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--- ece4580:module_detection [2017/01/24 03:00] – [Module #1] typos
+++ ece4580:module_detection [2024/08/20 21:38] (current) – external edit 127.0.0.1
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 ====== Object Detection ======
+**Note:** To be filled out on Friday or Saturday.
   - Boosting
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+===== Module #1: Human Detection =====
+The classic human detection paper is by Dalal and Triggs, and it involves using the Histogram of Oriented Gradients (HOG) feature descriptor together with Support Vector Machines.  It does quite well, though more modern and more expensive methods have superceded it.
-===== Module #1 =====
-Clustering
+===== Module #2: Boosting =====
-  - Study [[https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm|k-means clustering algorithm]] and the algorithmic steps for k-means clustering.
-  - Download (or clone) the clustering skeleton code [[https://github.gatech.edu/fchu9/ECE4580_LearningModules/tree/master/objectDetection/clustering|here]]
-  - Implement k-means clustering algorithm working in RGB space by following the algorithmic steps. You are welcome to implement from scratch without skeleton code.
-  - Test your algorithm on segmenting the image //segmentation.jpg// using k=3
-  - Try different random initialization and show corresponding results.
-  - Comment on your different segmentation results.
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-===== Module #2 =====
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+Boosting as a concept is about creating a very accurate detector from a series of somewhat low accuracy detectors.  The trick is to control the false positive rate (have it be very low) while tolerating false negatives.  Cascading enough of these together will eventually create a detector that has bot a low false positive rate and a low false negative rate (after all, how many times can one be wrong in a row?).
+----------
 ;#;
 [[ECE4580:Modules|ECE4580 Learning Modules]]
 ;#;