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Basics of Computer Vision using Python in Bangalore

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 Bangalore

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 Bangalore

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 the Basics of Computer Vision Using Python by Skill-Lync in Banglore 

Artificial Intelligence (AI) is the future of the tech industry. The need for AI experts has escalated with increased automation in all business domains. Companies are looking to hire skilled professionals who are well-versed in AI technologies, such as computer vision. In response to this industry demand, Skill-Lync developed a comprehensive Computer Vision with Python course in Bangalore to help students and professionals.

In this 12-week-long computer vision course, you will learn the basics of image processing, edge detection, and image segmentation, which are critical to computer vision. This computer vision online course also familiarises you with cutting-edge tools and technologies like OpenCV, TensorFlow, and Keras TensorFlow 2. The course curriculum also includes industry projects enabling you to have a first-hand experience in the real-time application of computer vision in industries.

Skill-Lync Bangalore has thoughtfully designed the best computer vision course to develop the skills and proficiencies needed to excel in the dynamic tech industry. So, if you are interested in pursuing a career in computer vision, Skill-Lync’s computer vision online course in Bangalore is the best for you. Enrol today.

FAQ’s on Basics of Computer Vision Using Python Course in Banglore

Why should I choose Skill-Lync’s computer vision course in Bangalore?

Skill-Lync offers a comprehensive computer vision course online in Bangalore covering the fundamentals and advanced concepts of computer vision using Python. Besides, the course introduces various technologies, such as OpenCV, TensorFlow, and Keras TensorFlow 2. The course curriculum also includes industry projects to help you gain real-time industry experience.

What are the prerequisites for enrolment in Skill-Lync’s Basics of Computer Vision Using Python course?

Students and professionals from any field with a basic understanding of mathematical concepts and programming fundamentals can enrol for this course.

What is the course fee for the Skill-Lync’s Basics of Computer Vision Using Python course in Bangalore?

Skill-Lync's Basics of Computer Vision using Python course fee is flexible and 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 advantages of Skill-Lync’s computer vision course in Bangalore?

Pursuing a course 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 and merit certificate for the top 5% of scorers.

What are the career prospects after completing Skill-Lync’s computer vision course in Bangalore?

After course completion, you can apply for the following jobs:

  1. Computer Vision Engineer
  2. Computer Vision Solution Architect
  3. CV Test Engineer
  4. Systems Engineer
  5. AI Scientist

What is the expected salary range for Computer Vision Engineers in India, particularly in Bangalore?

The average salary of computer vision engineers in India is estimated to be INR 5.4 LPA. Particularly in Bangalore, computer vision engineers can earn INR 3-10 LPA depending on their skills, knowledge, and experience.

Can you tell me more about Skill-Lync?

Skill-Lync is among India's leading EdTech platforms dedicated to transforming engineering education. We equip young engineers with the latest skill sets and cutting-edge tools in new-age technologies.
The brainchild of two engineers from Chennai, Skill-Lync, is on a mission to bridge the skill gap between aspiring professionals and the industry's demands through job-oriented courses.

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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