Price 2590 + VAT
DURATION 2 Days

Course Overview

This course holds a two days’ review of the basic functions of computer vision including:
Basic filters, edge detectors, feature extractor, object (face) identifier, optical flow and additional subjects.
The students are experiencing this field by coding in matlab and python with opencv

Course Outline:

Image processing & Matching

1. Introduction to OpenCV with Python
• Installation / API

2. Basic Operators
• Median, Box, common neighbors
• convolution and kernel filters
• Coding example: filtering an image and seeing results
• Segmentation and thresholding methods
• Morphological operators: dilate erode
• Coding example: dilate/erode showing results and solving a basic problem
• Connected components and labeling

3. Edge /Corner / Line detectors
• Sobel
• Canny
• Roberts
• Laplacian
• Hough transform
• Coding example: running Sobel vs Canny and watching results

4. Image Matching
• Harris
• Scale Invariant – why??
• SIFT
• Advance Lab
– SIFT
– Effects of different params/config (bins, scaling, best match vs NN)
– Effects of Noise in the image
• SURF

5. Object detectors
• Object detection – Theory
• Face detection – Viola jones Haar Filters & Integral Image
• HoG6. Mapping transforms -optional
• Theory: Translation, Rotation, Rigid body, affine perspective
• Lab OpenCV transformations7. 3D understanding
• Camera Projection theory
• Two cameras
• Structured light

8. Optical flow and tracking
•  Lucas-Kanade Theory
• Code Review in OpenCV ( Link ) & Applications

9. Deep Learning Intro
• Overview of the technology
• Tools like Keras & TensorFlow

10. Summary Exercise
• Processing path: Image processing & scaling->Computer vision feature extraction->Machine Learning classifier

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