MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01C8A0FC.D3CBF120" This document is a Single File Web Page, also known as a Web Archive file. If you are seeing this message, your browser or editor doesn't support Web Archive files. Please download a browser that supports Web Archive, such as Microsoft Internet Explorer. ------=_NextPart_01C8A0FC.D3CBF120 Content-Location: file:///C:/453AC971/Yoon_proj1.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" CSE576 (Winter 2008) Project #1

CSE576 (Winter 2008) Project #1

Woon Jong Yoon (4/17/2008)

Detecting interest points /Feature Descriptor

I used the Harris corner measure discussed in lecture to detect interest points. The Harris corner measure is computed as determinant divided by tra= ce of the Harris matrix. All points for which this measure is greater than some threshold are identified. My first and only feature descriptor is a simple = 5 X 5 window descriptor. For each of the points of interest, I stored the value= s of the pixels in the 5 x 5 window with this point as the center.

Based on careful observation, a threshold of 0.5 is observed as a good number compared to the state of the art technique, SIFT. See Figure 1. Also= the bike example shows how feature detection is working on different viewpoint pictures (Figure 3)

<= span style=3D'mso-spacerun:yes'> <= span style=3D'font-size:10.0pt'> Figure 1. The results of feature detection (Left) wind= ow descriptor with threshold 0.5 (Rigtht) SIFT.

<= span style=3D'mso-spacerun:yes'> <= span style=3D'font-size:10.0pt'> 

Figure 2. After Harris and HarrisMax= operation of yosemite1.jpg

 

<= !--[if gte vml 1]> <= /p>

Figure 3. Some more results of feature detect= ion with translational change (Threshold=3D0.5)

Matching features

There are two feature match algorithms are used. First= , This just calculate the Euclidean distance betwe= en two feature descriptors, and matches a feature in the first image with the clos= est feature in the second image.Second one is to Use Euclidean distance = to calculate the distance between two feature descriptor and then apply a threshold on the ratio of the best distance matched to the second best dist= ance matched. This matching can match multiple features in the first image to the same feature in the second image. Currently the threshold is set at 0.5 and this means if the second best distance method is at least twice as much as the first distance then that's good enough for a match.

Benchmark tests

Average pixel error/av= erage AUC (both used the simple window descriptor):

Benchmark Set

SSD matching

Ratio matching

bikes

527/0.26

528/0.007

leuven

371/0.400

371/0.0002

Based on the benchmark results, both matching methods show similar resul= ts with the simple window descriptor. Strangely, wall and giraffe pictures didn’t’ work with the benchmark test. It is recommenced that I would rather try a better descriptor to compare the average pixel error and= AUC to conclude that which descriptor performs considerably better.

ROC curves

Gnuplot tool didn’t work for m= e. I will leave this as a future work.

 

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