions for the simulations of the Greeks MC 2000 (July ) Eric Ben-Hamou Financial Markets Group London School of.

Stefan Popov Space Subdivision for BVHs Stefan Popov, Iliyan Georgiev, Rossen Dimov, and Philipp Slusallek Object Partitioning Considered Harmful: Space.

CS654: Digital Image Analysis Lecture 30: Clustering based Segmentation Slides are adapted from:

1 CSCI 3202: Introduction to AI Decision Trees Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell) Intro AIDecision Trees.

Classification (slides adapted from Rob Schapire) Eran Segal Weizmann Institute.

Vector Valued Functions

KNN Classifier.  Handed an instance you wish to classify  Look around the nearby region to see what other classes are around  Whichever is most common—make.

Intelligent Database Systems Lab Advisor ： Dr. Hsu Graduate ： Chien-Shing Chen Author ： Jessica K. Ting Michael K. Ng Hongqiang Rong Joshua Z. Huang 國立雲林科技大學.

Machine Vision ENT 273 Regions and Segmentation in Images Hema C.R. Lecture 4.

AQUARadar Identification of temporally stable Z-R relationships using measurements of micro-rain radars M. Clemens (1), G. Peters (1), J. Seltmann (2),

Measuring and analysing public expenditures Data management Joanna Komorowska.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology A support system for predicting eBay end prices Presenter.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Decision trees for hierarchical multi-label classification.

Bump Hunting The objective PRIM algorithm Beam search References: Feelders, A.J. (2002). Rule induction by bump hunting. In J. Meij (Ed.), Dealing with.

Stable Runge-Free, Gibbs-Free Rational Interpolation Scheme: A New Hope A New Hope Qiqi Wang.

Title of proposal Smart Spaces 2015 Proposers (name, organization, )  … Description of idea  …

Combining Models Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya.

Multiple Organ detection in CT Volumes Using Random Forests

Machine Vision ENT 273 Lecture 4 Hema C.R.

Data Transformation: Normalization

Ch 14. Combining Models Pattern Recognition and Machine Learning, C. M

Cristian Ferent and Alex Doboli

Eco 6380 Predictive Analytics For Economists Spring 2014

FUZZIFICATION AND DEFUZZIFICATION

Lecture 4: Econometric Foundations

Foundation of Video Coding Part II: Scalar and Vector Quantization

Local Regression, LDA, and Mixed Model Lab

Anti-aliased and accelerated ray tracing

Using Manifold Structure for Partially Labeled Classification

Local Regression, LDA, and Mixed Model Lab

Presentation transcript:

Approximations by Smooth Transitions in Binary Space Partitions Marcos Lage, Alex Bordignon, Fabiano Petronetto, Álvaro Veiga, Geovan Tavares, Thomas Lewiner, Hélio Lopes Matmidia Labs - PUC-Rio - Brazil

Motivation

Outline Background Bsp CART STR-Trees Results Motivation Smooth transition BSP approximation Problem description Contributions Transition function Parameters Future works

Background (Binary Space Partition trees - BSP )

Background (Classification and regression trees - CART ) Indicator function

Background (Smooth transition regression trees – STR-Trees ) for example...

Smooth transition BSP approximation (problem description)

Contributions Simple and fast approximation scheme Hierarchical plane-basis function modeling Infinitely smooth and analytical derivation

Smooth transition BSP approximation (Transition function)

Smooth transition BSP approximation (Transition parameter) Lambda = 5Lambda = 10Lambda = 25Lambda = 50

BSP refinement criteria (kd-tree versus bsp)

KD-tree versus BSP

Results and applications

Results (vector-valued data) Bsp level = 11 Lambda = 11

Results (analytic derivatives) Bsp level = 11 Lambda = 5

Results (scalar-valued data) Bsp level = 11 Lambda = 10

Results (curve reconstruction) Bsp level = 11 Lambda = 2.85

Future works Extension to higher dimensions BSP refinement criterias Adaptative transition parameter