Exam Review - Stanford Computer Vision Labvision.stanford.edu/.../lectures/exam_overview.pdf · 2015. 12. 3. · Ranjay Krishna Exam Review 3-Dec-15 Exam 2015.12.07 3:30pm-6:30pm

Exam Review 3-Dec-15Ranjay Krishna

Exam ReviewRanjay Krishna


Basic Exam Facts


Exam2015.12.073:30pm-6:30pmMudd Chemistry Building LEC

Note: one single-sided 8.5x11" sheet of notes is allowed in the final.


15 True False Questions (30 minutes)

15 Multiple Choice Questions (30 minutes)

4 Short Answer Questions with 4 sub parts each. (90 minutes)

Exam Layout


Recognizing Faces and Objects










K = 1 K = 3

Overfitting vs. Underfitting


Decision Boundaries


Other Issues• Dimension of features• Normalization?• Size of training dataset



Target Variance• More data• Regularize

Target Bias??











Regions of Images, and Segmentation


Segmentation







You should be able to understand the clusters formed would be different with different definitions of what a cluster means.



K-means Issues• How do you pick the number of clusters?• How do you prevent a bad local minima?• How do you choose what features to use?

Color or location or maybe something else?








Pixels and Features


Convolution


Convolution


Moving Average


Thresholding


Linear Systems



Cross Correlation




Edge DetectionHow does Canny Edge Detector work?





Image Matching


Image Matching



Harris Detector


SIFTScale Invariant


Camera









Future Research


Self Driving CarsEmail Juan Carlos to get involved.

Documents

Exam Review - Stanford Computer Vision Labvision.stanford.edu/.../lectures/exam_overview.pdf · 2015. 12. 3. · Ranjay Krishna Exam Review 3-Dec-15 Exam 2015.12.07 3:30pm-6:30pm