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Instructions for TINA KNOPPIX
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Running the tinatools
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TINA consists of a pair of libraries that contain code designed for use by machine vision or image analysis
researchers. The algorithms provided by TINA have been chosen according to a strict set of criteria: they must
be statistically valid and numerically stable. The libraries contain a set of "tools" which provide GUI access
to this functionality. In order to use the tools, you must build a "toolkit", a simple C program that accesses
the tools. We have provided an example toolkit, together with a set of macros, designed to allow users to
complete the practical sessions that form a part of the MSc course in adavanced computer vision taught by
the TINA core programmers. You can run these toolkits via the desktop icons: refer to the
MSc Machine Vision Course : Practicals document for instructions. Working
through these practicals will provide an overview of the basic functionality of TINA.
The TINA sources are located in /usr/local/Tina5. The subdirectory .../tina-tools/toolkits/knoppix_toolkit
contains ths example toolkit and macros. Sample images are provided in the directory "images"
(relative to the tinaTool).
The following documents describe how to use and program with TINA5.
TINA 5.0 Programmer's Guide
Neil Thacker
Tina 5.0 User's Guide
Edited by Neil Thacker
TINA 5.0 Programmer's Reference
Edited by Neil Thacker
MSc Machine Vision Course : Practicals
N.A.Thacker and P.A. Bromiley
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Saving images from TINA
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The usual procrdure for saving any image from TINA is to click on the "view" tool from the top-level tinatool
window, select the TV whose image you want to save (by clicking in the relevant "TV" button in the corresponding
tool: the name of the TV will then appear in the view tool TV field), clicking on "dump" in the view tool to
open the dump tool window, and finally entering the path for the saved image into the dump tool, selecting the
colour depth of the output image, and clicking on "TIFF" or "EPS" in the dump tool to output the image in that
format. However, the TINA KNOPPIX CD runs a linux operating system from the CD: you cannot write to the CD, and
so saving images requires a few more steps. KNOPPIX provides a home area called /home/knoppix: save your images
from TINA here. This area exists in ramdisk, so will be lost when you reboot.
To save images permanently, you must copy them to a permanent location, for example a memory stick, floppy disk
or hard disk. Copying the files to a hard disk presents complications: for example, linux cannot natively mount
NTFS partitions: you must use CaptiveNTFS (provided with KNOPPIX) to mount them. Since most modern computers
have WinXP installed, I will assume that you either know enough about hard disk partitioning to skip these
instuctions, or don't know enough to write safely to your hard disk.
So, with that out of the way, you must mount a floppy disk or memory stick. Insert said device, and then open
a terminal (the icon that looks like a computer monitor on the bottom left-hand side of the desktop). Then,
if you are using a memory stick, type
su root
mkdir /mnt/sda1
mount /dev/sda1 /mnt/sda1
cp /home/knoppix/image_filename /mnt/sda1/image_filename
Repeat the copy command for each image you have saved. For a floppy disk, replace /dev/sda1 with /dev/floppy.
Some memory sticks are partitioned in a more complex way: you may have to try sda4 instead of sda1 if the above
commands give you an error.
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KNOPPIX Documentation
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KNOPPIX cheatcodes
KNOPPIX FAQ
KNOPPIX License
KNOPPIX Security README
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Printers
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Printers are not set up when KNOPPIX boots: use the wizard (found in the start menu) to set them up.
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TINA Memos
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The TINA memo system contains preprints and extended versions of our published papers, together with internal
reports on the algorithms incorporated into TINA, mathematical derivations etc. The most recent copies of the
memos are available online here. The TINA KNOPPIX CD
contains the TINA memos at the time of writing.
Statistical Analysis of a Stereo Matching Algorithm.
N.A.Thacker and P.Courtney.
Tutorial: Overview of Stereo Matching Research.
R.A.Lane and N.A.Thacker.
A Correlation Chip for Stereo Vision.
R.A.Lane and N.A.Thacker.
Using a Switchable Model Kalman Filter.
A.J.Lacey, N.A.Thacker and N.L.Seed.
Specification and Design of a General Purpose Image Processing Chip.
N.A.Thacker, P.Courtney, S.N.Walker, S.J.Evans and R.B.Yates.
Calibrating a 4 DOF Stereo Head.
N. A. Thacker
Invariance Network Architecture.
A.J.Lacey, N. A. Thacker and N.L.Seed
Assessing the Completeness Properties of Pairwise Geometric Histograms.
N.A.Thacker, P.A.Riocreux, and R.B.Yates.
Tina Algorithms's Guide: (Beyond Geometric Vision....)
Neil Thacker.
Algorithmic Modelling for Performance Evaluation.
N.A.Thacker, P.Courtney and A.Clark.
Tutorial: The Kalman Filter.
N.A.Thacker, A.J.Lacey.
Tutorial: Algorithms For 2-Dimensional Object Recognition.
A.Ashbrook and N.A.Thacker.
Robust Recognition of Scaled Shapes Using Pairwise Geometric Histograms.
A.P.Ashbrook, N.A.Thacker, P.I.Rockett and C.I.Brown.
The Bhattacharyya Metric as an Absolute Similarity Measure for Frequency Coded Data.
N. A. Thacker, F. J. Aherne and P. I. Rockett
Supervised Learning Extensions to the CLAM Network.
N. A. Thacker, I.A.Abraham and P.Courtney.
Tutorial: Supervised Neural Networks in Machine Vision.
N. A. Thacker.
The Bhattacharyya Measure requires no Bias Correction.
N.A.Thacker.
A Feature Representation for Map Building and Path Planning.
N.A.Thacker and A. J. Harris.
Error Modelling of Stereo Vision Data.
A.Harris, N.A.Thacker.
B-Fitting: An Estimation Technique With Automatic Parameter Selection.
N.A.Thacker, D.Prendergast, and P.I.Rockett.
Renormalised Sinc Interpolation.
N.A.Thacker, A.Jackson, D.Moriarty and B.Vokurka.
Solving Shape Based Object Recognition from a Computational Standpoint - Practical and
Physiological Constraints.
N.A.Thacker.
Tutorial: Statistics and Estimation in Algorithmic Vision.
N.A.Thacker.
A Fast Model Independant Method for Automatic Correction of Intensity Non-Uniformity
E. Vokurka, N. Thacker and A. Jackson.
Tutorial: Structural MRI Analysis Using Volumetric Voxel Analysis.
N.A.Thacker and M. Pokric.
Mathematical Segmentation of Grey Matter, White Matter and Cerebral Spinal Fluid from MR image Pairs.
N.A.Thacker, A.Jackson, X.P.Zhu and K.L.Li.
Quantification of the severity and distribution of cerebral atrophy provides diagnostic information
in dementing diseases.
N.A.Thacker, A.R.Varma, D.Bathgate, J.S.Snowden,D.Neary, A.Jackson.
An Evaluation of the Performance of RANSAC Algorithms for Stereo Camera Calibration
A. J. Lacey, N. Pinitkarn and N. A. Thacker
Strategies for Identification and Mark-up of Common Brain Structures Visible on Transverse Sections.
Marietta Scott and N.A.Thacker.
Modal Division and its Application to Medical Image Analysis.
N.A.Thacker and A.J.Reader.
Tutoral: Functional MRI Analysis.
N.A.Thacker.
Locating Motion Artifacts in Parametric fMRI Analysis
A.J.Lacey, N.A.Thacker, E. Burton, and A.Jackson
A New Approach for the Estimation of MTT in Bolus Passage Perfusion Techniques.
N.A.Thacker.
Model Selection and Convergence of the EM Algorithm.
N.A. Thacker, M. Pokric and A.J.Lacey.
What is Intelligence?: Generalized Serial Problem Solving.
N. A. Thacker, A.J.Lacey and P.Courtney.
Performance Characterisation in Computer Vision: The Role of Statistics in Testing and Design.
P. Courtney and N.A.Thacker.
Derivation of the Renormalisation Formula for the Product of Uniform Probability Distributions
and Extension to Non-Integer Dimensionality.
P. A. Bromiley, T.F. Cootes and N.A. Thacker
Multi-dimensional Medical Image Segmentation with Partial Voluming
M. Pokric, N.A. Thacker, M.L.J. Scott and A. Jackson
The Effects of a Square Root Transform on a Poisson Distributed Quantity.
N.A. Thacker and P.A. Bromiley
The Evolution of the TINA Stereo Vision Sub-System
A. J. Lacey, N. A. Thacker, P. Courtney and S. Crossley
TINA 2001: The Closed Loop 3D Model Matcher
A. J. Lacey, N. A. Thacker, P. Courtney and S. B. Pollard
Computing Covariances for Mutual Information Co-registration.
N.A. Thacker, P.A. Bromiley and M. Pokric
Bayesian and Non-Bayesian Probabilistic Models for Image Analysis
P.A. Bromiley, N.A. Thacker, M.L.J. Scott, M. Pokric, A.J. Lacey and T.F. Cootes
Colour Image Segmentation by Non-Parametric Density Estimation in Colour Space
P. A. Bromiley, N.A.Thacker and P. Courtney
Absolute reproducible quantification of Net Cerebral Blood Flow using Dynamic Susceptibility
Contrast Enhanced MRI, and its application in disease
Marietta Scott, Neil Thacker, Paul Bromiley, Alan Jackson
Validating MRI Field Homogeneity Correction Using Image Information Measures.
N.A.Thacker , A. J. Lacey and P. A. Bromiley.
A Novel Method for Non-Parametric Image Subtraction: Identification of Enhancing Lesions in
Multiple Sclerosis from MR Images.
P.A. Bromiley, N.A. Thacker and A. Jackson
An Empirical Design Methodology for the Construction of Machine Vision Systems.
N.A.Thacker, A.J.Lacey, P.Courtney and G. S. Rees
Partial Volume Tissue Segmentation using Grey-Level Gradient.
D. C. Williamson, N. A. Thacker, S. R. Williams and M. Pokric.
The Effects of an Arcsin Square Root Transform on a Binomial Distributed Quantity.
P.A. Bromiley and N.A. Thacker
Diagnosis of Dementing Diseases through the Distribution of Cerebral Atrophy: Development of a
Multi-Objective Evolutionary Algorithm Optimiser
P. A. Bromiley and N.A. Thacker
Using Quantitative Statistics for the Construction of Machine Vision Systems.
N.A.Thacker
What can Regional Cerebral Blood Flow Measures tell us about the Separation of Normal and
Disease Groups?
M.L.J. Scott, N.A. Thacker, A.J. Lacey
Computing Covariances for Mutual Information Coregistration 2
P.A. Bromiley and N.A. Thacker
Products and Convolutions of Gaussian Distributions
P.A. Bromiley
An Electrical Equivalence Model for CSF Pulsitility.
N.A.Thacker, P.A.Bromiley and J.Kim
Voxel Based Analysis of Tissue Volume MRI Data
N.A.Thacker, D.C. Williamson, M. Pokric
Characterisation of a Stereo Matching and Object Location System
G. A. Buonaccorsi, A. J. Lacey, N. A. Thacker
Noise Filtering and Testing for MR Using a Multi-Dimensional Partial Volume Model
N.A.Thacker, M. Pokric
Assessment of the Pad\'e Approximant as a Method for Quantifying $^1$H Magnetic Resonance
Spectroscopic Data
D.C.Williamson and N.A.Thacker
Improving Accuracy, Robustness and Computational Efficiency in 3D Computer Vision
S.Crossley, N.L.Seed, N.A.Thacker and P.A.Ivey
Step Interpolation of MR Images with Inter-Slice Gap Correction
S. McKie and N.A. Thacker
Tutorial: A Critical Analysis of Voxel Based Morphometry (VBM).
N.A.Thacker
Empirical Evaluation of Covariance Estimates for Mutual Information Coregistration
P.A. Bromiley, M. Pokric and N.A. Thacker
Trends in Brain Volume Change with Normal Ageing
P.A. Bromiley, N.A. Thacker and A. Jackson
Measuring Cerebral Blood Flow Using Dynamic Susceptibility Contrast Enhanced MRI
A.Jackson PhD MB ChB MRCP FRCR, N.A.Thacker PhD, M.L.J. Scott MSc
Shannon Entropy, Renyi Entropy, and Information
P.A. Bromiley, N.A. Thacker and E. Bouhova-Thacker
The Equal Variance Domain: Issues Surrounding the Use of Probability Densities in Algorithm Design
N.A.Thacker and P.A.Bromiley
Parameter Estimation for EM Mixture Modelling and its Relationship to Likelihood and EML.
N.A.Thacker.
Cerebral Cortical Thickness Measurements
M.L.J. Scott and N.A. Thacker
Effects of Intramolecular Dipolar Coupling on the Isotropic-Nematic Phase Transition of a Hard
Spereocylinders Fluid.
D.C.Williamson, N. A. Thacker and S.R.Williams.
Multi-dimensional Medical Image Segmentation with Partial Voluming and Gradient Estimation.
N.A.Thacker, M. Pokric and D. C. Williamson.
Critical Values for the Test of Flatness of a Histogram Using the Bhattacharyya Measure.
M.L.J.Scott.
Empirical Validation of Cerebrospinal Fluid Pulsatility Model
J. Kim, N.A. Thacker, P.A. Bromiley and A. Jackson.
Tutorial: Computing 2D and 3D Optical Flow.
J.L.Barron and N.A.Thacker.
Incorportating Optical Flow into Tinatool.
J.L.Barron.
TINA 5.0 Programmer's Guide
Neil Thacker
Tina 5.0 User's Guide
Edited by Neil Thacker
How to set up a local TINA code browser
P.A. Bromiley
TINA 5.0 Programmer's Reference
Edited by Neil Thacker
MSc Machine Vision Course : Practicals
N.A.Thacker and P.A. Bromiley
Curve Fitting and Image Potentials: A Unification within the Likelihood Framework.
N.A. Thacker.
Robust Tissue Boundary Detection for Cerebral Cortical Thickness Estimation.
M.L.J. Scott and N.A.Thacker.
The Equal Variance Domain 2: Beyond Likelihood.
N.A. Thacker.
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