'Visual data' presentation slideshows

Introduction to NVivo Application

Introduction to NVivo Application. Dr. Roshartini Omar Faculty of Technology Management and Business (FPTP) J701-12 (Level 7). Session Aim and Objectives. Aim To introduce students to the analysis of qualitative data Objectives By the end students will have an appreciation of:

1.41k views • 34 slides

The Problem with the Blank Slate Approach

The Problem with the Blank Slate Approach. Derek Hoiem Beckman Institute, UIUC. Scene Understanding: What We Have. 7. TENT. DOG. GROUND. Scene Understanding: What We Have (More Realistically). 7. CAR. GROUND. Scene Understanding: What We Want.

431 views • 31 slides

The Problem with the Blank Slate Approach

The Problem with the Blank Slate Approach. Derek Hoiem Beckman Institute, UIUC * unpublished *. Scene Understanding: What We Have. 7. TENT. DOG. GROUND. Scene Understanding: What We Have (More Realistically). 7. CAR. GROUND. Scene Understanding: What We Want.

365 views • 31 slides

Advanced Methods of Interpretation Lecture VII Interpretation of Images Dr. Werner Binder

Advanced Methods of Interpretation Lecture VII Interpretation of Images Dr. Werner Binder. Masaryk University, Brno Faculty of Social Studies Department of Sociology. Advanced Methods of Interpretation in Cultural Sociology (soc 575) Spring 2017. Masaryk University

268 views • 26 slides

Multimodal Analysis Workshop: Visual Data

357 views • 21 slides

Visualization of Graph Structures with Magnetic-Spring Model and Color-Coded Interaction

Visualization of Graph Structures with Magnetic-Spring Model and Color-Coded Interaction. Mg.sc.ing. , doctoral student Vitaly Zabiniako Riga Technical University, Latvia. Baltic DB&IS 2012. Problem domain (1/4). Simplicity and complexity of visual data: Formalization:. Simple. Complex.

314 views • 20 slides

Goals

Good Morning! Discuss your answers to Journal 1 with a partner Pay attention to the skulls of these animals (they all eat meat) What animals might these be? Would that help you in your conclusion?. Goals. Identify different types of visual data Understand why we use visual data

204 views • 20 slides

Multimodal Emotion Recognition

Multimodal Emotion Recognition. Colin Grubb Advisor: Nick Webb. Motivation. Multimodal fusion Research looking at audio, visual, and gesture information Feature Level vs. Decision Level. Previous Research.

835 views • 19 slides

Distributed Temperature Sensing - Anglian Water Trial

344 views • 17 slides

CS 764 Seminar in Computer Vision

278 views • 17 slides

Toward Dynamic Grasp Acquisition: The G-SLAM Problem

Toward Dynamic Grasp Acquisition: The G-SLAM Problem. Li (Emma) Zhang and Jeff Trinkle Department of Computer Science Rensselaer Polytechnic Institute. G-SLAM Problem. The G-SL(AM) 2 problem is to autonomous robotic grasping, what the SLAM problem is to autonomous robotic mobility.

253 views • 15 slides

Creating Custom Dashboards with Insight Analyzer

232 views • 14 slides

Title

347 views • 13 slides

hardware

241 views • 11 slides

Qualitative Data Analysis : An Introduction

Qualitative Data Analysis : An Introduction. Carol Grbich Chapter 21: An overview of qualitative computer management programs. Qualitative Compter management programs. Qualitative computing packages comprise three main types: code and retrieve theory generation content analysis

314 views • 11 slides

Qualitative data analysis : An introduction

Qualitative data analysis : An introduction. Carol Grbich Chapter 16 : Visual data analysis. Visual data. Visual records fall into three groupings: A. Visual records currently in existence such as: film newspaper images artwork (paintings, posters, cartoons) computer images

113 views • 11 slides

GNORASI WORKSHOP WELCOME

221 views • 8 slides

A Lightweight Computer Vision-based Electronic Travel Aid

A Lightweight Computer Vision-based Electronic Travel Aid. Andrew B. Raij ( raij@cs.unc.edu ) Enabling Tech Project Status Report 3/6/2003. Department of Computer Science, UNC-Chapel Hill. High-Level (Long Term?) Goal.

206 views • 7 slides

WEEK2

WEEK2. DATA AND INFORMATION. Data is a collection of facts, figures and statistics related to an object. It is any facts, numbers or text that can be processed by a computer. Also, data is unprocessed or raw facts or unorganized facts that need to be processed.

285 views • 7 slides

Abstract, 60pt

This would be the area for your title, which should be at least 72 ppi tall, which is equal to one inch (this is 100 points). This font is 72 ppi. If you have room, using the logos is a good idea . Methods. Results. Abstract, 60pt. Experiments. Conclusions. Discussion.

113 views • 5 slides