Electrical · Course

Applying CV for Autonomous Vehicles using Python

This 3 month program from Skill-Lync teaches the student everything there is to know about computer vision. MATLAB will be used as a tool.

Talk to a counsellor →View curriculum

Duration: 12 weeks
Content: 21 hours
Projects: 2
Mode: Online

Overview

What you'll
be able to do.

The program is designed to provide an introductory /Intermediate level explanation of the various concepts in Computer Vision
The course is essentially for a sequential approach towards learning computer vision.
The course covers the basics of computer vision, understanding the perception of an image/Image sequence (video), working of a camera and applications of computer vision in Industry.
The course provides hands-on experience with coding challenges and projects that are relevant in the autonomous vehicle industry.
This course is designed to provide an overview and a platform for students to learn the concepts of computer vision and work on collaborative projects.

Tools you'll use

MATLABPythonTensorFlowOpenCVKeras TensorFlow 2

Curriculum

12 modules,
built with industry.

01Applying CV for Autonomous Vehicles using Python12 modules
1. Week - 01 Introduction to Computer VisionW01
  - In this session, we will learn about
    
    Introduction to Autonomous Vehicles
    
    Introduction to Computer Vision
    
    Applications of Computer Vision
    
    Course Content - Introduction
    
    Understanding Images
2. Week - 02 Image Processing Techniques – IW02
  - In this session, we will learn about
    
    Image Filters
    
    Correlation
    
    Convolution
    
    Noise in Images
    
    Types of Noise
    
    Filters for Noise
    
    Image Gradients
    
    Edge Detection Techniques
3. Week - 03 Image Processing using Edge and Line DetectionW03
  - In this session, we will learn about:
    
    Canny Edge Detector
    
    Hough Transformation - Lines
    
    Hough Transformation - Circles
    
    Domain Transformation
    
    Understanding Frequency Domain
    
    Spatial to Frequency Domain transformations
4. Week - 04 Projective and Stereo GeometryW04
  - In this session, we will learn about
    
    Image coordinate systems
    
    Projective Geometry
    
    Perspective Projections
    
    Multiview Geometry
    
    Stereography and Depth Imaging
    
    Stereo Correspondence
5. Week - 05 3D Computer VisionW05
  - In this session, we will learn about:
    
    Projective Geometry for 3D
    
    Camera calibration methods
    
    Epipolar Geometry
    
    Stereovision
6. Week - 06 Feature Extraction , Neural Networks and Image ClassificationW06
  - In this session, we will learn about:
    
    Image Classification
    
    Dimensionality Reduction
    
    Principal Component Analysis
    
    Convolutional Neural Networks
    
    Datasets
    
    Mobile Net Architecture
    
    Image Classification - Performance Metrics
7. Week - 07 Feature Detectors and DescriptorsW07
  - In this session, we will learn about:
    
    Feature Detectors
    
    Moravec’s Detector
    
    Harris Corner Detector
    
    Feature Descriptors
    
    SIFT
    
    ORB
    
    Feature matching methods
8. Week - 08 Optical FlowW08
  - In this session, we will learn about:
    
    Optical Flow
    
    Horn-Shunck method
    
    Lucas Kanade sparse optical flow
    
    Gunnar-Farneback Optical flow
    
    Deep learning based optical flow models
9. Week - 09 Object TrackingW09
  - In this session, we will learn about:
    
    Introduction to Object Tracking
    
    Deep SORT
    
    Lucas-Kanade-Tomasi(KLT) Tracker
    
    Minimum Of Sum Squared Error (MOSSE) Tracker
    
    Mean Shift Tracker
10. Week - 10 Image SegmentationW10
  - In this session, we will learn about:
    
    Introduction to Image Segmentation
    
    Methods of Segmentation
    
    Applications of Segmentation
    
    Thresholding based segmentation
    
    Otsu’s Thresholding
    
    Morphological Operations
    
    Connected Components
    
    Datasets for image segmentation
    
    Deep learning architectures for image segmentation
11. Week - 11 Object DetectionW11
  - In this session, we will learn about:
    
    Introduction to Object Detection
    
    Region Proposals
    
    Graph cut segmentation
    
    Selective search
    
    Object Detection Datasets
    
    Object Detection Models
    
    Tensorflow model zoo
    
    Evaluation metrics for object detection models
12. Week - 12 3D Object DetectionW12
  - In this session, we will learn about:
    
    Introduction to 3D Object Detection
    
    Types of 3D Object Detection
    
    Stereo Image based Detection
    
    Monocular 3D Object Detection

Industry projects

2 projects
you'll ship.

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

Implementation of an image classification model using MobileNet architecture

Image Classification is a very important task in deep learning employed in vast areas and has a very high usability and scope. Student will have to understand and implement an imag…

2D Object Detection with tensorFlow

The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. Student will h…

Fee

One fee.
Everything included.

Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals

Premium

₹40,000

Inclusive of all charges

1 Year Access
Personalized Page
Live Individual & Group Sessions
Personalized & Hands-On
Job-Oriented Industry Relevant Curriculum

Check eligibility →

FAQ

Questions,
answered.

Does the course come with a live project?

Yes, the course comes with weekly projects, and there are 12 projects in total. Also, there is a mini project post 6^th week of the course.

Can I find a job after this course?

This course will make you ready for the industry, and you can easily find a job after its completion.

Will I get a merit certificate once the course is completed?

The merit certificate will be awarded to the top 5% of the class. However, everyone will be given a certificate of completion.

Is MobileNet architecture a part of the course?

Yes, you will be taught about MobileNet architecture, and the mini-project will be based on its fundamentals.

Can a commerce graduate enrol for this course?

No, the course is designed for people who have some experience in programming and have come from an engineering background.

Can the course fee be paid in instalments?

Yes, we have a 0% EMI plan for paying the course fee in instalments.

Can I get a scholarship for this course?

Yes, you can take a scholarship test and get a 10% scholarship on the course fee.

Will Computer Vision help me in other industries too?

Computer Vision is finding its application in fields like law, agriculture, etc. Therefore, you should opt-in for this course for some exposure.

You might also like.

Course · 12 weeksPath Planning & Trajectory Optimization Using C++ & ROS→Course · 12 weeksLocalization, Mapping, and SLAM using Python→Course · 12 weeksIntroduction to ROS/GitHub/Linux→Course · 12 weeksBasics of Computer Vision using Python→Course · 8 weeksIntroduction to Camera Systems→

Next step

Not sure it's
the right fit?

A counsellor will walk you through the syllabus, projects and fee options — and tell you honestly if another course suits you better.

Call us Enquire

Start your next chapter this Dussehra

You're on the list

Applying CV for Autonomous Vehicles using Python

What you'll
be able to do.

12 modules,
built with industry.

2 projects
you'll ship.

Implementation of an image classification model using MobileNet architecture

2D Object Detection with tensorFlow

One fee.
Everything included.

Questions,
answered.

You might also like.

Not sure it's
the right fit?

Get the syllabus + a call back

Applying CV for Autonomous Vehicles using Python

What you'llbe able to do.

12 modules,built with industry.

2 projectsyou'll ship.

Implementation of an image classification model using MobileNet architecture

2D Object Detection with tensorFlow

One fee.Everything included.

Questions,answered.

You might also like.

Not sure it'sthe right fit?

Get the syllabus + a call back

What you'll
be able to do.

12 modules,
built with industry.

2 projects
you'll ship.

One fee.
Everything included.

Questions,
answered.

Not sure it's
the right fit?