CS294-2 Visual Grouping and Object Recognition (Prof. Jitendra Malik)

November 24, 1999

Lecture 23: 3D Object Recognition (cont.)

Scribe Notes by Shawn Hsu

 

Sections:

  1. Counterexamples to Hypothesis I
  2. Hypothesis II
  3. Hypothesis III
  4. Object Recognition

 

 

1. Counterexamples to Hypothesis I

 

Last lecture we talked about Goldmeir's hypothesis on how we perceive similarity among different forms. Hypothesis I stated that the more parts that are in common between two forms, the more similar they appear. It then follows that if the parts themselves are different, then the forms are less similar. However, it could be shown that his hypothesis is not complete. The counterexamples to Goldmeir's hypothesis are shown below.


 

 

 


In Figure 2, it would appear to most people that (a) and (d) are most similar, even though there is less change from (a) to (b), since (b) is just (a) with the vertical line extended. So, according to hypothesis I, (a) should be most similar to (b).

 

 

 


 


In Figure 10, it seems like (a) and (c) are more similar. However, (b) is just (a) with some points moved further out, while (c) is (a) with all the points moved outward. Again, hypothesis I would conclude that (b) is more similar to (a), even though to us (c) would appear to be more similar to (a). Another case is shown in figure 11. Most people would pick (c) to be more similar to (a). However, from hypothesis I, (b) should be more similar to (a) because the center line moved less in (b), while the line moved more in (c).

 

 

2. Hypothesis II: Similarity as identity of relations

 

We have shown with the previous figures that there are numerous counterexamples to Hypothesis I. Hypothesis II states that proportional changes of parts of a figure result in a more similar figure. This would require the figures to have homologous parts. The examples are shown below.

 


In figure 27, we would normally perceive (a) and (c) as being most similar. However, comparing (a) to (b), it could be seen that (b) is the same as (a), except three of the points are larger, while in (c), all of the points have been enlarged. This would be a counterexample to hypothesis I, but in this case it supports hypothesis II. Hypothesis II would explain this phenomenon as the result of (c) being the proportional enlargement of all the points in (a), which makes the figures look similar.

 

 



Figure 24 is used as a counterexample to hypothesis II. In this case, we would normally perceive (a) and (c) to be most similar, even though (b) is the result of proportional expansion of the space between the lines in (a). Hypothesis II, in this case, would conclude that (a) and (b) would be most similar. For figure 25, less lines are used, and now it appears (a) and (b) are most similar.

 

 


3. Hypothesis III

 

Hypothesis III: The similarity of figures is related to differences and agreements of phenomenally realized qualities, as opposed to logically constructible attributes. Two examples of such qualities are:

 

a)     Variations of Groupings

b)     Singular Values

 

Phenomenon: Human observers don't perceive a collection of points, instead we extract something "special" from these points. We can think of this "special" quality as being a phenomenon.

 

3.1 Variations of Groupings

 

Examples of finding similarity in different forms through variations in groupings are shown below.

 


 


In figure 29, (b) and (c) both have 9 points, but we would normally perceive (a) and (c) to be most similar, even though (a) has 10 points. This is because we group (a) and (c) as one line, while we group (b) into 2 lines. Another example for grouping is shown in figure 30. We would perceive (a) to be more similar to (b), even though (b) and (c) have the same number of points. This is because we perceive (a) and (b) as a hexagon, while we perceive (c) as an ellipse. The way these points are grouped is more important than the actual points themselves.

 

 

3.2 Singular Values

 

There are some special configurations of forms that we can visually detect. Examples of singular values include parallelism, verticality, and symmetry.


 


In Figure 41, we perceive (a) to be most similar to (c). This is due to the fact that the lines show parallelism in the two figures.

 

 

3.3 Reasons for the development of the Human Visual System

 

Let's digress a bit and talk about why our visual system has this kind of similarity measure. It can be argued that this similarity measure allows us to recognize classes of similar objects that are not exactly the same. In nature, it is almost always the case that objects in the same class (ex. tigers, penguins, trees) do not look exactly the same, but we must be able to deduce that they belong to a certain class. There are two reasons why the perceived shapes of objects in the same class are going to vary.

 

  1. 3D shape of the object within a class could be different
  2. the 3D to 2D transformation of an object could introduce different poses

 

The reasons listed above can introduce variations in:

 

  1. Metric Features (lengths, angles, etc.)
  2. Grouping
  3. Singular Features (parallelism, symmetry, etc)

 

Metric Features There are a lot of variations within a category in metric features, which arise from both 3D variation and pose. This is not a reliable indicator for finding similarity.

 

Grouping This feature changes a lot less than metric features. We group features together the same way, even though the pose could change. Grouping offers a more accurate measure of similarity.

 

Singular Features This feature is preserved under certain projections (ex. parallelism is preserved under orthographic projection). Symmetry is also preserved, such as in a picture (perspective projection).

 

The conclusion is that our visual system is developed as a way to recognize these classes of objects despite of the differences in the appearance of objects in the same class. One can argue that the way our visual system determines the similarity of objects came about because we can use features such as grouping, parallelism, symmetry, etc, to more accurately recognize an object, while other features such as length and height change too much due to variations in 3D form and 2D pose to provide a good measure of object similarity.

 

 

4. Object Recognition

 

4.1 Suggested Approach to Object Recognition

 

First, take the object/image and describe it hierarchically as a tree of groups. Then, look at each group and note the singular features of each group. We describe three attempts at using this approach.

 

4.2 Binford Generalized Cylinders (1971)

 

Binford proposed that each part could be modeled as a cylinder, and the relationships between these cylinders could be described. A generalized cylinder could be specified by three qualities: cross section, axis, and sweeping rule. While the definitions of cross section and axis should be clear, we need to define sweeping rule. Sweeping rule determines how the cross section changes as the axis is traversed. So, in the case of a regular cylinder, the cross section is a circle, the axis is a straight line extending from the circle, and the sweeping rule dictates that the size of the circle remains the same. We have more examples below.

 

Example 1: Cone

Cross Section circle

Axis straight line

Sweeping Rule cross section decreases linearly

 

Example 2: Cube

Cross Section square

Axis straight line

Sweeping Rule cross section stays constant

 

Example 3: Torus

Cross Section circle

Axis - circle

Sweeping Rule cross section stays constant

 

We can specify many shapes with few parameters using generalized cylinders. But why do we do this? The reason is because we want to recover 3D shapes from 2D images. We can determine the 3D structure of an object depicted as a 2D line drawing. We want to do the same with our algorithm. Generalized cylinders offer us a way to use a small number of parameters to represent a 3D shape. We can recover 3D shapes given a limited amount of image data. One insight of this theory is that a small number of parameters can represent many shapes. Another insight is that we can use these parameters to deduce the shape of a 3D object in a 2D image, where some of the 3D information is lost.

 

The criticism of generalized cylinders is that they are not general enough. Generalized cylinders could not be used to represent complex objects. In order to represent complex objects, more parameters need to be used. We would reach a point where there are too many parameters, and the theory collapses.

 

4.3 Hoffman & Richards Parts at Concavities (1984)

The goal of this theory is to explain how to segment objects into parts. The proposed rule is that we segment at the negative curvature extrema. This makes finding the figure/ground problem very important, because depending on whether an edge is on the figure or ground, the segmentation changes. For example, in the face/vase illusion, the segmentation changes depending on whether the figure is perceived as a face or a vase.

 

What could we find out about the actual surface from information about the occluding contour? Koenderink showed that the sign of the curvature of the occluding contour is the same as the sign of the Gaussian curvature on the surface (Koenderink's Theorem).

 

4.4 Biederman Geons (1987)

 

This is an extension of the generalized cylinders theory. Biederman used the same idea, but then quantized the parameters so that each parameter could only be changed to certain cases. For example, axis would be set to either be straight or curved. Biederman renamed generalized cylinders to Geons.