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by Hong Jeong
Architectures for Computer Vision: From Algorithm to Chip with Verilog
About the Author
Preface
Part One: Verilog HDL
Chapter 1: Introduction
1.1 Computer Architectures for Vision
1.2 Algorithms for Computer Vision
1.3 Computing Devices for Vision
1.4 Design Flow for Vision Architectures
Problems
References
Chapter 2: Verilog HDL, Communication, and Control
2.1 The Verilog System
2.2 Hello, World!
2.3 Modules and Ports
2.4 UUT and TB
2.5 Data Types and Operations
2.6 Assignments
2.7 Structural-Behavioral Design Elements
2.8 Tasks and Functions
2.9 Syntax Summary
2.10 Simulation-Synthesis
2.11 Verilog System Tasks and Functions
2.12 Converting Vision Algorithms into Verilog HDL Codes
2.13 Design Method for Vision Architecture
2.14 Communication by Name Reference
2.15 Synchronous Port Communication
2.16 Asynchronous Port Communication
2.17 Packing and Unpacking
2.18 Module Control
2.19 Procedural Block Control
Problems
References
Chapter 3: Processor, Memory, and Array
3.1 Image Processing System
3.2 Taxonomy of Algorithms and Architectures
3.3 Neighborhood Processor
3.4 BP Processor
3.5 DP Processor
3.6 Forward and Backward Processors
3.7 Frame Buffer and Image Memory
3.8 Multidimensional Array
3.9 Queue
3.10 Stack
3.11 Linear Systolic Array
Problems
References
Chapter 4: Verilog Vision Simulator
4.1 Vision Simulator
4.2 Image Format Conversion
4.3 Line-based Vision Simulator Principle
4.4 LVSIM Top Module
4.5 LVSIM IO System
4.6 LVSIM RAM and Processor
4.7 Frame-based Vision Simulator Principle
4.8 FVSIM Top Module
4.9 FVSIM IO System
4.10 FVSIM RAM and Processor
4.11 OpenCV Interface
Problems
References
Part Two: Vision Principles
Chapter 5: Energy Function
5.1 Discrete Labeling Problem
5.2 MRF Model
5.3 Energy Function
5.4 Energy Function Models
5.5 Free Energy
5.6 Inference Schemes
5.7 Learning Methods
5.8 Structure of the Energy Function
5.9 Basic Energy Functions
Problems
References
Chapter 6: Stereo Vision
6.1 Camera Systems
6.2 Camera Matrices
6.3 Camera Calibration
6.4 Correspondence Geometry
6.5 Camera Geometry
6.6 Scene Geometry
6.7 Rectification
6.8 Appearance Models
6.9 Fundamental Constraints
6.10 Segment Constraints
6.11 Constraints in Discrete Space
6.12 Constraints in Frequency Space
6.13 Basic Energy Functions
Problems
References
Chapter 7: Motion and Vision Modules
7.1 3D Motion
7.2 Direct Motion Estimation
7.3 Structure from Optical Flow
7.4 Factorization Method
7.5 Constraints on the Data Term
7.6 Continuity Equation
7.7 The Prior Term
7.8 Energy Minimization
7.9 Binocular Motion
7.10 Segmentation Prior
7.11 Blur Diameter
7.12 Blur Diameter and Disparity
7.13 Surface Normal and Disparity
7.14 Surface Normal and Blur Diameter
7.15 Links between Vision Modules
Problems
References
Part Three: Vision Architectures
Chapter 8: Relaxation for Energy Minimization
8.1 Euler–Lagrange Equation of the Energy Function
8.2 Discrete Diffusion and Biharminic Operators
8.3 SOR Equation
8.4 Relaxation Equation
8.5 Relaxation Graph
8.6 Relaxation Machine
8.7 Affine Graph
8.8 Fast Relaxation Machine
8.9 State Memory of Fast Relaxation Machine
8.10 Comparison of Relaxation Machines
Problems
References
Chapter 9: Dynamic Programming for Energy Minimization
9.1 DP for Energy Minimization
9.2 N-best Parallel DP
9.3 N-best Serial DP
9.4 Extended DP
9.5 Hidden Markov Model
9.6 Inside-Outside Algorithm
Problems
References
Chapter 10: Belief Propagation and Graph Cuts for Energy Minimization
10.1 Belief in MRF Factor System
10.2 Belief in Pairwise MRF System
10.3 BP in Discrete Space
10.4 BP in Vector Space
10.5 Flow Network for Energy Function
10.6 Swap Move Algorithm
10.7 Expansion Move Algorithm
Problems
References
Part Four: Verilog Design
Chapter 11: Relaxation for Stereo Matching
11.1 Euler–Lagrange Equation
11.2 Discretization and Iteration
11.3 Relaxation Algorithm for Stereo Matching
11.4 Relaxation Machine
11.5 Overall System
11.6 IO Circuit
11.7 Updation Circuit
11.8 Circuit for the Data Term
11.9 Circuit for the Differential
11.10 Circuit for the Neighborhood
11.11 Functions for Saturation Arithmetic
11.12 Functions for Minimum Argument
11.13 Simulation
Problems
References
Chapter 12: Dynamic Programming for Stereo Matching
12.1 Search Space
12.2 Line Processing
12.3 Computational Space
12.4 Energy Equations
12.5 DP Algorithm
12.6 Architecture
12.7 Overall Scheme
12.8 FIFO Buffer
12.9 Reading and Writing
12.10 Initialization
12.11 Forward Pass
12.12 Backward Pass
12.13 Combinational Circuits
12.14 Simulation
Problems
References
Chapter 13: Systolic Array for Stereo Matching
13.1 Search Space
13.2 Systolic Transformation
13.3 Fundamental Systolic Arrays
13.4 Search Spaces of the Fundamental Systolic Arrays
13.5 Systolic Algorithm
13.6 Common Platform of the Circuits
13.7 Forward Backward and Right Left Algorithm
13.8 FBR and FBL Overall Scheme
13.9 FBR and FBL FIFO Buffer
13.10 FBR and FBL Reading and Writing
13.11 FBR and FBL Preprocessing
13.12 FBR and FBL Initialization
13.13 FBR and FBL Forward Pass
13.14 FBR and FBL Backward Pass
13.15 FBR and FBL Simulation
13.16 Backward Backward and Right Left Algorithm
13.17 BBR and BBL Overall Scheme
13.18 BBR and BBL Initialization
13.19 BBR and BBL Forward Pass
13.20 BBR and BBL Backward Pass
13.21 BBR and BBL Simulation
Problems
References
Chapter 14: Belief Propagation for Stereo Matching
14.1 Message Representation
14.2 Window Processing
14.3 BP Machine
14.4 Overall System
14.5 IO Circuit
14.6 Sampling Circuit
14.7 Circuit for the Data Term
14.8 Circuit for the Input Belief Message Matrix
14.9 Circuit for the Output Belief Message Matrix
14.10 Circuit for the Updation of Message Matrix
14.11 Circuit for the Disparity
14.12 Saturation Arithmetic
14.13 Smoothness
14.14 Minimum Argument
14.15 Simulation
Problems
References
Index
End User License Agreement
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Index
A* algorithm
Affine graph
Altera Quartus
Anisotropic diffusion
AOR
Appearance model
Approximate DP
ASIC
ASM
Asynchronous
Backward probability
Baum–Welch algorithm
Bayesian inference
Bayesian network
Bayesian tree
BB
BC
Behavioral model
Belief
Bellman equation
Bellman's principle
Bethe approximation
Biharmonic
Biltzmann's law
Binding problem
Blocking
Blocks world
Blur diameter
Blurring
BMP
BNB
Boltzmann machine
Border expansion
Border shrink
BP
Calculus of variation
Calibration matrix
Call by reference
Call by value
Camera calibration
Camera matrix
Canonical form
Center left reference
Center reference
Center right reference
Centroid
Cholesky deccomposition
Circle of confusion
Clique
Clique potential
CNF
Combinational circuit
Compressed sensing
Concurrent
Conjugate pair
Conservation
Constraint propagation
Continuity equation
Continuous assignment
Control unit
Correspondence problem
Corresponding point
CPLD
Curse of dimensionality
CYK
Data term
Datapath
Deep learning
Delay control
DfD
DfF
Diagonal method
Diffusion
Dimensionality reduction
Discretization
Disparity
Disparity map
Divide and conquer
DMMP
DMSV
DoG
Doubleton
Downward referencing
DP
DPI
DUT
EDA
Egomotion
EM
EMD
Energy function
Energy minimization
EP
Epiplane
Epipolar line
Epipole
Essential matrix
Euler–Lagrange equation
Event control
Expansion move
expansion move
Exponential time
Extended DP
Extrinsic parameter
Factor graph
Factorization method
FBP
FIFO
FIR
Flow network
Flynn's taxonomy
Flynn–Johnson taxonomy
FOC
Focal length
FOE
Forward probability
FOV
FPGA
Frame buffer
FRE
FRE machine
Free energy
FSM
Function
Fundamental equation
FVSIM
Gauss–Seidel method
Gaussian
GBC
GC
Generalized heat equation
Gibbs distribution
GMMP
GMSV
GP
GPU
GSJ method
Hammersley-Clifford theorem
Handshaking
Hard copy
HDL
Helmholtz free energy
Hermite polynomial
HLS
HMM
Homogeneous coordinates
Homography
Horn-Shunck method
Horn–Shunck method
IDE
Ideals
ILP
Inhomogeneous coordinates
Inside probability
Inside-Outside algorithm
Instantiation
Interleaving
Intrinsic parameter
IPs
Isotropic diffusion
Iteration
Jacobi method
KL divergence
Labeling
Laplacian
LBC
LBP
LE
Left disparity
Left reference
Line at infinity
Little-endian
LLSE
LoG
Longuet-Higgins, H. C.
Loopy MRF
LPR
Lukas-Kanade method
LVSIM
Manifold lerning
MAP
Marginal
Marginalization
Matching node
Max product
Max-flow Min-cut
max-flow min-cut
MCMC
Mealy machine
Message
Metric
MIMD
MIOP
Mirror expansion
MISD
Mixed model
ML
ModelSim
Module link
Moore machine
Motion flow
Move algorithm
MPLP
MRF
MRU
Multigrid method
Multiple view
Multisensory integration
N-best
n-link
Net type
Netlist
NN
Nonblocking
Normal flow
Normalization
Normalized camera
Normalized camera matrix
NP-hard
Occlusion node
Open end problem
OpenCL
OpenCV
Optical center
Optical flow
Orthographic projection
Outside probability
Packed array
Pair-wise MRF
Parallel DP
Parameter
Parse tree
Partition function
PCFG
PE
Perplexity
Perspective projection
Phasor
Pipelining
PLD
Point at infinity
Polytope
Port
Potts model
Principal axis
Principal plane
Principal point
Procedural assignment
PSF
RAM
RE machine
Rectification
Reg type
Regularizer
Reinforcement learning
Relaxation
Relaxation graph
Reparameterization
Retiming
RGB
Right disparity
Right reference
RTL
SA
SAT
Scene flow
SCFG
SDRAM
Semaphore
Semimetric
Sequential
Serial DP
SfC
SfM
SfS
SfT
Shortest path algorithm
SIMD
Singleton
SISD
Smoothness constraint
Smoothness term
Soft matting
SOR
Spectral warping function
SSE
Stack
Stereo matching
Stereographic projection
Structural model
Submodularity
Sum product
Sum-sum
Superscalar
Surface normal
SVD
Swap move
swap move
Synchronous
Synthesizable
System functions
System tasks
SystemVerilog
Systolic array
t-link
Task
TB
Tensor diffusion
Terminal symbol
Thin lens
Topological transformation
Transition probability
Tree-trellis
Trellis
Triangle inequality
Trinocular stereo
Tripleton
Unpacked array
Unsynthesizable
Upward referencing
UUT
Value set
Variable referencing
Variable type
Verilog HDL
Vertical method
VHDL
Virtual image plane
Vision integration
Vision simulator
Viterbi algorithm
Von Neumann architecture
VPI
VSIM
Xilinx ISE
Xilinx Vivado
Zero padding
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