Basics of Computer Vision using Python by Skill Lync: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The Basics of Computer Vision using Python Certification Course is a 12 week long course that discusses the advanced concepts and techniques of computer vision which is a combination of deep learning and machine learning concepts.

Through the Basics of Computer Vision using Python Training Course, learners will be introduced to various terminologies and applications of computer vision using different software tools. After successful completion of the course through video lectures and weekly challenges, learners will be rewarded with a valid certificate.

The Basics of Computer Vision using Python Live Course will give learners an opportunity to work on major technical projects and build their professional portfolios. Learners who are interested in applying for the course can request a demo session before enrolling.

Course and certificate fees

The fees for the course Basics of Computer Vision using Python is -

Head	Amount
Programme fees	Rs. 40,000

certificate availability

Yes

certificate providing authority

Skill Lync

Who it is for

The Basics of Computer Vision using Python course can be opted by anyone with an interest to learn about computer vision applications.
The course can be taken by learners with basic knowledge of algebra, statistics, and programming (Python/Matlab).

Eligibility criteria

Candidates are required to have basic knowledge of statistics and linear algebra along with basic programming skills in Python or Matlab.

What you will learn

Knowledge of python

After completing the Basics of Computer Vision using Python Classes, you will gain insights into the following topics:

Introduction to computer vision
Image processing and edge detection
Image Segmentation and features
Camera Models and Calib Transformation
Stereo Vision and 2D shape + PCA
SOTA ML-based CV Techniques8-Oct-2021
Image registration

The syllabus

Week 1: Introduction to Computer Vision

General introduction,
History of CV
Formulating the field, why is it a hard topic?
Definition of computer vision
Required components
What qualifies as a vision system
Humans as a vision system: how good do we “see”?
Useful applications
Image acquisition using a camera
Different types of cameras for different domain
Stills, Video, DSLR, Bodycam, Drone
Infrared, Ultrasonography, Magnetic resonance
Physics of Color: color spectrum
Human encoding of color: Rods and cons of eyes
Color spaces:
RGB, CMYK, HSV
Color balance
Camera specifications:
Pinhole
CMOS
CCD
Image specifications:
Pixel (Picture element)
Aspect ratio, HD, Interlacing
Conversions
Type of digital images:
Binary, Grayscale, Color
Conversion techniques

Week 2: Image processing

Noise Removal
Pixel Neighborhood
Salt and pepper noise
Morphing to hide cracks in the image
Applying filters to images
Convolution of matrices
Types of Filter:
Mean or Box filtering
Median Filter
Mode, Mean, Pass
Generic properties of smoothing
Anisotropic filtering
Gaussian: Isotropy condition, formulation, figure
Weight influence of pixels by their distance to the center pixel
Spread parameter
Motivating examples
Filter Separability
Computation and Maths
Gaussian Separability

Week 3: Edge Detection

Introduction to edges and gradients
Intensity difference
1D versus 2D edge detection
Edge detection in mammals
1D signals and 2D signals
Difference and derivative mask
Examples
Image Gradient
Image noise: Gaussian noise
Smoothing + Edge detection
Gaussian Derivative Signals
2D gradient operators
Prewitt Masks
Sobel Masks
Steerable filters
Laplacian filters
Laplacian of Gaussian
Zero Crossings
Canny edge detection
Hysteresis thresholding
Non-maximal Suppression

Week 4: Image Segmentation and features

Thresholding based on histogram
Otsu, Adaptive Otsu
Formulation, Advancements, and effectiveness
Examples
Distance Metric: Norm functions
Thresholding based on different metrics, covariance-based
Different types of background subtraction
Mean, Euclidean, Mahalanobis
Covariance matrix, multidimensional mahalanobis
Shadow modeling
Transform to color spaces
Multimodal background distribution
Gaussian Mixture Model
Foreground Assignment
Clustering to Image Segmentation
Agglomerative Clustering
K Means, K Means ++
Mean Shift Clustering
Hierarchical Clustering

Week 5: Binary Image Operation

Morphology:
Erosion, Dilation
Open, Close
Connected component
Counting objects: Sequential count etc
Recursive count
Remove Small Features
Hough Transformation Algorithm
Radon Transformation Algorithm
Image Pyramids: Gaussian Laplacian Coding Compression

Week 6: Shape of Objects

Largest component
Medial axis
Boundary coding
Chain Coding
Shape Numbering
Quadtree Representation
Bounding box
Perimeter, Compactness, Circularity
Centroid
Spatial Moments
Central
Second third order
Similitude Moment
Dimensionality Reduction
Linear basis set
Principal Component Analysis
Eigen Values and Vectors
Finding Eigen sets
Test on synthetic and real data
Face Recognition using PCA: kernel trick

Week 7: Motion

Definition, simple motion
Image differentiation
Single constant threshold
Weber's Law
Optical flow
Formulas, geometry, example
Normal optic flow
Weighted aggregate,
Hierarchical Motion Estimation
Motion: Use of linear Algebra
3D motion of a point
Matrix operations for different motion in objects
Pinhole Camera Model
2D matrix motion
Translation Motion
Similarity Motion
Affine Motion
Motion History Image
Spatial Pattern of where motion occurred
Progression of motion
Motion Energy Image
Silhouette Difference

Week 8: Matching & Tracking

Motivation, Example
Feature-based tracking
How to find good features to track
Find Interest Points (General)
Panoramic stitching
Features from Accelerated Segment (FAST)
Harris Detector
Gradients
Window weighing function
Harris Corner Response Function

Week 9: Interest Point

SURF algorithm
SIFT algorithm for automated feature selection
Free alternative to SIFT and SURF in OpenCV
Laplacian Of Gaussian
Automated Feature selection
Diff Of Gaussian
Covariance tracking
Descriptor Matrix
Finding best match
Rotation Invariance
Kanade-Lucas-Tomasi (KLT) Tracker
Tracking Features
Formulations
Reduction Pyramid
Select “good” feature based on Eigen Value
Mean shift tracking
Weighted histograms using spatial kernels
Evaluating similarity between distributions using Bhattacharyya coefficient
Object tracking by target localization (in each frame) by maximizing the similarity function using mean shift
Template Matching
Sum-of-Absolute Differences
Sum-of-Squared Differences
Normalized Cross-Correlation

IWeek 10: mage Registration

Lens
Thin Lens Model
Focus, DoF, Aperture,
Projective Camera Model
Pinhole Camera
Intrinsic and Extrinsic Camera Parameters
Homogeneous Coordinate
Projection
Camera Projection
Camera Matrices
Estimating camera matrices
Extracting parameter P
Calibration
Projection
Perspective Effective
Affine
Orthographic
Weak
Transformation: Translation, Rotation, Skew, Reflection
Planar homography/ Projective Transformation
Solving homography matrix
Normalized Direct Linear Transformation
Example on real 3D data
RANSAC Algorithm
Gold Standard Algorithm

Week 11: Lens & Camera projection

3D intro
Motivation
Ambiguity in single View
Geometry for simple stereo system
Depth and Calibration
Epipolar Geometry
Baseline, Epipole, Epipolar Line, Epipolar Plane
Epipolar Constraint
Converging camera
Parallel Camera
Camera Motion
Fundamental Matrix
Computation
8 point algorithm
Depth Matrix
Stereo Matching Algorithm
Correspondence Search
Estimate disparity by finding corresponding points
Depth is inversely related to disparity
Stereo Matching as Energy Minimization
Graph cut algorithm

Week 12: SOTA ML based CV Techniques

LeNet
AlexNet
General detection techniques
YOLO
GAN
Autonomous Vehicle Specific Networks
Gaussian Neural Network
Confidence in classification output: decide object confidence for autonomous vehicle

Admission details

Follow the steps below to enroll in the Basics of Computer Vision using Python Live Course:

Step 1: Go to the official website by clicking on the URL given below -

https://skill-lync.com/computer-science-engineering-courses/basics-computer-vision

Step 2: Click on the "Enroll Now" option provided on the course page.

Step 3: Select a suitable payment package and unlock access by submitting your name, email id and phone number.

How it helps

The Basics of Computer Vision using Python Certification Benefits are given below:

The course will help learners build a knowledge base in computer vision and machine learning techniques.
Through the course learners will be included in real-world projects involving face analysis, road camera footage and 3D camera systems that help in understanding the subject in depth.
The Basics of Computer Vision using Python course will equip learners with fine skills and expertise in computer vision systems.

FAQs

What is the duration of the Basics of Computer Vision using Python Training course?

The Basics of Computer Vision using Python online course has a duration of 12 weeks.

What are the software tools learned through the Basics of Computer Vision using Python Training course?

The Basics of Computer Vision using Python course assist learners in developing software skills in Python, OpenCV, numpy, pandas, matplotlib, scikit-learn, Tensorflow, Keras and CUDA.