Basics of Computer Vision using Python

BY
Skill Lync

Learn everything about the applications of computer vision using Python through the Basics of Computer Vision using Python Course.

Lavel

Beginner

Mode

Online

Duration

12 Weeks

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.

The highlights

  • Merit certificate
  • 12 weeks duration
  • Course videos
  • Expert instructors
  • Project portfolio page
  • One-one zoom support sessions
  • Group zoom support sessions
  • Email and telephone support
  • Course-specific forum group
  • Industry oriented projects
  • Case studies
  • 1-on-1 demo session
  • Course counselling
  • Personalized hands-on support from expert engineers

Program offerings

  • Merit certificate
  • Individual video support
  • Group video support
  • Email support
  • Forum support
  • Telephone support
  • Professional portfolio
  • Technical projects
  • 1-on-1 demo session
  • Course counselling
  • 12 weeks duration

Course and certificate fees

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

HeadAmount
Programme feesRs. 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

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.

On which platform can I get access to the Basics of Computer Vision using the Python course?

The Basics of Computer Vision using Python Live Course can be accessed on the Skill-Lync platform.

Does the Basics of Computer Vision using Python courses provide placement opportunities?

No, the Basics of Computer Vision using Python Certification course does not offer placement opportunities.

In what type of companies can I use the skills gained from the Basics of Computer Vision using Python Training course?

The skills in image detection for hardware or API development are employed by companies like Google, Amazon, Facebook, and Apple.

Articles

Popular Articles

Latest Articles

Similar Courses

Python Foundations

PW Skills

Online
Beginner
Free

Python Interview Questions and Answers

Great Learning

Online
Beginner
Free

Python Fundamentals for Beginners

Great Learning

Online
Beginner
Free

Python for Beginners to Advance

Udemy

Online
Beginner
₹ 2,499

Learn Python Turtle Using Block Coding

Udemy

Online
Beginner
₹399 ₹799

Master Python Basics For Developer

Udemy

Online
Beginner
₹475 ₹3,499

Programming in Python for Beginners

Udemy

Online
Beginner
₹ 799

Learn Python 3 Programming from Scratch

Udemy

Online
Beginner
₹475 ₹1,299

Courses of your Interest

Professional Certificate Course in Data Science

Professional Certificate Course in Data Science

Newton School

8 Months Online
Beginner

JavaScript Foundations

PW Skills

Online
Beginner
Free

Technical Analysis Series

PW Skills

3 Months Online
Beginner
Free

C Programming Foundations

PW Skills

Online
Beginner
Free

Cracking the Coding Interview in Java Foundation

PW Skills

5 Months Online
Beginner
Free
Getting Started with Generative AI APIs

Getting Started with Generative AI APIs

Codio via Coursera

3 Weeks Online
Beginner
Generating code with ChatGPT API

Generating code with ChatGPT API

Codio via Coursera

3 Weeks Online
Beginner
Prompt Engineering for ChatGPT

Prompt Engineering for ChatGPT

Vanderbilt via Coursera

Online
Beginner

Data Structures and Algorithms in Java

Great Learning

Online
Beginner
Free

Angular7 for Beginners

Great Learning

Online
Beginner
Free

More Courses by Skill Lync

Advanced CFD Meshing using ANSA

Skill Lync

4 Weeks Online
Beginner
₹ 40,000

Introduction to Aero Thermal simulation using ANSY...

Skill Lync

12 Weeks Online
Beginner
₹ 40,000

Basics of CATIA V5

Skill Lync

4 Weeks Online
Beginner
₹ 40,000

Embedded C Essentials

Skill Lync

3 Months Online
Beginner

Introduction to Automotive Electronics

Skill Lync

24 Weeks Online
Beginner

Reinforced Cement Concrete Design

Skill Lync

12 Weeks Online
Beginner

Post Graduate Program in Computational Fluid Dynam...

Skill Lync

2 Months Online
Beginner

Construction Planning using Primavera P6

Skill Lync

3 Months Online
Beginner
Business Analyst Fundamentals for Beginners

Business Analyst Fundamentals for Beginners

Skill Lync

3 Months Online
Beginner

Design of RCC and PSC Superstructures using LUSAS

Skill Lync

2 Months Online
Beginner

Trending Courses

Popular Courses

Popular Platforms

Learn more about the Courses

Download the Careers360 App on your Android phone

Regular exam updates, QnA, Predictors, College Applications & E-books now on your Mobile

Careers360 App
150M+ Students
30,000+ Colleges
500+ Exams
1500+ E-books