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Basics of Computer Vision using Python in Mumbai

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 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

  • 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 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.

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

Accelerate Your Career with the Basics of Computer Vision Using Python by Skill-Lync in Mumbai

Computer vision deals with acquiring, analyzing, and understanding digital images. It is a rapidly growing area with applications in many fields, including medical imaging, video surveillance, automotive safety, and biometrics. The career opportunities available in this field are enormous and it is wise to choose a course to acquire the right skillsets.

Skill-Lync offers a comprehensive computer vision with Python course in Mumbai that covers all the essential topics in the field. The course is designed to suit the latest trends and technologies, and it is suitable for beginners and experienced professionals.

The computer vision online course curriculum includes image processing, object detection, face recognition, motion estimation, and deep learning. You'll also learn about the various application areas of computer vision, such as medical imaging, video surveillance, and biometrics.

So if you're a computer vision enthusiast and want to get your hands-on experience, Skill-Lync's computer vision course is the perfect option. Enrol now and get started on becoming a computer vision expert!

FAQs About the Basics of Computer Vision Using Python Course in Mumbai

Why should I choose the Basics of Computer Vision Using Python course from Skill-Lync in Mumbai?

Skill-Lync's computer vision course online in Mumbai is designed by leading academicians and experienced industry professionals, making it the best in class. The course covers all the essential topics of Computer Vision and provides a live project to get real-time experience. After completing the course, you can take up any job that involves Computer Vision or its application like Image Processing, Object Detection, etc.

By the end of the course, you'll be able to understand the basics of Computer Vision, apply Machine Learning in Computer Vision, use Python for implementing Computer Vision algorithms, get introduced to Image Processing and Object Detection, and use Cloud Services for deploying Computer Vision applications.

So if you're looking for the best Computer Vision course in Mumbai, then Skill-Lync is the right place for you!

What are the prerequisites for taking up the Computer Vision Online Course from Skill-Lync in Mumbai?

This is a beginner's level course and does not require any prerequisites. However, some knowledge of Python would be beneficial.

What is the program fee for the Basics of Computer Vision Using Python course from Skill-Lync in Mumbai?

Skill-Lync's Computer Vision Using Python course fee is flexible ad is available in three plans. 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 benefits of choosing to do the Computer Vision Online Course from Skill-Lync in Mumbai?

The main benefit of learning computer vision with Python in Mumbai is that you will receive a certificate of completion upon finishing the course and that the course offers a live project where you can learn computer vision with Python and put the theory into practice.

The live project will involve elements of Image Processing, and with its help, you'll get real-time experience using Computer Vision. The course is relevant for engineers from a mechanical or computer science background. Upon completing the course, you'll be able to apply your knowledge and skills in practical settings, giving you an edge in the job market.

For those looking for a career in Computer Vision, this course will be beneficial as it will make you aware of the latest technologies and trends driving computer vision.

What are the career prospects after completing the computer vision online course from Skill-Lync in Mumbai?

After completing the Basics of Computer Vision Using Python course from Skill-Lync in Mumbai, you'll be ready to take up a job in the field. You can look for opportunities as a:

You can alternatively conduct independent research and create products to solve real-world problems.

What is the expected salary range after completing the Basics of Computer Vision Using Python course from Skill-Lync in Mumbai?

The average annual salary for a computer vision developer is 7,00,000 per annum. It can go up to 17,00,000 or even higher depending on the company you work for and your experience.

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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