Research Activities at
Florida State Vision Group
Florida State University
Xiuwen Liu
Department of Computer Science
Florida State University
http://fsvision.fsu.edu
http://www.cs.fsu.edu/~liux/courses/intro-seminar-08.ppt
Research Statement
 My
research goal is to create machines that can
“see” with similar and super human
performance and their applications
• This seems a trivial problem as each of us can do this
without any effort
• Computer + Camera = “A See Machine” ?
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Visual Pathway
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Visual Illusion
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Outline
 Motivations
• Some applications of computer vision and pattern
recognition techniques
 Some
of my research projects
 Related
courses
 Contact
information
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Computer Vision Applications
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DARPA Urban Challenge

U.S. Congressional set a goal that by 2015 one-third of the U.S.
Armed Forces’ operational ground combat vehicles be unmanned
•
Section 220 of the Floyd D. Spence National Defense Authorization Act for Fiscal Year 2001,
Public Law 106-398
 The
Urban Challenge
• http://www.darpa.mil/grandchallenge/index.asp
• Teams competed to build an autonomous vehicle able to
complete a 60-mile urban course safely in less than 6
hours
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7
DARPA Urban Challenge
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8
3D Urban Models
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9
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10
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11
Computational Photography
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12
Face Detection for Auto Focusing

Some cameras now have the built-in algorithms to automatically
detect and focus on the faces as they are the most important subject
in everyday photographing
Source: http://facedetection.fujifilmusa.com/
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13
Human-Computer Interactions
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Sign Language Recognition
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CyberKnife
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CyberKnife – Cont.
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Image-Guided Neurosurgery
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Intelligent Transportation Systems
http://dfwtraffic.dot.state.tx.us/dal-cam-nf.asp
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Computer Vision Applications – cont.
 Military
applications
• Automated target recognition
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Biometrics
Iris code can achieve zero
false acceptance
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10/13/2008 10:16:38 PM
Computer Vision in Sports
 How
was the yellow created?
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Generic Image Modeling

How can we characterize all these images perceptually?
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Spectral Histogram Representation
 Spectral
histogram
• Given a bank of filters F(a), a = 1, …, K, a spectral
histogram is defined as the marginal distribution of filter
responses
I(a ) (v)  F (a ) * I(v)
H
(a )
I
1
(a )
( z) 
δ
(
z

I
(v))

|I| v
H I  ( H I(1) , H I( 2) ,, H I( K ) )
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Spectral Histogram Representation - continued
 Choice
•
•
•
•
of filters
Laplacian of Gaussian filters
Gabor filters
Gradient filters
Intensity filter
LoG filter
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Gabor filter
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Spectral Histogram Representation - continued
10/13/2008 10:16:41 PM
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Texture Synthesis Examples - continued
Observed image
 An
Synthesized image
image with periodic structures
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Object Synthesis Examples - continued
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Performance Comparison
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Face Detection Based On Spectral Representations
 Face
detection is to detect all instances of faces in a
given image
 Each image window is represented by its spectral
histogram
• A support vector machine is trained on training faces
• Then the trained support vector machine is used to classify
each image window in an input image

More results at http://fsvision.fsu.edu/face-detection
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Face detection - continued
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Face detection - continued
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Face detection - continued
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Rotation Invariant Face Detection
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Rotation Invariant Face Detection - continued
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Linear Representations

Linear representations are widely used in appearance-based
object recognition and other applications
• Simple to implement and analyze
• Efficient to compute
• Effective for many applications
a ( I ,U )  U I  R
T
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d
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Standard Linear Representations
 Principal
Component Analysis
• Designed to minimize the reconstruction error on the training set
• Obtained by calculating eigenvectors of the co-variance matrix
 Fisher Discriminant Analysis
• Designed to maximize the separation between means of each class
• Obtained by solving a generalized eigen problem
 Independent
Component Analysis
• Designed to maximize the statistical independence among coefficients
along different directions
• Obtained by solving an optimization problem with some object function
such as mutual information, negentropy, ....
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Standard Linear Representations - continued
 Standard
linear representations are sub optimal
for recognition applications
• Evidence in the literature
• A toy example
– Standard representations give the worst recognition performance
 Optimal
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component analysis
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Performance Measure - continued

Suppose there are C classes to be recognized
• Each class has ktrain training images
• It has kcross cross validation images
• We used h(x) = 1/(1+exp(-2bx)
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Performance Measure - continued
 F(U)
depends on the span of U but is invariant to
change of basis
• In other words, F(U)=F(UO) for any orthonormal matrix O
• The search space of F(U) is the set of all the subspaces,
which is known as the Grassmann manifold
– It is not a flat vector space and gradient flow must take the
underlying geometry of the manifold into account
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Deterministic Gradient Flow - continued

Gradient at [J] (first d columns of n x n identity matrix)
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Deterministic Gradient Flow - continued
 Gradient

at U: Compute Q such that QU=J
Deterministic gradient flow on Grassmann manifold
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Stochastic Gradient and Updating Rules

Stochastic gradient is obtained by adding a stochastic
component

Discrete updating rules
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MCMC Simulated Annealing Optimization Algorithm

Let X(0) be any initial condition and t=0
1.
2.
3.
4.
5.
6.
7.
Calculate the gradient matrix A(Xt)
Generate d(n-d) independent realizations of wij’s
Compute Y (Xt+1) according to the updating rules
Compute F(Y) and F(Xt) and set dF=F(Y)- F(Xt)
Set Xt+1 = Y with probability min{exp(dF/Dt),1}
Set Dt+1 = Dt / g and set t=t+1
Go to step 1
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ORL Face Dataset
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Performance Comparison
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Performance Comparison – cont.
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Brain Curve Classification
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Brain Curve Classification – cont.
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Real-time Scene Interpretation
 Object
detection and recognition problem
• Given a set of images, find regions in these images which
contain instances of relevant objects
• Here the number of relevant objects is assumed to be large
– For example, the system should be able to handle 30,000 different
kinds of objects, an estimate of the human brain’s capacity for basic
level visual categorization [I. Biederman, Psychological Review, vol. 94, pp. 115-147,
1987]
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Global Monitoring Through High-resolution Satellite Images
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Problem Statement for Scene Interpretation
 Object
detection and recognition problem
• Given a set of images, find regions in these images which
contain instances of relevant objects
• Here the number of relevant objects is assumed to be large
– For example, the system should be able to handle 30,000 different
kinds of objects, an estimate of the human’s capacity for basic level
visual categorization [I. Biederman, Psychological Review, vol. 94, pp. 115-147, 1987]
 Goal
• Develop a system that can achieve real-time detection and
recognition for images of size 640 x 480 with high accuracy
– Say, at a frame rate of 15 frames per second
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Existing Approaches
 Fast
methods but low
accuracy
• One can for example classify
one pixel at a time
• However, it is to identify
airplanes with high accuracy
due to high false positives
and negatives
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Existing Approaches – cont.
 Fast
methods but low
accuracy
• One can for example classify
one pixel at a time
• However, it is to identify
airplanes with high accuracy
 Methods
with good
accuracy but slow
• One can in theory use
deformable template
matching to locate instances
of airplanes
• It may need several hours to
process one image
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Proposed Framework
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Specifications and Requirements
 We
want to detect and recognize at least 30,000
object classes in images
• At four different scales
• Using exhaustive search of local windows, that is, we do not
assume segmentation or other pre-processing
• If we assume objects are in some (e.g. 21 x 21) windows, this
means that there will be many (18,432,000) local windows to
be classified/processed
• We want to do this on a 3.6 Ghz Dell Precision workstation
with an estimated performance of 28,665.4 MIPS
• This amounts to that we have about 1555 instructions to
process a 21 x 21 local window
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Requirements – cont.
 To
achieve the specifications, we need two critical
components
• A classifier that can reduce the average classification time
effectively
– Note that on average we have 1555 instructions; if we can process
90% of those windows using only 100 instructions per window, we
can have on average 14,650 instructions for the remaining 10% local
windows
• Features that can discriminate a large number of objects and
can be computed using a few instructions
– Do such features exist?
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Topological Local Spectral Histograms
 We
introduce a new class of features, which we
called TLSH features
• It is defined relative to a chosen set of filters
• For a given filter, it is defined as a histogram of a local
window of the filtered image
• One bin of the histogram is given by
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Topological Local Spectral Histogram Example
Convolution is implemented
using FPGAs
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Local Spectral Histogram Features
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Field Programmable Gate Arrays
•
Two primary methods for computation
• Hard Wired Application Specific Integrated Circuit (ASIC)
• Software-programmed microprocessors
•
New Approach
• Programmable hardware
• Field Programmable Gate Arrays (FPGAs) represent a
breakthrough in computing technology
– Especially for intrinsically parallel applications
10/12/2008 9:51:41 PM
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μP/ ASIC / FPGA Comparison Summary
μP
ASIC
FPGA
Programmable (flexible)
Fixed Design Functionality (inflexible)
Programmable (flexible)
Relatively Slow Serial Computation
Very Fast, highly parallelized
computation
Fast, Parallel Computation
Floating and Fixed Point
Fixed Point / Floating
Fixed Point / Floating
Relatively Inexpensive Design Cycle
(Software)
Expensive Design Cycle (requires chip
design)
Relatively Inexpensive Design Cycle
Limited Bandwidth
Very High Bandwidth
Near ASIC Bandwidth
Standard High Level Languages
C/C++ or Assembly
Hardware Description Language for
Design / Simulation
VHDL / Verilog
Hardware Description Language for
Design / Simulation
VHDL / Verilog
10/12/2008 9:51:44 PM
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Hardware vs. Software
L 1
• Software Implementation: y (n )   xk (n )  hk
k 0
Sum = 0.0
I = 0;
While (I < L)
tmp = x(i) * h(i)
Sum = Sum + tmp
I = I+1
end
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A typical software
implementation takes 4*L
instructions to compute one
convolution
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Hardware vs. Software
 A custom
hardware implementation
Multiply/Accumulate
performed in parallel
Can be done in one
clock cycle
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Convolution Timing Diagram
Convolution
Start Signal
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All nine
response
values
finished
Clock
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Every 7 Clock
Cycles: 9 new
response
values
Topological Local Spectral Histograms – cont.
 Why TLSH
features?
• It provides a very rich set of over-complete features
– For example, suppose we have 22 filters, there will be 1,173,942
different TLSH features within a 21 x 21 region, considering different
windows and different filters
– TLSH features are more effective than Haar features used by Viola and
Jones [P. Viola and M. Jones, International Journal of Computer Vision, vol. 57, pp.
137-154, 2004]
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ORL Face Dataset
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Comparison Between Haar and TLSH Features
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COIL Dataset
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Comparison Between Haar and TLSH Features
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Texture Dataset
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Comparison Between Haar and TLSH Features
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Mixed Dataset
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Comparison Between Haar and TLSH Features
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Comparison Between Haar and TLSH Features
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Classifier
 To
achieve the specification, we also need a
classifier that takes only a few instructions to make
a decision on average
• At the same time, we need to achieve high accuracy
 We
propose to use a look-up table tree classifier
• I.e., a decision tree classifier where each node is
implemented by a look-up table
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Look-up Table Tree Classifier
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Look-up Table Tree Classifier
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An Example Path in a Decision Tree
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Constructing Look-up Table Decision Tree
 Joint
optimization of clustering, TLSH features,
and optimal linear projections
• We want to maximize the separations between marginal
distributions of different clusters
• We can do the optimization iteratively
– We can do clustering first using current TLSH features and
projections to maximize the separations
– We can find optimal TLSH features given linear projections
– Then we can find optimal linear projections given updated TLSH
features
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Performance Comparison
RCT – Rapid Classification Tree, implemented by Keith Haynes
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Detection and Recognition
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Detection and Recognition
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Content-based Video Representation, Indexing and Retrieval

A video is an extrinsic 3D representation of a 4D volume
• 3D spatial space + 1D temporal space = 4D volume
• For video, 2D image space + 1D temporal space = 3D volume

Our group is working an intrinsic 4D representation for
video
• By first reconstructing the scene using SLAM (Simultaneous
localization and mapping) and stereopsis
10/12/2008 9:52:18 PM
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Computer Vision on Mobile Devices
Source: http://www.appliedmediaanalysis.com/MATES.htm
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Computer Vision for Gerotechnology

As mobile devices become more powerful, they may serve
as an efficient interface to make up visual, memory, and
other deficiencies due to aging
• The society is aging
– For example, people of 65 and older are 16.8% of Florida’s population
(US Census Bureau, 2005)
• By modifying and enhancing environments, vision technology can be
critical for helping people stay active and be independent
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Motivations

The goal is to organize large number of images so that they can
be retrieved efficiently and effectively based on content
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Problems And Approach
 Organization
of large libraries of images for efficient
content-based indexing and retrieval of images
 Image
Categorization - We assume that a training
database containing labeled images representing
various different classes is available
• The goal is to learn optimal low-dimensional features that can
be used to assign a new query image to the correct class
Retrieval - The objective is to find the top ℓ
matches in a database to a query image
 Image
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Preliminary Experiments With SH-features

Dataset: COREL-1000 (100 images in each of 10 categories)
 We
utilize a bank of 5 filters and apply each
filter to the R, G, and B channels of the images to
obtain a total of fifteen 11-bin histograms per
image
• Thus, the SH-feature vector h(I, F) has dimension 165
 For a
query image I, we calculate the Euclidean
distances between h(I, F) and h(J, F), for every J
in the database, and rank the images according
to increasing distances
• To quantify retrieval performance and compare the results
with those reported for other methods, we use the
weighted precision and the average rank
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The Weighted Precision
 For a
query image I, the retrieval precision for
the top ℓ returns is nℓ(I)/ℓ, where nℓ is the number
of correct returns
 The
weighted precision for COREL-1000 for I is
defined as
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The Average Rank
 For each
query image, we rank all the images in
the dataset
• The average rank is the average rank of all images that
belong to the same class as the query image
• For COREL-1000, the perfect value for a query images is
50.5
10/12/2008 9:52:56 PM
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Average Precision: Comparison
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Average Rank: Comparison
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Content-based Image Retrieval
 We
now apply the image-categorization classifier
learned with OFA to retrieve images
• Note that the low-dimensional image representation was
optimized to categorize images correctly according to the
nearest neighbor criterion, but not to rank matches to a
query image correctly according to their distances in
feature space
 The
goal is to exploit the image categorization
method to retrieve images
• Idea is to use the distance in feature space to assign
probabilities that measure the compatibility of the image
with a given class. Use these probabilities to rank classes
and retrieve images using this ranking
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Image Retrieval - continued
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Image Retrieval - continued
a query image I and a positive integer ℓ, the
goal is to retrieve a ranked list of ℓ images from the
database.
 Given
• We assume that all images in the database have been indexed
according to content using the classifier learned with OFA.
• Rank the classes according to the probabilities p(i|I).
• Select as many images as possible from the most likely class.
• Within this class, images are retrieved and ranked according to
their Euclidean distances to I as measured in the reduced
feature space (several variants possible).
• Once that class is exhausted, we proceed similarly with the
second most likely class and iterate the procedure until ℓ
images are obtained.
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Experimental Results
 OFA was
used to “learn” a 9-dimensional linear
reduction of the SH-features of dimension 165,
with 400 training images, 40 from each class
• Leave-one-out was used for cross-validation
 The
entire database was indexed with the nearest
neighbor classifier applied to the reduced features
 All 1,000 images were used as query images
• For each class i, the average weighted precision and the
average rank were calculated for comparison with
SIMPLIcity and Spectral Histograms
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Average Precision: Comparison
10/12/2008 9:53:00 PM
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Average Rank: Comparison
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Examples
The top-left image is the query image, which is also the top return
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More Examples
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Precision–Recall
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Average Recall vs. Average Precision
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Average Recall vs. Average Precision
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Invariant Content Characterization Based on Single View 3D Reconstruction
 Many
of the images share the following
characteristics
• There is a ground plane, which is relative flat
• On the ground plane, we have objects that can be
approximated by planar sides
– Note the images will change depending on the view angles
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Single View 3D Reconstruction
 We
want to characterize the meaningful contents
of images
• Ground itself in general is not interesting
• We want to “capture” the objects on the ground
10/12/2008 9:53:17 PM
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Ground Plane Estimation Using Horizon Line
 For a
pinhole camera model, each plane at the
infinity will become a line in the image
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Pinhole Camera Model
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Plane at Infinity
AX  BY  CZ  D  0
Axcam Z By cam Z

 CZ  D  0
f
f
Axcam By cam
D

C   0
f
f
Z
Axcam By cam

 C  0 if Z  
f
f
That is, we can estimate the ground plane’s normal if we
can estimate the horizon in the image
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Vertical Planes

After we estimate the ground plane, we can then estimate
in 3D any vertical plane if we know (at least) two pixels
on the intersection line between the vertical plane and
ground plane
• We now approximate the scene using boxes in 3D
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Invariant Representations of Content
 For content-based
retrieval, we derive a
representation of an image that is scale and view
independent
• By representing each side of each 3D box using a
normalized view
– In other words, for each side of the box, we place a virtual camera at
a fixed distance (which fixes the scale) and a fixed view, whose up
vector is perpendicular to the ground plane (which fixes the view)
10/12/2008 9:53:50 PM
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An Example
Shape Theory

We want to quantify the difference between two shapes in
a principled way
• We do this by constructing a shape space and then use the geodesic
distance of two shapes on the shape manifold as the metric
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Shape Clustering
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Shape Clustering
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Clustering Dendrogram
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Sulcal Curves

Sulcal curves are important for characterizing brain
functions
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Sulcal Curves

Sulcal curves are important for characterizing brain
functions
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Clustering of Sulcal Curves
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Modeling Mathematical Abilities and Disabilities

As it is possible to acquire detailed surfaces of the human
brain, one may ask how characteristics of the brain
structure affect the mathematical abilities and disabilities
• The U.S. Department of Education wants to know so that they can understand and
find solutions to the mathematical problems young children have
Corpus callosum examples of young children without mathematical disabilities (a) and with (b)
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Surface Parametrization
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Geodesic Interpolation Between Surfaces
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Atlas for Hippocampus
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Model for Blindness
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Automated Alignment of Large Surfaces
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Robust Visual Inference

With a common domain for surface representations, we can
pose the visual inference in the Bayesian framework by
building probability models
10/12/2008 9:54:31 PM
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Human-Robot Collaborative Interaction

The goal is to let robots be aware of the positions, poses,
expressions, moods, and other factors of the humans so that
robots can interact with humans collaborative
In collaboration
with Prof. Emmanuel Collins
at the College Engineering
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Automated 3D Phenotype Measurement

The central problem in biology is to understand the
relationship between genotype and phenotype
• With availability of genomes of humans and model organisms, the central
problem becomes how to measure phenotype at a large scale
10/12/2008 9:54:39 PM
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Computer Vision for Computational Systems Biology

The goal of systems biology is to link the molecular and cellular
events and properties to physiological functions
10/12/2008
9:54:42
PM proteins to organs: The Physiomeintro-seminar-08.ppt
Source:
“Integration
from
Project”, Nature Review, Vol. 4, 2007.
Live Cell Imaging at Cellular Level
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MRI / fMRI / CT / PET Imaging
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FISHFinder@FSU
Source: Gilbert’s group at Biology Department, FSU
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134
Courses
 Most
Relevant Courses
•
•
•
•
•
•
CAP 5638 Pattern Recognition – Offered Spring 2008
CAP 5415 Principles and Algorithms of Computer Vision
CAP 6417 Theoretical Foundations of Computer Vision
STA 5106 Computational Methods in Statistics I
STA 5107 Computational Methods in Statistics I I
ISC 5935-05/STA 5934-01 Applied Machine Learning –
Offered Spring 2008
• Seminars and advanced studies
 Related
Courses
• CAP 5615 Artificial Neural Networks
• CAP 5600 Artificial Intelligence
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Funding of the Group
 National
•
•
•
•
Science Foundation
DMS
CISE IIS
ACT
CCF
 National
10/12/2008 10:01:09 PM
Institute of Health
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Summary
 Computer Vision
Group offers interesting
research topics/projects
• Efficient represents for generic images and videos
• Real-time detection and recognition of objects
• Computational models for object recognition and image
classification
• Medical/biological image analysis
• Motion/video sequence analysis and modeling
• They are challenging, interesting, and exciting
• Now it is a productive and fruitful area to be in
10/12/2008 10:01:16 PM
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Contact Information
•
•
•
•
•
Name
Web sites
Email
Offices
Phones
10/12/2008 10:01:23 PM
Xiuwen Liu
http://cavis.fsu.edu
http://fsvision.fsu.edu
http://www.cs.fsu.edu/~liux
liux@cs.fsu.edu
LOV 166 and 118 North Woodward Ave.
644-0050 and 645-2257
intro-seminar-08.ppt
Thank you!
Any questions?