'Closest points' diaporamas de présentation

Advanced Computer Vision

Advanced Computer Vision . Chapter 5 Segmentation. Presented by: ??? & ??? r99922094@ntu.edu.tw 0928083710. Segmentation (1/2). 5.1 Active Contours 5.2 Split and Merge 5.3 Mean Shift and Mode Finding 5.4 Normalized Cuts 5.5 Graph Cuts and Energy-based Methods. Segmentation (2/2).

858 views • 69 slides

Clustering

639 views • 51 slides

Collision recognition from a video part A

Collision recognition from a video part A . Students: Adi Vainiger , Eyal Yaacoby Supervisor: Netanel Ratner Laboratory of Computer Graphics & Multimedia Electrical Engineering faculty, Technion Semester: Winter 2012. Objective. Design a system with two main roles:

606 views • 50 slides

Clustering

452 views • 44 slides

Clustering

443 views • 44 slides

Soft Large Margin classifiers

654 views • 38 slides

Clustering

Clustering. Overview. What is clustering? Similarity/distance metrics Hierarchical clustering algorithms K-means. What is clustering?. Group similar objects together Objects in the same cluster (group) are more similar to each other than objects in different clusters

629 views • 37 slides

Physics in games

CS 599 Physically Based Modeling for Interactive Simulation and Games Case Study: Havok Engine for Game Physics Jernej Barbic University of Southern California. Physics in games. Custom, in-house software Off-the shelf libraries Physics middleware. Physics Engines. Real-time Video games

655 views • 32 slides

Machine learning

Machine learning. Image source: https://www.coursera.org/course/ml. Machine learning. Definition Getting a computer to do well on a task without explicitly programming it Improving performance on a task based on experience. Learning for episodic tasks.

491 views • 32 slides

Instance Based Learning

Instance Based Learning. Nearest Neighbor. Remember all your data When someone asks a question Find the nearest old data point Return the answer associated with it In order to say what point is nearest, we have to define what we mean by "near".

325 views • 25 slides

CSE 326: Data Structures Lecture #24 The Algorhythmics

225 views • 22 slides

Image classification

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?. Classifiers. Learn a decision rule assigning bag-of-features representations of images to different classes. Decision boundary. Zebra. Non-zebra.

353 views • 21 slides

Continuous or discontinuous?

Continuous or discontinuous?. By: Dylan Shupe. At what x-value does the discontinuity occur?. What happens to point Q when you drag point A all the way to the right?. What happens to the coordinates of point Q when you drag point A as far right as it can go?.

504 views • 21 slides

Shape inference for sheet of paper with text via characteristic strips

Shape inference for sheet of paper with text via characteristic strips. Project by Arie Kozak. Introduction and goals. Given photograph with sheet of paper with text only, infer shape of the surface and plot it in 3d.

288 views • 19 slides

St. Kitts and Nevis Health Situation Analysis

St. Kitts and Nevis Health Situation Analysis. Group #2. Background Information. Saint Kitts (also called Saint Christopher) and Nevis is located in the northern part of the Leeward Islands chain in the Caribbean.

323 views • 16 slides

MA/CSSE 473 Day 16

MA/CSSE 473 Day 16. Answers to your questions Divide and Conquer Closest Points Convex Hull intro. Exercise from last time. Which permutation follows each of these in lexicographic order? 183647520 471638520

164 views • 16 slides

Nearest-Neighbor Searching Under Uncertainty II

Nearest-Neighbor Searching Under Uncertainty II. Nearest Neighbor (NN) Searching. Post office problem. : a set of points. : any query point. Find the closest one. Voronoi Diagram. Voronoi cell : Voronoi diagram : decomposition induced by . Data Uncertainty.

256 views • 14 slides

3.7 Optimization

223 views • 13 slides

Determining College Football Rankings

156 views • 8 slides

Support Vector Machines Exercise solutions

Support Vector Machines Exercise solutions. Ata Kaban The University of Birmingham. Exercise 1. What is the main idea behind linear Support Vector Machines (SVM)? Illustrate your explanation by drawing a figure. ANSWER

165 views • 5 slides