9.913 pattern recognition for vision class9 - object detection and recognition bernd heisele
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9.913 Pattern Recognition for Vision
Class9 - Object Detection and Recognition
Bernd Heisele
Outline
• Object Detection• Object Recognition
Object Detection
• Task: Given an input image, determine if there are objects of a given class in the image and where they are located.
Face Detection System Architecture
Testing
Image Features
ROC for Image Features
Gray
Gray + Haar
Haar
Gray + Grad
Positive Training Data
Real vs. Synthetic
Real
Synthetic
ROC for Classifiers
LDA
Linear SVM
Poly2
Global vs. Components
(Whole Face)
Component-based Detection
Some Examples
ROC Component vs. Global
• About 40000 faces
• 68 people
• 13 poses
• 43 illuminations condition
• CMU PIE database
Training on Faces
Positive
Facial Negative
Non-facial Negative
Use the remainder of the face in the negative training set
Training on Faces
Red: Trained on facial and non-facial negative set.
Blue: Trained only on non-facial negative set.
Pair-wise Biasing
Often, many components classify correctly, with only a few errors.
Use the pair-wise relative position information from training data to bias the result image.
Pair-wise Biasing
Result Images
Biased Results
ROC Pair-wise Biasing
Red: Trained on facial and non-facial negative set.
Blue: Trained only on non-facial negative set.
Dashed: Biasing andtrained on facial and non-facial negative set.
Pedestrian Detection
Object Recognition
• Task: Given an image of and object of a particular class identify which exemplar it is.
Recognition System Architecture
Multi-class Classification with SVM
Training: N (N-1) / 2Classification: N - 1
Training: NClassification: N
The two different architecture has similar performance!!
Global Approach
1. Detect and extract face
2. Feed gray values of extracted face into N SVMs
3. Classify based on maximum output
Each SVM is one vs. all approach
Global Approach with Clustering
T1. Partition training images of each person into viewpoint- specific clusters
T2. Train a SVM on each cluster.
R1. Detect and extract face
R2. Feed extracted face to all SVMs
R3. Take maximum over all SVM outputs
Component-based Approach
1. Detect face and extract components
2. Combine gray values of components to a feature vector , and feed to the N SVMs
3. Take maximum over all SVM outputs
ROC Component vs. Global Recognition
• Trained and tested on frontal and rotated faces.
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