Exam Review - Stanford Computer Vision Labvision.stanford.edu/.../lectures/exam_overview.pdf ·...

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

Moving Average

Thresholding

Linear Systems

Cross Correlation

Edge DetectionHow does Canny Edge Detector work?

Image Matching

Harris Detector

SIFTScale Invariant

Camera

Future Research

Self Driving CarsEmail Juan Carlos to get involved.

Exam Review - Stanford Computer Vision Labvision.stanford.edu/.../lectures/exam_overview.pdf ·...

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Transcript of Exam Review - Stanford Computer Vision Labvision.stanford.edu/.../lectures/exam_overview.pdf ·...

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