Embedded Vision Pattern Recognition: Techniques and Applications

1. 嵌入式視覺Pattern Recognition for Embedded Vision • Template matching • Statistical / Structural Pattern Recognition • Neural networks

2. Embedded Vision System • Image acquisition • Image Processing • Feature Extraction • Decision Making(Pattern Recognition)

3. Pattern Recognition model 1. Template matching 2. Statistical Pattern Recognition: based on underlying statistical model of patterns and pattern classes. 3. Structural (or syntactic) Pattern Recognition : pattern classes represented by means of formal structures as grammars, automata, strings, etc. 4. Neural networks: classifier is represented as a network of cells modeling neurons of the human brain (connectionist approach).

4. Pattern Representation • A pattern is represented by a set of d features, or attributes, viewed as a d-dimensional feature vector.

5. Classification Mode test pattern Preprocessing Feature Measurement Classification Feature Extraction/ Selection training pattern Preprocessing Learning Training Mode Two Process for Pattern Recognition system

6. Generic concepts for PR Pattern Feature vector - A vector of observations (measurements). - is a point in feature space . Hidden state -Cannot be directly measured. - Patterns with equal hidden state belong to the same class. Task - To design a classifer (decision rule) which decides about a hidden state based on an onbservation.

7. Pattern Representation by Feature Vector for Character Recognition • X=[x1, x2, … , xn], each xj a real number • Xj may be object measurement • Xj may be count of object parts • Example: object rep. [#holes, Area, moments, ]

8. Example height Task:identity recognition. The set of hidden state is The feature space is weight Training examples Linear classifier:

9. Pattern Recognition system Image processing Feature extraction Class assignment Object Classifier Learning algorithm • Image acquisition and image processing. • Feature extraction aims to create discriminative features good for classification. • Classifier. • Learning algorithm sets PR from training examples-- supervised learning

10. Feature extraction Task: to extract features which are good for classification. Good features: • Objects from the same class have similar feature values. • Objects from different classes have different values. “Good” features “Bad” features

11. Feature extraction methods Feature extraction Feature selection Problem can be expressed as optimization of parameters of featrure extractor . Supervised methods: objective function is a criterion of separability (discriminability) of labeled examples, e.g., linear discriminat analysis (LDA). Unsupervised methods: lower dimesional representation which preserves important characteristics of input data is sought for, e.g., principal component analysis (PCA).

12. Classifier A classifier partitions feature space X into class-labeled regions such that and The classification consists of determining to which region a feature vector x belongs to.Borders between decision boundaries are called decision regions.

13. Decision-Tree Classifier • Uses subsets of features in seq. • Feature extraction may be interleaved with classification decisions • Can be easy to design and efficient in execution CSE803 Fall 2014

14. Decision Trees #holes 0 2 1 moment of inertia #strokes #strokes  t < t 1 0 best axis direction #strokes 0 1 4 2 0 90 60 - / 1 x w 0 A 8 B

15. Classification using nearest class mean • Compute the Euclidean distance between feature vector X and the mean of each class. • Choose closest class, if close enough (reject otherwise)

16. Unsupervised learning Input: training examples {x1,…,x} without information about the hidden state. Clustering: goal is to find clusters of data sharing similar properties. A broad class of unsupervised learning algorithms: Classifier Classifier Learning algorithm Learning algorithm (supervised)

17. Classifier Learning algorithm Example of unsupervised learning algorithm k-Means clustering: Goal is to minimize