how-many-bees.blogspot.com

bees counting project

UCSD CSE190a Haili Wang. Sunday, April 11, 2010. Http:/ cseweb.ucsd.edu/classes/wi10/cse190-a/blogs.html. Wednesday, March 10, 2010. L channel responded index. This is a imagesc show the index table of max L channel responds. It looks good on the upper middle region. It was a big block of mud on the original image, but the index table just shows different texture than those bees. Sunday, March 7, 2010. Tuesday, March 2, 2010. Monday, March 1, 2010. I may have missed something that the result is completel...

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UCSD CSE190a Haili Wang. Sunday, April 11, 2010. Http:/ cseweb.ucsd.edu/classes/wi10/cse190-a/blogs.html. Wednesday, March 10, 2010. L channel responded index. This is a imagesc show the index table of max L channel responds. It looks good on the upper middle region. It was a big block of mud on the original image, but the index table just shows different texture than those bees. Sunday, March 7, 2010. Tuesday, March 2, 2010. Monday, March 1, 2010. I may have missed something that the result is completel...

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2 final reports

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6 filter bank result

7 many many clicks

8 filter bank

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bees counting project | how-many-bees.blogspot.com Reviews

https://how-many-bees.blogspot.com

UCSD CSE190a Haili Wang. Sunday, April 11, 2010. Http:/ cseweb.ucsd.edu/classes/wi10/cse190-a/blogs.html. Wednesday, March 10, 2010. L channel responded index. This is a imagesc show the index table of max L channel responds. It looks good on the upper middle region. It was a big block of mud on the original image, but the index table just shows different texture than those bees. Sunday, March 7, 2010. Tuesday, March 2, 2010. Monday, March 1, 2010. I may have missed something that the result is completel...

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how-many-bees.blogspot.com

1

bees counting project: filter bank

http://how-many-bees.blogspot.com/2010/03/filter-bank.html

UCSD CSE190a Haili Wang. Monday, March 1, 2010. In order to display properly, FB*256 : should be imagesc() with colormap('gray'):. Apply each of these filters to the image and save the largest *absolute* responded filter's index to an index table. After that do a computer the histogram of these index. Another problem is that the algorithm is very slow, takes 3 minutes to compute. Subscribe to: Post Comments (Atom). CSE 190a class website. L channel responded index. View my complete profile.

2

bees counting project: final reports

http://how-many-bees.blogspot.com/2010/04/final-reports.html

UCSD CSE190a Haili Wang. Sunday, April 11, 2010. Http:/ cseweb.ucsd.edu/classes/wi10/cse190-a/blogs.html. Subscribe to: Post Comments (Atom). CSE 190a class website. View my complete profile.

3

bees counting project: L channel responded index

http://how-many-bees.blogspot.com/2010/03/l-channel-responded-index.html

UCSD CSE190a Haili Wang. Wednesday, March 10, 2010. L channel responded index. This is a imagesc show the index table of max L channel responds. It looks good on the upper middle region. It was a big block of mud on the original image, but the index table just shows different texture than those bees. Subscribe to: Post Comments (Atom). CSE 190a class website. L channel responded index. View my complete profile.

4

bees counting project: filter bank result

http://how-many-bees.blogspot.com/2010/03/filter-bank-result.html

UCSD CSE190a Haili Wang. Sunday, March 7, 2010. I think the previous failure of filter bank approach was caused by alignment of those windows. In this post I am using a fine 2 by 2 window, overlap by 7 pixels, to slide through the input image(actual window size is 16 by 16, 2 7*2). It rejects those negative objects pretty well but also does not fire on the positive bees. Again, I may need a bigger sample size to get an ideal result. Subscribe to: Post Comments (Atom). CSE 190a class website.

5

bees counting project: February 2010

http://how-many-bees.blogspot.com/2010_02_01_archive.html

UCSD CSE190a Haili Wang. Wednesday, February 24, 2010. Big group bees results. Only red channel is used. Single channel is filtered by median filter and then convoluted by the different filters. These filters act as edge detectors, and toward the end they are rotated ccw about 45 degrees. These filters are hard coded. F = ([1 1 5 7 5 -10 -15 -10 5 7 5 1 1]);. F = ([1 1 5 7 5 0 -5 -10 -15 -10 -5 0 5 7 5 1 1]);. F = ([ 1 5 7 5 0 -5 -10 -15 -10 -5 0 5 7 5 1]);. F = ([ 1 5 7 5 0 -15 -10 -7 -3 0 5 7 5 1;.

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fg-object-removal.blogspot.com

Foreground object removal: Preliminary pedestrian removal results

http://fg-object-removal.blogspot.com/2010/02/preliminary-pedestrian-removal-results.html

Sunday, February 28, 2010. Preliminary pedestrian removal results. Currently, this is my pipeline to remove pedestrians:. Compute homographies between two views (I1 and I2) using SIFT and RANSAC. Detect pedestrians on both views (with bounding boxes). Warp pedestrian bounding boxes using homography from step 1, determine overlap (if any). Use method proposed by James Davis. To obtain a dividing boundary in overlap region. March 3, 2010 at 9:17 PM. Subscribe to: Post Comments (Atom).

fg-object-removal.blogspot.com

Foreground object removal: How google does surface normals

http://fg-object-removal.blogspot.com/2010/02/how-google-does-surface-normals.html

Thursday, February 25, 2010. How google does surface normals. Apparently, google does use 3D laser point clouds to estimate surface normals (or building facades as they call them), see this quote from http:/ google-latlong.blogspot.com/2009/06/introducing-smart-navigation-in-street.html. We have been able to accomplish this by making a compact representation of the building facade and road geometry for all the Street View panoramas using laser point clouds and differences between consecutive pictures.

fg-object-removal.blogspot.com

Foreground object removal: A relevant paper

http://fg-object-removal.blogspot.com/2010/02/relevant-paper.html

Thursday, February 18, 2010. Found a paper that may be useful: " Piecewise Planar City 3D Modeling from Street View Panoramic Sequences. The focus of this paper is 3D modeling, but they mention a multi-view stereo technique for dense depth estimation. This may make it possible to remove foreground objects based on histogram analysis of pixel depths (similar to the way foreground objects were removed in Dr. Zakhor's work. Which was the inspiration for this project). Subscribe to: Post Comments (Atom).

fg-object-removal.blogspot.com

Foreground object removal: Hole filling (and reading)

http://fg-object-removal.blogspot.com/2010/02/hole-filling-and-reading.html

Wednesday, February 10, 2010. Hole filling (and reading). Since it is likely I will need to incorporate one more view of the scene, I have been reading about the trifocal tensor in Hartley and Zisserman. I've also been reading some material on how to further refine the estimated homography here. Subscribe to: Post Comments (Atom). Preliminary pedestrian removal results. How google does surface normals. Hole filling (and reading). CSE 190a Spring 2010 bees counting project. Real Time Detection of Groceries.

fg-object-removal.blogspot.com

Foreground object removal: March 2010

http://fg-object-removal.blogspot.com/2010_03_01_archive.html

Thursday, March 4, 2010. Seam carving for content-aware image resizing. Uses the same technique James Davis uses, but for a different application. See the "Object removal" section (section 4.6) for another interesting application. I may play around with this to reduce the artifacts after removing people. Subscribe to: Posts (Atom). CSE 190a Spring 2010 bees counting project. Real Time Detection of Groceries. Dancer Identification within Shot Boundaries. Forgot to Mention Histograms.

fg-object-removal.blogspot.com

Foreground object removal: January 2010

http://fg-object-removal.blogspot.com/2010_01_01_archive.html

Wednesday, January 27, 2010. Odd results and some runtime errors. At this point, I am trying to debug the Josh Wills' motion segmentation code. After altering the code to use SIFT correspondences, I encountered a few bugs:. Attempted to access IndexSam(4997,:); index out of bounds because size(IndexSam)=[4996,4]. Error in = get warps at 56. From reading the code, it appears this function is using RANSAC to estimate the homography. I changed a hard-coded iters. Monday, January 25, 2010. The correspondence...

fg-object-removal.blogspot.com

Foreground object removal: Interesting paper

http://fg-object-removal.blogspot.com/2010/03/interesting-paper.html

Thursday, March 4, 2010. Seam carving for content-aware image resizing. Uses the same technique James Davis uses, but for a different application. See the "Object removal" section (section 4.6) for another interesting application. I may play around with this to reduce the artifacts after removing people. Subscribe to: Post Comments (Atom). CSE 190a Spring 2010 bees counting project. Real Time Detection of Groceries. Dancer Identification within Shot Boundaries. Forgot to Mention Histograms.

fg-object-removal.blogspot.com

Foreground object removal: Pedestrian detection

http://fg-object-removal.blogspot.com/2010/02/pedestrian-detection.html

Monday, February 22, 2010. Due to the lack of progress with the proposed method, I decided to simplify and restrict the problem to removal of pedestrians only. From a privacy standpoint, this would be a step beyond the face blurring that google already does. I have been testing with pedestrian detection. Code by Bastian Leibe. Some results on google streetview data can be seen here. I also tried pedestrian detection software by Liming Wang. Subscribe to: Post Comments (Atom). Hole filling (and reading).

fg-object-removal.blogspot.com

Foreground object removal: February 2010

http://fg-object-removal.blogspot.com/2010_02_01_archive.html

Sunday, February 28, 2010. Preliminary pedestrian removal results. Currently, this is my pipeline to remove pedestrians:. Compute homographies between two views (I1 and I2) using SIFT and RANSAC. Detect pedestrians on both views (with bounding boxes). Warp pedestrian bounding boxes using homography from step 1, determine overlap (if any). Use method proposed by James Davis. To obtain a dividing boundary in overlap region. Thursday, February 25, 2010. How google does surface normals. Due to the lack of pr...

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how-many-bees.blogspot.com

bees counting project

UCSD CSE190a Haili Wang. Sunday, April 11, 2010. Http:/ cseweb.ucsd.edu/classes/wi10/cse190-a/blogs.html. Wednesday, March 10, 2010. L channel responded index. This is a imagesc show the index table of max L channel responds. It looks good on the upper middle region. It was a big block of mud on the original image, but the index table just shows different texture than those bees. Sunday, March 7, 2010. Tuesday, March 2, 2010. Monday, March 1, 2010. I may have missed something that the result is completel...

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