Outline of Object Recognition - Appearance-based Methods

Appearance-based Methods

- Use example images (called templates or exemplars) of the objects to perform recognition

- Objects look different under varying conditions:

• Changes in lighting or color
• Changes in viewing direction
• Changes in size / shape

- A single exemplar is unlikely to succeed reliably. However, it is impossible to represent all appearances of an object

1. Edge matching

• Uses edge detection techniques, such as the Canny edge detection, to find edges.
• Changes in lighting and color usually don’t have much effect on image edges
• Strategy:

1. Detect edges in template and image
2. Compare edges images to find the template
3. Must consider range of possible template positions

• Measurements:

• Good – count the number of overlapping edges. Not robust to changes in shape

• Better – count the number of template edge pixels with some distance of an edge in the search image

• Best – determine probability distribution of distance to nearest edge in search image (if template at correct position). Estimate likelihood of each template position generating image

2. Divide-and-Conquer search

• Strategy:

• Consider all positions as a set (a cell in the space of positions)
• Determine lower bound on score at best position in cell
• If bound is too large, prune cell
• If bound is not too large, divide cell into subcells and try each subcell recursively
• Process stops when cell is “small enough”

• Unlike multi-resolution search, this technique is guaranteed to find all matches that meet the criterion (assuming that the lower bound is accurate)

• Finding the Bound:

• To find the lower bound on the best score, look at score for the template position represented by the center of the cell
• Subtract maximum change from the “center” position for any other position in cell (occurs at cell corners)

• Complexities arise from determining bounds on distance

3. Greyscale matching

• Edges are (mostly) robust to illumination changes, however they throw away a lot of information
• Must compute pixel distance as a function of both pixel position and pixel intensity
• Can be applied to color also

4. Gradient matching

• Another way to be robust to illumination changes without throwing away as much information is to compare image gradients
• Matching is performed like matching greyscale images
• Simple alternative: Use (normalized) correlation

5. Histograms of receptive field responses

• Avoids explicit point correspondences
• Relations between different image points implicitly coded in the receptive field responses
• Swain and Ballard (1991), Schiele and Crowley (2000), Linde and Lindeberg (2004, 2012)

6. Large modelbases

• One approach to efficiently searching the database for a specific image to use eigenvectors of the templates (called eigenfaces)
• Modelbases are a collection of geometric models of the objects that should be recognised