Fast Multiscale Image Segmentation From Pixels to Semantics
- Slides: 65
Fast, Multiscale Image Segmentation: From Pixels to Semantics Ronen Basri The Weizmann Institute of Science Joint work with Achi Brandt, Meirav Galun, Eitan Sharon
Camouflage
Camouflage Malik et al. ’s “Normalized cuts”
Our Results
Segmentation by Weighted Aggregation A multiscale algorithm: • Optimizes a global measure • Returns a full hierarchy of segments • Linear complexity • Combines multiscale measurements: – Texture – Boundary integrity
The Pixel Graph Couplings (weights) reflect intensity similarity Low contrast – strong coupling High contrast – weak coupling
Normalized-cut Measure Minimize:
Saliency Measure Minimize:
Multiscale Computation of Ncuts • Our objective is to rapidly find the segments (0 -1 partitions) that optimize E. • For single-node cuts we simply evaluate E. • For multiple-node cuts we perform “soft contraction” using coarsening procedures from algebraic multigrid solvers of PDEs.
Coarsening the Graph • Suppose we can define a sparse mapping such that for all minimal states
Coarse Energy • Then • PTWP, PTLP define a new (smaller) graph
Recursive Coarsening
Recursive Coarsening Representative subset
Recursive Coarsening For a salient segment : , sparse interpolation matrix
Weighted Aggregation aggregate
Hierarchical Graph § Pyramid of graphs § Soft relations between levels § Segments emerge as salient nodes at some level of the pyramid
Importance of Soft Relations
Physical Motivation • Our algorithm is motivated by algebraic multigrid solutions to heat or electric networks • u - temperature/potential • a(x, y) – conductivity • At steady state largest temperature differences are along the cuts • AMG coarsening is independent of f
Determine the Boundaries 1, 0, 0, …, 0 P 0 0 1
Hierarchy in SWA
Texture Examples
Filters (From Malik and Perona) Oriented filters Centersurround
A Chicken and Egg Problem: Coarse measurements mix neighboring statistics Hey, I was here first Solution: Support of measurements is determined as the segmentation process proceeds
Texture Aggregation • Aggregates assumed to capture texture elements • Compare neighboring aggregates according to the following statistics: – Multiscale brightness measures – Multiscale shape measures – Filter responses • Use statistics to modify couplings
Recursive Computation of Measures • Given some measure of aggregates at a certain level (e. g. , orientation) • At every coarser level we take a weighted sum of this measure from previous level • The result can be used to compute the average, variance or histogram of the measure • Complexity is linear
Use Averages to Modify the Graph
Adaptive vs. Rigid Measurements Original Our algorithm - SWA Averaging Geometric
Adaptive vs. Rigid Measurements Original Our algorithm - SWA Interpolation Geometric
Adaptive vs. Rigid Measurements
Adaptive vs. Rigid Measurements
Adaptive vs. Rigid Measurements
Adaptive vs. Rigid Measurements
Adaptive vs. Rigid Measurements
Texture Aggregation Fine (homogeneous) Coarse (heterogeneous)
Multiscale Variance Vector
Multiscale Variance Vector
Variance: Avoid Mixing aggregation Sliding window
Leopard
More Leopards…
And More…
Birds
More Animals
Boat
Malik’s Ncuts
Key Differences • Optimize a global measure (like Malik’s Ncuts) • Hierarchy with soft relations (unlike agglomerative/graph contraction) • Combine texture measurements while avoiding the “chicken and egg problem”
Complexity • Every level contains about half the nodes of the previous level: Total #nodes 2 X #pixels • All connections are local, cleaning small weights • Top-down sharpening: constant number of levels • Linear complexity • Implementation: 5 seconds for 400 x 400
Representation • Average intensity • Texture • Shape
Matching (with Chen Brestel)
More…
Data: Filippi MRI Data 30 slices, 180 x 220 in 3 minutes
Data: Filippi MS Lesion Detection Tagged Our results
Data: Filippi Tagged Our results
Data: Filippi Tagged Our results
Data: Filippi Tagged Our results
Data: Filippi 2 D Segmentation
Data: Filippi 3 D Segmentation
Cell Movement
Summary image segments Shape properties Leopard
- Scratch
- What is scrath
- 800 pixels wide and 200 pixels tall pictures
- The scratch stage is 480 pixels wide and 360 pixels high
- The scratch stage is 480 pixels wide and 360 pixels high
- Adjacency in image processing examples
- The scratch stage is 480 pixels wide and 360 pixels high
- Multiscale combinatorial grouping
- Eurofer
- Biomodels database
- Key stages in digital image processing
- Compare procedural semantics and declarative semantics.
- For coordinates p(2,3) the 4 neighbors of pixel p are
- Example of acid-fast bacteria
- Acid fast and non acid fast bacteria
- Image thresholding matlab
- Segmentation in digital image processing
- Image processing
- Filteration
- Deep automatic portrait matting
- Segmentation is a process of
- Medical image segmentation tutorial
- Boykov and jolly 2001
- Regional park indore
- Oliver van kaick
- Image segmentation
- Relationships between pixels
- 16:9 in pixels
- Hot pixels
- Megavideo x company
- Cuda cda
- Neighbors of a pixel
- Pixels 2
- Purity index
- Wirzip
- Real vs virtual images
- Virtual vs real image
- Translate
- Optimum notch filter in image processing
- Image compression model in digital image processing
- Analog image and digital image
- Lossless image compression matlab source code
- Image sharpening in digital image processing
- Static digital image
- Geometric transformation in digital image processing
- Area of convergence
- Isopreference curve
- Appuyez sur l’image qui est dans le bon sens
- Difference between logical file and physical file
- Qual o resultado de: imagem image
- Image restoration is to improve the dash of the image
- Image transform in digital image processing
- Imtransform matlab
- Image restoration in digital image processing
- "image.so.com" -site:image.so.com
- Oren freifeld
- Aki karusmäki
- Semantic vector
- Sense and reference linguistics
- Syntax vs semantics
- Syntax vs semantics
- Lexical semantics
- Grammar vs syntax vs semantics
- Pragmatic definition in linguistics
- Constant and variable reference in semantics
- Semantic studies