KNOWLEDGE MIS-MANAGEMENT USF The University of Sigmund Freud.

Slides:



Advertisements
Similar presentations
Pseudo-Relevance Feedback For Multimedia Retrieval By Rong Yan, Alexander G. and Rong Jin Mwangi S. Kariuki
Advertisements

Towards a Quadratic Time Approximation of Graph Edit Distance Fischer, A., Suen, C., Frinken, V., Riesen, K., Bunke, H. Contents Introduction Graph edit.
Chapter 8 Enhancing Learning with Visuals
STUDYING COLLEGE TEXTBOOKS AND INTERPRETING VIAUAL AND GRAPHIC AIDS
An Introduction to Sparse Coding, Sparse Sensing, and Optimization Speaker: Wei-Lun Chao Date: Nov. 23, 2011 DISP Lab, Graduate Institute of Communication.
Principal Component Analysis CMPUT 466/551 Nilanjan Ray.
© 2003 by Davi GeigerComputer Vision September 2003 L1.1 Face Recognition Recognized Person Face Recognition.
Machine Vision and Dig. Image Analysis 1 Prof. Heikki Kälviäinen C50A6100 Lectures 12: Object Recognition Professor Heikki Kälviäinen Machine Vision and.
Chapter Sixteen EXPLORING, DISPLAYING, AND EXAMINING DATA
1 International Workshop on Computer Vision April 26-30, 2004 Tehran,Iran Singular Value Decompositions with applications to Singular Value Decompositions.
From Prototypes to Abstract Ideas A review of On The Genesis of Abstract Ideas by MI Posner and SW Keele Siyi Deng.
Dimension of Meaning Author: Hinrich Schutze Presenter: Marian Olteanu.
Face Recognition: An Introduction
HCC class lecture 14 comments John Canny 3/9/05. Administrivia.
Multimedia By: Hector.Grijalva Period.1. What is meant by multimedia? Multimedia is media and content that uses a combination of different content forms.
Presenting by, Prashanth B R 1AR08CS035 Dept.Of CSE. AIeMS-Bidadi. Sketch4Match – Content-based Image Retrieval System Using Sketches Under the Guidance.
Computer vision: models, learning and inference
Chapter 2 Dimensionality Reduction. Linear Methods
Presented By Wanchen Lu 2/25/2013
CS 376b Introduction to Computer Vision 04 / 29 / 2008 Instructor: Michael Eckmann.
Image recognition using analysis of the frequency domain features 1.
Extended Assessments Elementary Mathematics Oregon Department of Education and Behavioral Research and Teaching January 2007.
BACKGROUND LEARNING AND LETTER DETECTION USING TEXTURE WITH PRINCIPAL COMPONENT ANALYSIS (PCA) CIS 601 PROJECT SUMIT BASU FALL 2004.
Texture scale and image segmentation using wavelet filters Stability of the features Through the study of stability of the eigenvectors and the eigenvalues.
Pseudo-supervised Clustering for Text Documents Marco Maggini, Leonardo Rigutini, Marco Turchi Dipartimento di Ingegneria dell’Informazione Università.
Chapter Five Nonlinguistic Representation. Nonlinguistic representation enhances a students’ ability to use mental images to represent and elaborate on.
SVD ? name : Bei Wang COM471 Algorithms and Mathematics for Games and Graphics 19/03/2015.
Understanding The Semantics of Media Chapter 8 Camilo A. Celis.
1 Pattern Recognition Pattern recognition is: 1. A research area in which patterns in data are found, recognized, discovered, …whatever. 2. A catchall.
Face Recognition: An Introduction
Spreadsheet Charts Vocabulary Reference – pp. IE123-IE127 in Office 2000 Textbook Do not login. You will need a writing utensil.
Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.
©Brooks/Cole, 2003 Chapter 2 Data Representation.
Computer Graphics and Image Processing (CIS-601).
A graphical display should: Show the data Induce the viewer to think about the substance of the graphic Avoid distorting the message.
1 CSC 594 Topics in AI – Text Mining and Analytics Fall 2015/16 6. Dimensionality Reduction.
Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.
Shuai Zheng TNT group meeting 1/12/2011.  Paper Tracking  Robust view transformation model for gait recognition.
A NOVEL METHOD FOR COLOR FACE RECOGNITION USING KNN CLASSIFIER
Interactive Evolution in Automated Knowledge Discovery Tomáš Řehořek March 2011.
Principle Component Analysis and its use in MA clustering Lecture 12.
CS559: Computer Graphics Lecture 3: Digital Image Representation Li Zhang Spring 2008.
Non-Fiction Text Features Vocabulary
Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.
Principal Component Analysis (PCA).
2D-LDA: A statistical linear discriminant analysis for image matrix
Thinking Like a Scientist: Observing Ask questions like: What? How? Why?
Non-parametric Methods for Clustering Continuous and Categorical Data Steven X. Wang Dept. of Math. and Stat. York University May 13, 2010.
Martina Uray Heinz Mayer Joanneum Research Graz Institute of Digital Image Processing Horst Bischof Graz University of Technology Institute for Computer.
Chapter 6.20: Presentation Aids “A picture is worth a thousand words.”
Data Science Dimensionality Reduction WFH: Section 7.3 Rodney Nielsen Many of these slides were adapted from: I. H. Witten, E. Frank and M. A. Hall.
Visual Information Processing. Human Perception V.S. Machine Perception  Human perception: pictorial information improvement for human interpretation.
Computer Vision COURSE OBJECTIVES: To introduce the student to computer vision algorithms, methods and concepts. EXPECTED OUTCOME: Get introduced to computer.
What is an infographic?.
S.Rajeswari Head , Scientific Information Resource Division
Graphs Earth Science.
COMPUTING BTEC LEVEL /17.
Preparing and Interpreting Tables, Graphs and Figures
X AND R CHART EXAMPLE IN-CLASS EXERCISE
CSc4730/6730 Scientific Visualization
Principal Component Analysis
Outline H. Murase, and S. K. Nayar, “Visual learning and recognition of 3-D objects from appearance,” International Journal of Computer Vision, vol. 14,
Stylometry and Authorship
Digital Imagery: The Basic Idea.
Random Rectangles When given the cue turn the paper over. Within 5 seconds make a guess for the average area of the rectangles. When given the cue turn.
Data Representation Chapter 2 Computer HW (Von Neumann Model) Program
Image Compression via SVD
Motivation Semantic Transformation Module Most of the existing works neglect the semantic relationship between the visual feature and linguistic knowledge,
Latent Semantic Analysis
PRESENTER GOES HERE SERVICE LINE GOES HERE (BOTH IN ALL CAPS)
Presentation transcript:

KNOWLEDGE MIS-MANAGEMENT USF The University of Sigmund Freud

The current research Mental Mapping and Multi-media Analysis (MMAMMA) An analysis of graphical content in JASIST

Procedure Study of cluster and other similar graphics Study of columnar and bar charts, graphs and tables

Procedural Issues Digital Library STILL unavailable Weeded extraneous data such as –Text –Context –Kept some pretext (of scholarship) Razored graphics from paper JASIST issues Scanned into Adobe Acrobat

Processed With Updated Image Recognition Algorithm The eigenspace representation of images has attracted much attention recently among vision researchers. The basic idea is to represent images or image features in a transformed space where the individual features are uncorrelated. For a given set of (deterministic) images this can be achieved by performing the Singular Value Decomposition (SVD). The statistical equivalent of this is the Karhunen-Loeve Transform (KLT) which is computed by diagonalizing the autocorrelation matrix of the image ensemble. Both are well known techniques in image processing. However, they are computationally expensive. S. Chandrasekaran †, B.S. Manjunath †, Y.F. Wang ‡, J. Winkeler †, and H. Zhang ‡ (1996)

Cluster Data Median Transformation

Columnar and Chart Data

Findings Researchers Anderson and Lee (Cluster data) were concerned with: –Bilaterality –Symmetry –Data Stability Plotted data seemed to change size frequently –Data life Plot of older data revealed ellipsoid pattern

Findings Researchers Dole and Palmiero (Columnar data) were concerned with: –Sample Size –Data Stability –Data Permanence In some cases data was not available for all intended uses –Data life Plot of older data revealed more horizontal pattern

Conclusions It is Data when you store it, Information when you find it and Knowledge when you use it Sometimes data is just data, Ada.

Thank you! Your time is up, that will be 90 dollars!