CS 4670

Project2 report

Xiaobo Shen(xs83)

Changchen He(ch627)

Design choices:

1. The main framework of the program is given, but we modified the function signature

of the computeHarrisValues function by adding 'CFloatImage &harrisOri' argument.

This is useful for holding the computed orientation.

2. We implemented our own Gaussian smoothness kernel which has controllable sigma and

Window size parameters

3. We implemented a simple descriptor not just directly taking the intensity values but

normalizing the intensity value by the square root of mean so that the features are

invariant to illumination change.

4. We implemented a custom descriptor which takes the first order and second order central

moments of the intensities from the rotated 5x5 window, and we compute some scale and

rotation invariant values from those central moments. the simple descriptor feature vector

is also appended to this feature vector. We believe that the central moment and the derived

scaling the rotation invariant values are useful features for performing matching.

5. We wrote automatic testing scripts for running experiments which saved a lot of human

labor time.

Strength and weakness

Strength:

1. Our system has multiple feature descriptors functions which can be selected by user's choice.

2. Our custom descriptor outperforms the other two in terms of average AUC on some particular image data set such as graf

Weakness:

1. Our MOPS descriptor does not meet our expectation of performance. It works well in the Yosemite image data set but in graf

image data set, it works worse than our modified simple feature descriptor.

2. We need to manually select the threshold value for the Harris corner detector which need to be varied on different

image data set. It needs a lot of user time to fine-tune.

Experiment results:

harris image for Graf img1.ppm:

harris image for yosemite yosemite1.jpg:

bikes Average AUC:

bikes
	simple	MOPS	custom
ssd	0.23	0.644	0.254
ratio	0.45	0.636	0.444

graf Average AUC:

graf
	simple	MOPS	custom
ssd	0.68	0.42	0.705
ratio	0.6	0.293	0.63

leuven Average AUC:

leuven
	simple	MOPS	custom
ssd	0.22	0.638	0.274
ratio	0.614	0.802	0.667

wall Average AUC:

wall
	simple	MOPS	custom
ssd	0.52	0.681	0.486
ratio	0.696	0.705	0.64

Roc Curves for graf:

Roc Curves for Yosemite:

Self-taken image matching result (use MOPS descriptor and ratio test match):

Extra credits:

1. We implemented a custom descriptor which takes the first order and second order central

moments of the intensities from the rotated 5x5 window, and we compute some scale and

rotation invariant values from those central moments. the simple descriptor feature vector

is also appended to this feature vector. We believe that the central moment and the derived

scaling the rotation invariant values are useful features for performing matching.