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Visual Learning and Recognition: Difference between revisions
→Object Detection and Instance Segmentation
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* Ensembles of networks | * Ensembles of networks | ||
==Object Detection and | ==Object Detection and Segmentation== | ||
Beginning of Lecture 10 (Oct 1) | |||
===Edge Templates + Nearest Neighbor=== | |||
Gavrila & Philomen (1999) | |||
# From a raw image, do feature extraction and calculate distance transform. | |||
# Do nearest neighbor search. | |||
Cons: | |||
* Templates are hand-made. | |||
===Haar Wavelets + SVM=== | |||
A Trainable System for Object Detection. (Papageorgou & Poggio, 2000) | |||
# Extract Overcomplete Representation | |||
#* Called Haar Wavelets. Similar to CNN filters. | |||
#* Wavelet features can be calculated by averaging all faces. Similar to CNN features. | |||
# Do SVM Classifier | |||
;+ Parts (2001) | |||
Trained an SVM for face, legs, left arm, right arm. | |||
When detecting a person, make sure all parts are in the correct location and shape with some constraints. | |||
===YYY + adaBoost=== | |||
Basically, do the same as before (Haar Wavelets) but replace SVM with adaBoost. | |||
;Rectangular differential features (Viola & Jones 2001) | |||
Use fast features to throw out parts of the image. | |||
Then do processing on the remainder. | |||
Became the standard object detection system in OpenCV. | |||
;Learnt wavelets + adaBoost | |||
Works on more than just faces. | |||
Ensemble face detection. | |||
===Dynamic Programming=== | |||
Efficient matching of pictorial structures (Felzenszwalb & Huttenlocher, 2000) | |||
Basically have a cartoon model and match the position & orientation of each part. | |||
Probabilistic Methods for Finding People (Ioffe & Forsyth, 1999) | |||
===More Techniques=== | |||
How to detect objects at different scale? | |||
One trick is to detect the horizon line and scale based on the horizon line. | |||
Sliding Window: | |||
Create multiple scales of the image and detect at each scale. | |||
This is done by building a feature pyramid using an image pyramid. | |||
===Histograph of Gradients (HoG)=== | |||
How many octaves? However many octaves to reduces the image size to the template size + 1 for 2x2 upscaling. | |||
How many levels? Generally people try 10 levels. | |||
==Will be on the exam== | ==Will be on the exam== | ||