Region identification

By -

Shape representation and description

• Defining the shape of an object can prove to be very difficult. Shape is usually represented verbally or in figures.

• There is no generally accepted methodology of shape description. Further, it is not known what in shape is important.

• Current approaches have both positive and negative attributes; computer graphics or mathematics use effective shape representations which are unusable in shape recognition and vice versa.

• In spite of this, it is possible to find features common to most shape description approaches.

• Common shape description methods can be characterized from different points of view

o Input representation form: Object description can be based on boundaries or on more complex knowledge of whole regions.

o Object reconstruction ability: That is, whether an object's shape can or cannot be reconstructed from the description.

o Incomplete shape recognition ability: That is, to what extent an

object's shape can be recognized from the description if objects are occluded and only partial shape information is available.

o Local/global description character: Global descriptors can only be used if complete object data are available for analysis. Local descriptors describe local object properties using partial information about the objects. Thus, local descriptors can be used for description of occluded objects.

o Mathematical and heuristic techniques: A typical mathematical

technique is shape description based on the Fourier transform. A representative heuristic method may be elongatedness.

o Statistical or syntactic object description.

o A robustness of description to translation, rotation, and scale transformations: Shape description properties in different resolutions.

clip_image002

Fig 1: Image analysis and understanding methods

• Sensitivity to scale is even more serious if a shape description is derived, because shape may change substantially with image resolution.

• Therefore, shape has been studied in multiple resolutions which again cause difficulties with matching corresponding shape representations from different resolutions.

• Moreover, the conventional shape descriptions change discontinuously.

• A scale-space approach aims to obtain continuous shape descriptions if the resolution changes continuously.

clip_image004

Fig 2: (a)Original image 640x480 (b)Contours of (a) (c)Original image 160x120 (d)Contours of (c) (e)Original image 64x48 (f)Contours of (e)

• In many tasks, it is important to represent classes of shapes properly, e.g. shape classes of apples, oranges, pears, bananas, etc.

• The shape classes should represent the generic shapes of the objects belonging to the same classes well. Obviously, shape classes should emphasize shape differences among classes while the influence of shape variations within classes should not be reflected in the class description.

• Despite the fact that we are dealing with two-dimensional shape and its description, our world is three-dimensional and the same objects, if seen from different angles (or changing position/orientation in space), may form very different 2D projections.

• The ideal case would be to have a universal shape descriptor capable of overcoming these changes to design projection-invariant descriptors.

• Consider an object with planar faces and imagine how many very different 2D shapes may result from a given face if the position and 3D orientation of this simple object changes with respect to an observer.

• In some special cases, like circles which transform to ellipses, or planar polygons, projectively invariant features (invariants) can be found.

• Object occlusion is another hard problem in shape recognition. However, the situation is easier here (if pure occlusion is considered, not combined with orientation variations yielding changes in 2D projections), since visible parts of objects may be used for description.

• Here, the shape descriptor choice must be based on its ability to describe local object properties -- if the descriptor only gives a global object description; such a description is useless if only a part of an object is visible.

• If a local descriptor is applied, this information may be used to compare the visible part of the object to all objects which may appear in the image.

• Clearly, if object occlusion occurs, the local or global character of the shape descriptor must be considered first.

Comments

Post a Comment

Popular posts from this blog

Top hat transformation

By -
Image
• The top hat transformation is used as a simple tool for segmenting objects in gray-scale images that differ in brightness from background, even when the background is of uneven gray-scale. • The top hat transform is superseded by the watershed segmentation for more complicated backgrounds. • Assume a gray-level image X and a structuring element K. The residue of opening as compared to original image X ∖ (X ∘ K) constitutes a new useful operation called a Top hat transformation. • The top hat transformation is a good tool for extracting light objects on a dark but slowly changing background. Those parts of the image that cannot fit into structuring element K are removed by opening. • Subtracting the opened image from the original provides an image where removed objects stand out. • The actual segmentation can be performed by simple thresholding (Fig 18). • If an image were a hat, the transformation would extract only the top of it, provided that the structuring element is larger...
Read more

Digital Image Processing part1

By -
Image
SEGMENTATION THRESHOLDING • Segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). • The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. • Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. • More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. • Complete segmentation - set of disjoint regions uniquely corresponding with objects in the input image. • Cooperation with higher processing levels which use specific knowledge of the problem domain is necessary. • Partial segmentation - regions do not correspond directly with image objects. • Image is divided into separate regions that are homogeneous with respect to a chosen property such as bright...
Read more

Basics of Spatial Filtering,Frequency Domain filters and Homomorphic filtering

By -
Image
Basics of Spatial Filtering some neighborhood operations work with the valuesof the image pixels in the neighborhood and the corresponding values of asubimage that has the same dimensions as the neighborhood.The subimage iscalled a f i l t e r , m ask , k ernel, template, or window , with the first three terms beingthe most prevalent terminology.The values in a filter subimage are referred toas coefficients , rather than pixels. The concept of filtering has its roots in the use of the Fourier transform for signal processing in the so-called f r equency domain . This topic is discussed in more detail in Chapter 4. In the present chapter, we are interested in filtering operations that are performed directly on the pixels of an image.We use the term spatial filtering to differentiate this type of process from the more traditional frequency domain filtering. The mechanics of spatial filtering are illustrated infig.The process consists simply of moving the filter mask from point to poi...
Read more