6.4.4 Finding Region

A region 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. (c) 2000-2002 SNU CSE Biointelligence Lab

Split-and-merge method • The algorithm begins with just one candidate region, the whole image. • Until no more splits need be made. • For all candidate regions that do not satisfy the homogeneity property, are each split into four equal-sized candidate regions. • Adjacent candidate regions are merged if their pixels satisfying homogeneity property. (c) 2000-2002 SNU CSE Biointelligence Lab

“Cleaned Up” the regions found by Split-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. (c) 2000-2002 SNU CSE Biointelligence Lab

6.4.5 Using Image Attributes Other Than 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. (c) 2000-2002 SNU CSE Biointelligence Lab

Methods 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 probability 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 (c) 2000-2002 SNU CSE Biointelligence Lab

Other 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 (c) 2000-2002 SNU CSE Biointelligence Lab

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 (c) 2000-2002 SNU CSE Biointelligence Lab

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 (c) 2000-2002 SNU CSE Biointelligence Lab

6.5.1 Interpreting Lines and Curves in the Image • Interpreting the line drawing • Association between scene properties and the components of a line drawing • Trihedral vertex polyhedra • The scene to contain only planar surfaces such that no more than three surfaces intersect in a point Figure 6.15 A Room Scene (c) 2000-2002 SNU CSE Biointelligence Lab

Three kinds of edges in Trihedral vertex polyhedra • 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. (c) 2000-2002 SNU CSE Biointelligence Lab

Three kinds of edges in Trihedral vertex polyhedra (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 () (c) 2000-2002 SNU CSE Biointelligence Lab

Line-labeling scene analysis (1/2) • 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 (c) 2000-2002 SNU CSE Biointelligence Lab

Line-labeling scene analysis (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 (c) 2000-2002 SNU CSE Biointelligence Lab

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. (c) 2000-2002 SNU CSE Biointelligence Lab

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 Figure 6.19 A Scene Model Using Generalized Cylinders (c) 2000-2002 SNU CSE Biointelligence Lab

6.6 Stereo Vision and Depth Information • Depth information can be obtained using stereovision, 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. (c) 2000-2002 SNU CSE Biointelligence Lab

Stereo Vision • Stereo vision uses triangulation. • Two lenses whose centers are separated by a baseline, b. • The image point of a scene point, at distanced, 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. (c) 2000-2002 SNU CSE Biointelligence Lab

The main complication in stereo vision • 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 (c) 2000-2002 SNU CSE Biointelligence Lab

Techniques for correspondence problem • Geometric analysis reveals that we need only search along one dimension (epipolarline). • One-dimensional searches can be implemented by 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) 2000-2002 SNU CSE Biointelligence Lab

6.4.4 Finding Region