Topic 10 - Image Analysis

1. Topic 10 - Image Analysis Department of Physics and Astronomy DIGITAL IMAGE PROCESSING Course 3624 Professor Bob Warwick

2. 10. Image Analysis Mapping, filtering and restoration techniques are all aimed at producing an “enhanced” image as an input to IMAGE ANALYSIS On the one hand image analysis can be carried out by visual inspection perhaps supported by a number of interactive software tools. At the other extreme, it may involve automated processing utilising very complex computer algorithms Here we consider a few relevant techniques under the headings: 10.1 Simple interactive techniques 10.2 Image segmentation 10.3 Feature description & recognition 10.4 Pattern recognition via cross-correlation

3. 10.1 Simple Interactive Tools (i) Description of a Point (selected via a mouse/cursor) Position = x, y (and via the calibration  scene coordinate θ,Φ) Gray level = fxy (and via the calibration  flux density W/m2) (iii) Subtraction of a Background Signal ie net =<f>-<b> where: (ii) Description of an Extended Object Position = <x>,<y> (centroid within the defined object region, weighted by gray level) Average gray level = <f> (iv) Distribution along a 1-d Cut through the Image (v) Distribution Radially and Azimuthally around a Point

4. 10.2 Image Segmentation This is the process of sub-dividing an image into its constituent parts (i.e. into features or objects which comprise the image). Segmentation is often the first step in an automated “feature-identification” procedure • Segmentation often relies on either: • The identification of the gray-level range which “characterises” the features/objects of interest (against the confusion of the surrounding scene. • (ii) The identification of edge/discontinuities which suitably delineate the features/objects (against the confusion of the surrounding scene). Method (i) often reduces simply to defining a suitable threshold in the gray-level distribution. Method (ii) often involves the use of an edge-detection filter

5. Segmentation by applying a gray-level threshold-I Example 1 The detection of bright point sources in an image. source detection threshold

6. Segmentation by applying a gray-level threshold II Example 2 The detection of a set of bright point sources against a varying background an image.

7. Segmentation by applying a gray-level threshold - III Example 3. How well might the rectangles be "identified" by the suitable choice of a gray-level threshold? EASY! HARD!

8. Segmentation via Edge Detection - I Discontinuities/edges in images can be detected by the use of either “gradient” or “Laplacian” spatial filters. Example 1: Using the Sobel filters -1 -2 -1 -1 0 1 0 0 0 -2 0 2 1 2 1 -1 0 1

9. Segmentation via Edge Detection - II Example 2: Using a Laplacian mask and an "edge-crossing" algorithm.

10. A Full Segmentation Process Compute a gradient image or “zero-crossing” image. Apply a threshold to remove clutter and noise. Apply “edge growing” and “edge thinning” algorithms. Apply “edge linking” and “stray-filament removal” algorithms.

11. Image Segmentation - Example

12. 10.3 Feature Description and Recognition Once an image has been segmented into a set of individual objects or features, the next step is to characterize the image in terms of these components. The possibilities range from: Object Identification – how many objects are there of a given type? Scene Analysis – what is inter-relation of the objects? Easy Hard A common requirement is to identify the individual objects against a specified (and restricted) set of possibilities – i.e., aRECOGNITIONproblem. • How should the objects be represented? The two possibilities are: • In terms of their (external) boundary characteristics. • i.e. the morphology (shape) of the object. • In terms of the (internal) object pixel characterictics. • i.e. the gray level, colour or texture of the object. A quantitative representation of the object characteristics is then provided by its descriptor values. Once measured, the object descriptor value is compared to the possible values for known types of object (held in a look-up table) so as to search for a match.

13. Some Object Descriptors 1. Compactness Parameter • pq’th Central Moment Normalization 1 Defines <x> Defines <y> 3. Texture Ideally descriptors are insensitive to variations in the object size, translation and rotation

14. Shape Recognition via Fourier Descriptors • Consider the set of (x,y) values of the boundary pixels as a set of complex numbers x+iy. • Compute the DFT of this set of complex numbers • Extract a subset of the Fourier values and use as descriptors. • As a check apply the inverse DFT to the restricted set • (setting the non-descriptors to zero) • Compare the limited set if descriptors with look-up tables for the “target” objects Aircraft silhouettes reconstructed from 32 DFT Descriptor values

15. 10.4 Pattern recognition via cross-correlation Convolution Cross-correlation

16. Pattern Recognition via Cross-Correlation cont. Cross-correlation techniques are very powerful when searching for objects/pattern of fixed size and orientation.

17. Pattern Recognition via Cross-Correlation cont. In 2-d the process involves cross-correlating the image with a sub-image (ie the template)