Applying CV for Autonomous Vehicles using Python in Mumbai
RELATED RECENT PLACEMENTS
Omkar Dinkar
Akkodis
Top Engineering College in Nashik - MET Institute of Engineering, Bhujbal Knowledge City
Pushkar Sonwane
Akkodis
Deogiri Institute Of Technology & Manegement
Pravin Magar
Technorithmengg
Dr. D. Y. Patil Institute of Technology
Shruti Jadhav
Speed Engineering Solutions
Sandip University
Shubham Nashirkar
UPR Engineering Pvt Ltd
Shri Datta Meghe Polytechnic
Ritik Rathi
Cyient
Prof.Ram Meghe Institute of Technology & Research
Meghraj Patil
Tech Mahindra
Sinhgad College of Engineering, Pune
Akshay Pawar
Equilibrium Solutions
MET's Institute Of Engineering, Nashik, Maharashtra, India
Syllabus
This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
Course Overview
- 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.
Course Syllabus in Mumbai
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week - 01 Introduction to Computer Vision
- In this session, we will learn about
- Introduction to Autonomous Vehicles
- Introduction to Computer Vision
- Applications of Computer Vision
- Course Content - Introduction
- Understanding Images
Week - 02 Image Processing Techniques – I
- 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
Week - 03 Image Processing using Edge and Line Detection
- 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
Week - 04 Projective and Stereo Geometry
- In this session, we will learn about
- Image coordinate systems
- Projective Geometry
- Perspective Projections
- Multiview Geometry
- Stereography and Depth Imaging
- Stereo Correspondence
Week - 05 3D Computer Vision
- In this session, we will learn about:
- Projective Geometry for 3D
- Camera calibration methods
- Epipolar Geometry
- Stereovision
Week - 06 Feature Extraction , Neural Networks and Image Classification
- 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
Week - 07 Feature Detectors and Descriptors
- In this session, we will learn about:
- Feature Detectors
- Moravec’s Detector
- Harris Corner Detector
- Feature Descriptors
- SIFT
- ORB
- Feature matching methods
- Feature Detectors
Week - 08 Optical Flow
- 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
Week - 09 Object Tracking
- 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
Week - 10 Image Segmentation
- 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
Week - 11 Object Detection
- 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
Week - 12 3D Object Detection
- 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
Our courses have been designed by industry experts to help students achieve their dream careers
Industry Projects in Mumbai
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
2D Object Detection with tensorFlow
Our courses have been designed by industry experts to help students achieve their dream careers
Ratings & Reviews by Learners
Skill-Lync has received honest feedback from our learners around the globe.
Instructors profiles
Our courses are designed by leading academicians and experienced industry professionals.
1 industry expert
Our instructors are industry experts along with a passion to teach.
5 years in the experience range
Instructors with 5 years extensive industry experience.
Areas of expertise
- Autonomous Vehicle Controls
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