Menu

Job Guaranteed

Workshops

Projects

Blogs

Careers

Hire from us


For Business / Universities

Corporate Training

Academic Up-skilling


All Courses

Choose a category

Loading...

All Courses / undefined

All Courses / undefined / undefined

logo

Loading...

FOR BUSINESSES

Corporate Upskilling

FOR Universities

Academic Training

More

Basics of Computer Vision using Python

A comprehensive course on Computer Vision using Python. This course is highly suited for beginners

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies Python, OpenCV, TensorFlow, Keras TensorFlow 2

Book a Free Demo Session

Enter your phone number and book a FREE Demo session on your favourite courses now!
Please enter a valid email
Please enter a valid number

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

  • This course provides the parameters and processes that define computer vision and delve into details like image processing, edge detection and image segmentation.
  • This course provides the basics of classical computer vision, 2d and 3d processing, camera projections, use of machine learning in vision systems

Course Syllabus

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

  • 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 - 02 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 - 03 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 - 04 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 - 05 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 - 06 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 - 07 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 - 08 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 - 09 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

 

Week - 10 Image 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

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects

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.

Real time traffic tracking using matching algorithms

In this project, the students are expected to perform the "matching algorithms" to capture moving pedestrians and vehicles traffic and compare them quantitatively.

Real time video segmentation using machine learning and feature extraction

In this project, students will have to download public road video dataset and design a machine learning based technique to segment and identify specific tasks related to autonomous systems and vehicles using open source and data competitions (can choose between kaggle competitions)

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.

Google Rating
4.6

Basics of Computer Vision using Python

The computer vision online course is quite comprehensive as it covers an array of functions and techniques. Once students enrol in the course, they can easily learn computer vision with Python. The programme begins with some basic concepts and then progresses to the nitty-gritty of the language. Students get a chance to work on a live project where they will get hands-on experience on how computer vision will work with the help of Python.

The computer vision course includes two projects on which the students will be working. The project will consist of fundamentals of Python along with background segmentation and designing of basic techniques that build autonomous systems and vehicles. The computer vision online course will also cover the following aspects:

  • Introduction to Computer Vision.
  • Image Processing.
  • Image Segmentation.
  • Stereo Vision.
  • Camera Models.

The computer vision online course will also include 2D shapes and stereo vision. The computer vision course is very comprehensive and will help in understanding Python and then using it practically in real-life based situations. Skill Lync provides the best computer vision course where you can easily learn computer vision with Python and learn the basics of Python, a multipurpose language. The course fee is Rs.30,000 and will last for 12 weeks.

Who Should Take the Computer Vision Online Course? 

The computer vision course is ideally designed for students who have prior experience handling Algebra and Statistics. Also, students should opt-in for this course and learn computer vision with Python if they have some basic programming skills or have used platforms like Python, Matlab, etc., before. Also, this course will require them to design a lot of functions. Therefore, students should have decent mathematical skills to join this course. Maximising a function, taking derivatives, etc., will be an important part of the computer vision course.

However, the language is a little difficult, and the course has a few prerequisites. An ideal student is expected to understand Statistics, Algebra, etc., before enrolling on the course. Also, an idea of basic programming is a must-have as Python can get tricky. We offer the best computer vision course to help professionals make a career out of the language.

What Will you Learn?

While students learn computer vision with Python, they will touch upon several other aspects once they get enrolled in the computer vision course. The course gives students two projects to work on, which will help them understand how the industry works on computer vision and how Python can be integrated to use computer vision. Some aspects that students will learn are listed below:

  • Introduction to Computer Vision and the history.
  • Image processing and understanding the Pixel neighbourhood.
  • An introduction to edges and gradients and edge detection in 1D and 2D images.
  • Segmenting of images and their features.
  • Morphology and its elements.
  • Object shapes and sizes and the element of the Medial axis.
  • Feature-based and matching and tracking and spotting good and trackable features.
  • Understanding the SURF algorithm and the SIFT algorithm and their alternatives can be brought to use.
  • Registration of Images.
  • Projection of the lens and the camera.

Skills You Will Gain?

Here are some of the skills you will acquire once students start working on Python and implementing it to computer vision:

  • Programming Skills
  • Statistics
  • Linear Algebra
  • Deriving functions
  • Using Computer Vision and working with all kinds of images
  • Plotting of data
  • A sense of how machine learning works
  • Tools like TensorFlow, Kera’s, etc
  • Image Processing

Key Highlights of the Programme 

  • The resources shared with the candidates align with the standard industry practices. Students will get an idea as to how things work practically.
  • Skill-Lync provides certificates to anyone who completes the course. Additionally, a merit certificate will be provided to the top 5% of the course participants.
  • It is a 3-month course, and access to the course material will depend on the amount you pay.
  • Several technical engineers will support the students throughout the course.

Career Opportunities after taking the course 

There are several career opportunities for people who opt-in for the computer vision course. Engineer, researcher, developer, programmer, and scientist are just a few computer vision occupations available. Opportunities abound, and the pay for these positions is quite competitive.

A computer vision online course will make students abreast with the latest technologies and trends driving computer vision. Not only that, but the course also offers a live project where students can learn computer vision with Python and bring the literature to use. With the help of the live project, students will get a real-time experience using Computer Vision. Students can then take up any job that involves Computer Vision or its application like Image Processing, Object Detection, etc.

FAQs on Basics of Computer Vision using Python

  1.  Is Machine Learning an important part of Computer Vision?
    Computer vision involves various algorithms, and hence Machine Learning has become an important part of it.
  2. Will we be provided with some reading material for the computer vision course?
    Yes, a lot of reading material is provided for you to read from and practice.
  3. Will Cloud Services be a part of the computer vision course?
    Yes, but you are not required to be a specialist in the same. An introductory knowledge would be more than enough.
  4. Will I get a certificate post the completion of the course?
    You will get a certificate of completion once you finish the course.
  5. Is the course relevant for engineers?
    Students who come from a mechanical and computer science background can enrol in this course.
  6. Will the project involve the elements of Image Processing?
    Yes, image processing forms a very important part of the live project.
  7. Can I take a trial session before paying for the course?
    Yes, you can book a one-on-one demo before making the payment for the course.
  8. Can Computer Vision be used in Autonomous Vehicles?
    Computer Vision is very actively used in Autonomous Vehicles, and you can take up the course if you are developing new-age vehicles.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

image

1 industry expert

Our instructors are industry experts along with a passion to teach.

image

7 years in the experience range

Instructors with 7 years extensive industry experience.

image

Areas of expertise

  • Machine Learning

Similar Courses

Got more questions?

Talk to our Team Directly

Please fill in your number & an expert from our team will call you shortly.

Please enter a valid email
Please enter a valid number
Try our top engineering courses, projects & workshops today!Book a FREE Demo