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Basics of Computer Vision using Python in Delhi

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

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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 in Delhi

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 in Delhi

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

Learn and Upskill yourself with the Basics of Computer Vision using Python Course in Delhi

Skill-Lync's Computer Vision online course in Delhi is an advanced comprehensive course on Computer Vision using Python, developed to provide industry-relevant skills.

This 12-week online computer vision course in Delhi focuses on Python, OpenCV, TensorFlow, and Keras TensorFlow 2. Delhi attracts a considerable number of students pursuing career-oriented higher education. Fresh graduates steer Delhi, making it one of the most popular locations in India for computer vision courses.

Skill-Lync is one of Delhi's most popular e-learning establishments for studying computer vision using Python. The computer vision sector is still developing and is at the forefront of a wide range of cutting-edge technologies. Students with a thorough knowledge of the computer vision course will have many options to pursue a successful career in this industry. This online computer vision course in Delhi provides students with an understanding of CV and neural networks in TensorFlow and Keras for designing self-driving cars, including an introduction to image recognition tools such as OpenCV and LabVIEW.

In this computer vision online course, Skill-Lync has curated an industry-focused plan. Students who enrol in this computer vision online course in Delhi obtain a thorough cognition of the fundamentals of classical computer vision, including 2D and 3D processing, camera projections, machine learning in vision systems, image processing, edge detection, and picture segmentation.

FAQs About Basics of Computer Vision using Python Course in Delhi

Why Should I choose a Basics of Computer Vision using Python Course in Delhi?

You can master one of the most in-demand industries today by learning one of the best computer vision courses. A learner who takes a computer vision course may produce incredible projects in fields like self-driving vehicles, illness diagnosis, and much more. Computer vision careers include engineer, researcher, developer, programmer, and scientist, to name a few. There are many opportunities, and the pay for these jobs is relatively good.

What are the prerequisites for pursuing a Basics of Computer Vision using Python course in Delhi?

There are so specific prerequisites for pursuing the Basics of Computer Vision using Python course.

What is the course fee of Skill-Lync's Basics of Computer Vision using Python course in Delhi?

At Skill-Lync, this course is available with three payment plans: the basic, the pro, and the premium plan. The basic plan provides you with 2 months of access at INR 7000 per month for three months, the Pro plan provides you with 4 months of access at INR 10,000 per month for three months and the Premium plan provides you lifetime access at INR 15,000 per month for three months.

What are the advantages of pursuing the online computer vision course in Delhi?

Learning computer vision with Python in Delhi at Skill-Lync would offer many benefits to you,

  • Industry-oriented curriculum.
  • Hands-on experience in solving industry projects.
  • Email and forum support from the technical support team to clear your doubts.
  • A certificate of completion.

What are the prospects for a career after completing an online course in computer vision in Delhi?

After completing this course, you can apply for different positions, such as:

  • Computer Vision Engineer
  • Computer Vision AI Practitioner
  • Computer Vision Researcher 

How much can you expect to earn after taking a Computer Vision Online Course in Delhi?

The average yearly income for a Computer Vision Engineer in India is INR 6.8 lakhs. It ranges from INR 2.1 lakhs to INR 17.0 lakhs depending on the experience and expertise.

Instructors profiles

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

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1 industry expert

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

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7 years in the experience range

Instructors with 7 years extensive industry experience.

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Areas of expertise

  • Machine Learning

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