jlintern-cse190.blogspot.com

jlintern-cse190

Wednesday, February 17, 2010. Image loading and feature extraction is working. I am computing the mean and standard deviation in grayscale brightness over adjacent rectangular regions. These values are my features for training. I have processed available training that I have and I'm currently working with roughly 12000 negative samples (no text in image or text not centered) and 6000 positive samples (text completely fills window). Once my detector is reasonably accurate, perform OCR on the results. ...

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jlintern-cse190 | jlintern-cse190.blogspot.com Reviews

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Wednesday, February 17, 2010. Image loading and feature extraction is working. I am computing the mean and standard deviation in grayscale brightness over adjacent rectangular regions. These values are my features for training. I have processed available training that I have and I'm currently working with roughly 12000 negative samples (no text in image or text not centered) and 6000 positive samples (text completely fills window). Once my detector is reasonably accurate, perform OCR on the results. ...

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jlintern-cse190 | jlintern-cse190.blogspot.com Reviews

https://jlintern-cse190.blogspot.com

Wednesday, February 17, 2010. Image loading and feature extraction is working. I am computing the mean and standard deviation in grayscale brightness over adjacent rectangular regions. These values are my features for training. I have processed available training that I have and I'm currently working with roughly 12000 negative samples (no text in image or text not centered) and 6000 positive samples (text completely fills window). Once my detector is reasonably accurate, perform OCR on the results. ...

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jlintern-cse190.blogspot.com

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jlintern-cse190: February 2010

http://www.jlintern-cse190.blogspot.com/2010_02_01_archive.html

Wednesday, February 17, 2010. Image loading and feature extraction is working. I am computing the mean and standard deviation in grayscale brightness over adjacent rectangular regions. These values are my features for training. I have processed available training that I have and I'm currently working with roughly 12000 negative samples (no text in image or text not centered) and 6000 positive samples (text completely fills window). Once my detector is reasonably accurate, perform OCR on the results. ...

2

jlintern-cse190: January 2010

http://www.jlintern-cse190.blogspot.com/2010_01_01_archive.html

Wednesday, January 27, 2010. I was going to be doing this project in Matlab and using a multilayer perceptron as my classifier. However I've since made the decision to work on this in C /OpenCV and use an SVM. Reason for C :. I've been working with Kai Wang, a graduate student in the CSE dept. He is also working with text detection and his project is implemented in C with OpenCV. I figured that it would be a lot easier to utilize his help if the two projects were under the same platform. Implement the fu...

3

jlintern-cse190: Week 4 Update

http://www.jlintern-cse190.blogspot.com/2010/01/week-4-update.html

Wednesday, January 27, 2010. I was going to be doing this project in Matlab and using a multilayer perceptron as my classifier. However I've since made the decision to work on this in C /OpenCV and use an SVM. Reason for C :. I've been working with Kai Wang, a graduate student in the CSE dept. He is also working with text detection and his project is implemented in C with OpenCV. I figured that it would be a lot easier to utilize his help if the two projects were under the same platform. Implement the fu...

4

jlintern-cse190: Introduction and Project Outline

http://www.jlintern-cse190.blogspot.com/2010/01/introduction-and-project-outline.html

Wednesday, January 13, 2010. Introduction and Project Outline. Recognition of text in arbitrary real world images is a largely unsolved problem in Computer Vision and Machine Learning. Contrast this with document-based OCR of scanned text which is solved to a near-human degree of accuracy. Image Text Recognition includes all of the difficulties of traditional OCR methods with some additional challenges. Http:/ dl.dropbox.com/u/3301354/190ppt.ppt. Work To Be Done. Implement word recognition algorithm.

5

jlintern-cse190: Week 7 Update

http://www.jlintern-cse190.blogspot.com/2010/02/week-7-update.html

Wednesday, February 17, 2010. Image loading and feature extraction is working. I am computing the mean and standard deviation in grayscale brightness over adjacent rectangular regions. These values are my features for training. I have processed available training that I have and I'm currently working with roughly 12000 negative samples (no text in image or text not centered) and 6000 positive samples (text completely fills window). Once my detector is reasonably accurate, perform OCR on the results. ...

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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: 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: 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: 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).

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jlintern-cse190.blogspot.com

jlintern-cse190

Wednesday, February 17, 2010. Image loading and feature extraction is working. I am computing the mean and standard deviation in grayscale brightness over adjacent rectangular regions. These values are my features for training. I have processed available training that I have and I'm currently working with roughly 12000 negative samples (no text in image or text not centered) and 6000 positive samples (text completely fills window). Once my detector is reasonably accurate, perform OCR on the results. ...

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