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Applying CV for Autonomous Vehicles using MATLAB

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.

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A Quick Overview

As of 2020, Google has more than 4 trillion photos stored in its data centers. However, when we search for a picture of Skill Lync, the results are surprisingly accurate each time! 

These accurate results within seconds have been made possible by Computer Vision. Computer Vision is a technology that is working towards giving cameras the ability to see things, detect them and make decisions accordingly, similar to what the human eye does. 

Computer Vision has seen it’s applications in the field of medicine, business,  agriculture, law enforcement etc. When we talk about it’s application in Autonomous Vehicle, Computer vision enables the vehicle to: 

  • Detect objects on the road, such as pedestrians, traffic lights and vehicles. 
  • Create situational awareness for the vehicle depending upon road conditions
  • Low light detection capabilities
  • Create a map based on what it sees 

These few aspects make computer vision the backbone of Autonomous Vehicle. Keeping this in mind, we at Skill lync have curated a 12 week program that is entirely detected towards teaching students computer vision applied to autonomous vehicle. 

The course also includes two practical sessions per week and two major projects to help the student get a better understanding of the subject. To help you know more about what we plan on covering each week, a descriptive syllabus is given below. 


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COURSE SYLLABUS

1Introduction to Computer Vision

Computer Vision aims at giving a high- level understanding to a machine. They focus on making a machine capable of making its own decisions. In the first week, we will walk you through on what computer vision is. The topics that will be covered in this week are: 

  • What is computer vision? 
  • Applications of computer vision 
  • Course details 
  • Software used in the course 
  • Understanding Images 
  • Loading and Displaying Images 
  • Basic Image modifications using Python/OpenCV 
  • Image representation modes: RGB, HSV, Greyscale etc. 

2Image Processing Techniques – I

In order to extract information from the image, image processing techniques are carried out on it. These extracted features in terms of shapes and alphabets, helps in recognizing an object. This week, we will look into the techniques that are used to do so. 

The topics, that will be discussed are:

  • Image Filters 
  • Gaussian Noise
  • Noise removal methods 
  • Convolution and Correlation operations
  • Mean and Median filters 
  • Template matching methods
  • Edge Detection methods : Canny , Sobel , Prewitt etc. 
  • Image Gradients 
  • Hough transforms – straight lines , circles and curves 
  • Time/Spatial to frequency domain Image conversion

3Image Geometries and Camera

The images are viewed on an x-y plane. Each of it’s pixels is traced in a manner to get information out of it. This is facilitated using image geometrics and cameras. In this week, we will learn about the different geometries that are used in computer vision:

  • Image coordinate system: Different systems 
  • Projective geometry 
  • Perspective Projection 
  • Multiview Geometry 
  • Stereography and Depth Imaging 
  • Stereo Correspondence 
  • Epipolar Geometry 
  • Rigid body transformations 
  • Geometric camera calibration 
  • Multiplane calibration 
  • Finding corners and matching feature points 
  • Performing transformations with intrinsic and extrinsic parameters 

4Motion Models

A video is a sequence of images. In order to extract information from it, these images will be evaluated every time with a different angle. The topics that will be discussed under motion models are:

  • Applications of motion modelling 
  • Motion estimation and techniques 
  • Optical flow 
  • Lucas and Kanade methods: Hierarchical and Sparse methods
  • Full motion, general motion and affine motion models 

5Trackers / Filters in Computer Vision

Filters are applied to an image to enhance it. However, applying a filter to a video would mean to continuously keep a track on all the images and apply filters to it. The process that is carried out to do this, will be discussed this week. 

  • Introduction to Tracking 
  • Feature trackers
  • Steps in tracking, Prediction and Correction 
  • Constant velocity, acceleration models 
  • Kalman filter: Prediction, correction, Intuition and tracking with Kalman filters 
  • Bayes Filters 
  • Particle Filters and Localization using Particle Filters 
  • Real life tracking issues and comparison of different methods 

6Image Recognition and Classification

There are various types of things around us, from cats, dogs, humans, tree so and so forth. One of the applications of computer vision is to differentiate one object from another.    This understanding of what falls under what category is done by image image recognition and then are further classified by image classification.  The topics, discussed under this week are: 

  • Introduction to Recognition and classification 
  • Supervised and Unsupervised learning methods 
  • Dimensionality reduction , Principal Component Analysis , Discriminative classifiers , 
  • Machine learning classifiers 
  • Feature extractors: HOG , Haar Cascade and CNN models 
  • Introduction to Convolutional Neural Networks 
  • Building blocks of Neural Network for computer vision 

7Video Analysis and Image Segmentation:

It is very easy for a human to differentiate one object from another just by looking at it. However, this task is done by computer vision following a number of steps. This becomes, even more difficult when it comes to videos, to recognize and detect objects from noise. An understanding of how this is done will be given this week.

  • Understanding background and background subtraction methods 
  • Filtering methods for videos 
  • Temporal templates 
  • Moments in images and video 
  • Hidden Markov model
  • Image segmentation 
  • Clustering methods for segmentation 
  • Mean shift algorithms for segmentation 
  • Segmentation by Graph partitioning 

83D Vision

In contrast to 2D vision, 3D introduces another dimension called the depth dimension. This makes the images that we are looking at more realistic. 3D vision is enabled in a number of ways. We will learn about these methods this week. 

  • Introduction to depth imaging 
  • Depth estimation from stereo imaging
  • Role of geometry in depth imaging 
  • IR based depth estimation 
  • Applications of depth imaging 

9Computer Vision architectures and Frameworks

To make the process of learning computer vision easier a number of libraries exist in the form of frameworks. What these are and how they can be used is what we will be covering this week. 

  • Introduction to Deep learning architectures
  • Alex net 
  • Yolo 
  • VGG Net 
  • GoogleNet 
  • ResNet 
  • Region based CNN
  • Introduction to deep learning frameworks / Libraries for computer vision 
  • Tensorflow
  • Keras 
  • Pytorch 
  • Caffe
  • Constructing a model and training with the frameworks 
  • Inference graph / model generation

10Data collection and Synthetic Data Generation

Computer vision does not gain its learning by looking at an image from one side. Instead, multiple copies of it are generated with the help of algorithms in order to understand an image. This is one of the things that gives Computer Vision its intelligence. 

  • Introduction to Data collection 
  • Data acquisition : Discovery , Augmentation and Generation 
  • Data Labelling : Labeling methods and tools used for labeling 
  • Using Existing Data : Relabeling and Transfer learning methods
  • Introduction to Synthetic data generation 
  • Rendering methods 
  • Procedural randomization 
  • Manual Modeling 
  • 2D and 3D datasets generation 

1112 Capstone Project

In the last two weeks, 5 research papers will be provided to the student. The student can pick the topic of their choice from them. This will be followed up with lectures on that topic. 

  • 3D Object detection from 2D Images – Monocular 3D , Object Detection Leveraging Accurate Proposals and Shape Reconstruction 
  • Human Pose Estimation – Deep Cut : Joint Subset Partition and Labeling for Multi Person Pose Estimation 
  • Panoptic Segmentation 
  • COCO-GAN : Generation by Parts via Conditional Coordinating 
  • Hierarchical Multi-Scale Attention for Semantic Segmentation 


Projects Overview


WHO IS THIS COURSE FOR ?


  • Graduates with a bachelors in ECE, EEE or Mechanical

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Frequently Asked Questions

1Who can take your course?

The course is designed for individuals aspiring to establish a career in Computer Vision. This course is structured to provide a clear and deep understanding of the basics of computer vision with an application-oriented approach. The applications will be channelized predominantly towards Autonomous Vehicles. However, the concepts learnt in the course can be applied in multifarious domains.

2What is included in your course?

The course is essentially 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 by providing exercises and projects that are relevant in the autonomous vehicle industry. The projects would code in Python and the students can familiarize themselves with standard tools used in computer vision such as OpenCV, Tensorflow / Pytorch and other standard Python libraries. There will be a total of 12 projects, one every week, and quiz /challenges to test the understanding of the course materials.

3What will the student gain from your course?

A complete understanding of computer vision, rationale behind the application of methodologies and hands on experience in developing and implementing state-of-the-art computer vision algorithms. The students will gain research and development experience in solving the problems / exercise every week.

4What software skills are you teaching and how well are these tools used in the industry?

TensorFlow, Keras, Python, OpenCV, Machine learning and Deep learning approaches will be taught in this course. The concepts and algorithms learnt in the course is agnostic to the programming language and can be readily applied across different languages including Matlab. Additional skills gained would be best coding practices, hardware and computation conscious implementations.

5What is the real world application for the tools and techniques will you teach in this course?

The real-world applications in this course would include computer vision methodologies and algorithms implemented for Autonomous vehicles. The exercises will follow a sequential approach in developing algorithms for autonomous driving from detecting lanes to developing perception of complex environments.

6Which companies use these techniques and for what?

Computer vision is increasingly being adapted and applied by companies across multiple domains such as banking, surveillance, Automotive, Sports Analytics, Virtual / Augmented reality, Medical Imaging etc. The essence of applying computer vision methodologies is to enable machine vision and provide the perception and logical thinking like that of a human being.

7How is your course going to help me in my path to MS or PhD?

The concepts learnt and the experience gained upon completion of this course will be a foundation in computer vision. Such a foundation is essential for a master’s course focussed in research. The exercises and projects completed in this course will certify the candidate’s knowledge and research experience. Such a background will make a compelling profile for Master’s / PHD study’s application.

8How is this course going to help me get a job?

The course provides hands on experience in developing and implementing computer vision methodologies and algorithms to problems in real life industries. The codes scripted and projects developed can make a great GitHub profile for the candidate. This will provide more visibility and subsequently competitive and lucrative job offers.

9What is the ratio of theory to hands-on practical content?

The course establishes a fine balance between imparting theoretical knowledge and providing hands on experience. 50% of the course will focus on theoretical knowledge and 50% will focus on practical experience. However , we strongly recommend the student to explore more research materials and algorithm implementation to gain enhanced understanding of the subject.


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