(c) 2000, 2001 SNU CSE Biointelligence Lab1 6.4.4 Finding Region Another method for processing image to find “regions” Finding regions Finding outlines.

(c) 2000, 2001 SNU CSE Biointelligence Lab

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6.4.4 Finding Region6.4.4 Finding Region

Another method for processing image

to find “regions” Finding regions Finding outlines


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A A regionregion of the image of the image

A region is homogeneous. The difference in intensity values of pixels in the region is no

more than some A polynomial surface of degree k can be fitted to the intensity

values of pixels in the region with largest error less than For no two adjacent regions is it the case that the union

of all the pixels in these two regions satisfies the homogeneity property.

Each region corresponds to a world object or a meaningful part of one.


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Split-and-mergeSplit-and-merge method method

1. The algorithm begins with just one candidate region, the whole image.

2. Until no more splits need be made.

1. For all candidate regions that do not satisfy the homogeneity property, are each split into four equal-sized candidate regions.

3. Adjacent candidate regions are merged if their pixels satisfying homogeneity property.


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Regions Found by Split Merge for a Regions Found by Split Merge for a Grid-World Scene (from Fig.6.12)Grid-World Scene (from Fig.6.12)


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““Cleaned Up” Cleaned Up” the regions found by the regions found by Split-and-merge methodSplit-and-merge method Eliminating very small regions (some of which are

transitions between larger regions). Straightening bounding lines. Taking into account the known shapes of objects

likely to be in the scene.


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6.4.5 Using Image Attributes Other 6.4.5 Using Image Attributes Other Than IntensityThan Intensity Image attributes other than the homogeneity

Visual texture fine-grained variation of the surface reflectivity of

the objects Ex) a field of grass, a section of carpet, foliage in

tree, the fur of animals The reflectivity variations in objects cause similar

fine-grained structure in image intensity.


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Methods for analyzing textureMethods for analyzing texture Structural methods

Represent regions in the image by a tessellation of primitive “texels” –small shapes comprising black and white parts

Statistical methods Based on the idea that image texture is best described by a pro

bability distribution for the intensity values over regions of the image.

Ex) an image of a grassy field in which the blades of grass are oriented vertically

a probability distribution that peaks for thin, vertically oriented regions of high intensity, separated by regions of low intensity


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Other attributesOther attributes

If we had a direct way to measure the range from the camera to objects in the scene, we could produce a “range image” and look for abrupt range differences. Range image : each pixel value represents the distance

from the corresponding point in the scene to the camera.

Motion, color


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6.5 Scene Analysis (1/2)6.5 Scene Analysis (1/2)

Scene Analysis Extracting from the image the needed information

about the scene Requires either additional images (for stereo vision) or

general information about the kinds of scenes, since the scene-to-image transformation is many-to-one.

The required knowledge very general or quite specific explicit or implicit


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6.5 Scene Analysis (2/2)6.5 Scene Analysis (2/2)

Knowledge of surface reflectivity characteristics and shading of intensity in the image

give information about the shape of smooth objects in the scene.

Iconic scene analysis Build a model of the scene or parts of the scene

Feature-based scene analysis Extracts features of the scene needed by task Task-oriented or purposive vision


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6.5.1 Interpreting Lines and Curves 6.5.1 Interpreting Lines and Curves in the Imagein the Image Interpreting the line drawing

Association between scene properties and the components of a line drawing

Trihedral vertex polyhedraThe scene to contain only planar surfaces such that no more than three surfaces intersect in a point


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Three kinds of edges in Three kinds of edges in Trihedral vertTrihedral vertex polyhedra ex polyhedra (1/2)(1/2) There are only three kinds of ways in which two planes

can intersect in a scene edge. Occlude

One kind of edge is formed by two planes, with one of them occluding the other.

labeled in Fig. 6.15 with arrows (). the arrowhead pointing along the edge such that

surface doing the occluding is to the right of the arrow.


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Three kinds of edges in Three kinds of edges in Trihedral vertTrihedral vertex polyhedra ex polyhedra (2/2)(2/2)

Blade Two planes can intersect such that both planes are

visible in the scene. Two surfaces form a convex edge. Labeled with pluses (+).

Ford Edge is concave. Labeled with minus ()


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Labels for Lines at JunctionsLabels for Lines at Junctions


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Line-labeling scene analysis (1/2)Line-labeling scene analysis (1/2)

1. Labeling all of the junctions in the image as V, W, Y, or T junctions according to the shape of the junctions in the image


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Line-labeling scene analysis (2/2)Line-labeling scene analysis (2/2)

2. Assign +, , or labels to the lines in the image. An image line that connects two junctions must have

a consistent labeling. If there is no consistent labeling,

there must have been some error in converting the image into a line drawing.

the scene must no have been one of trihedral polyhedra.

Constraint satisfaction problem


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6.5.2 Model-Based Vision (1/2)6.5.2 Model-Based Vision (1/2)

If, we knew that the scene contained a parallelepiped (in Figure 6.15), we could attempt to fit a projection of a parallelepiped to components of an image of this scene.

A generalized cylinders as building blocks for model construction

Each cylinder has 9 parameters.


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Model-Based Vision (2/2)Model-Based Vision (2/2)

An example rough scene reconstruction of a human figure Hierarchical representation Each cylinder in the model

can be articulated into a set of smaller cylinders


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6.6 Stereo Vision and Depth 6.6 Stereo Vision and Depth InformationInformation Depth information can be obtained using stereo

vision, which based on triangulation calculations using two (or more) images.

Some depth information can be extracted from a single image. The analysis of texture in the image can indicate that

some elements in the scene are closer than are others. More precise depth information; If we know that a

perceived object is on the floor and the camera height above the floor, we can calculate the distance to the object.


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Depth Calculation from a Single Depth Calculation from a Single ImageImage


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Stereo VisionStereo Vision

Stereo vision uses triangulation. Two lenses whose centers are separated by a baseline, b. The image point of a scene point, at distance d, created

by these lenses. The angles of these image points from the lens centers,

, .

The optical axes are parallel, the image planes are coplanar, and the scene point is in the same plane as that formed by two parallel optical axes.


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Triangulation in Stereo VisionTriangulation in Stereo Vision


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The main complication in stereo The main complication in stereo visionvision In scenes containing more than one point, it must

be established which pair of points in the two images correspond to the same scene point.

We must be able to identify a corresponding pixel in the other image. correspondence problem


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Techniques for correspondence Techniques for correspondence problemproblem Geometric analysis reveals that we need only sea

rch along one dimension (epipolar line). One-dimensional searches can be implemented b

y cross-correlation of two image intensity profiles along corresponding epipolar lines.

We do not have to find correspondences between individual pairs of image points but can do so between pairs of larger image components, such as lines.

(c) 2000, 2001 SNU CSE Biointelligence Lab1 6.4.4 Finding Region Another method for processing image to find “regions” Finding regions Finding outlines.

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