Topic 10 - Image Analysis

DIGITAL IMAGE PROCESSING

Course 3624

Department of Physics and Astronomy

Professor Bob Warwick

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

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)

(ii) Description of an Extended Object

Position = <x>,<y> (centroid within the defined object region, weighted by gray level)

Average gray level = <f>

(iii) Subtraction of a Background Signal

ie net =<f>-<b> where:

(iv) Distribution along a 1-d Cut through the Image

(v) Distribution Radially and Azimuthally around a Point

10.2 Image Segmentation

Segmentation often relies on either:

(i) 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

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 by applying a gray-level threshold-I

Example 1 The detection of bright point sources in an image.

source detection threshold

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.

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!

Segmentation via Edge Detection - I

Example 1:

Using the Sobel filters

-1 -2 -1 -1 0 1

0 0 0 -2 0 2

1 2 1 -1 0 1

Discontinuities/edges in images can be detected by the use of either “gradient” or “Laplacian” spatial filters.

Segmentation via Edge Detection - II

Example 2:

Using a Laplacian mask and an "edge-crossing" algorithm.

A Full Segmentation Process

1. Compute a gradient image or “zero-crossing” image.

2. Apply a threshold to remove clutter and noise.

1. Apply “edge growing” and “edge thinning” algorithms.

2. Apply “edge linking” and “stray-filament removal” algorithms.

Image Segmentation - Example

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?

A common requirement is to identify the individual objects against a specified (and restricted) set of possibilities – i.e., a RECOGNITION problem.

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.

Easy

Hard

Some Object Descriptors

3. Texture

2. pq’th Central Moment

Normalization 1 Defines <x> Defines <y>

1. Compactness Parameter

Ideally descriptors are insensitive to variations in the object size, translation and rotation

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

10.4 Pattern recognition via cross-correlationConvolution Cross-correlation

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

Pattern Recognition via Cross-Correlation cont.

In 2-d the process involves cross-correlating the image with a sub-image (ie the template)