Certification in Computer Vision

Learn Computer Vision for image representation, feature engineering, image preprocessing, analysis, application & trend

REDEEM

Computer Vision in Python for Beginners (Theory & Projects)

Computer Vision-Become an ace of Computer Vision, Computer Vision for Apps using Python, OpenCV, TensorFlow, etc.

REDEEM

Description

Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. In the CV process, real-world images and videos are captured, processed, and analyzed to allow machines to extract contextual, useful information from the physical world.

Until recently, computer vision functioned in a limited capacity. But due to the recent innovations in artificial intelligence and deep learning, this field has made great leaps. Today, CV surpasses humans in most routine tasks connected with detecting and labeling objects.

The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you to understand the digital imaging process and identify the key application areas of CV. The course is:

· Easy to understand.

· Descriptive.

· Comprehensive.

· Practical with live coding.

· Rich with state of the art and updated knowledge of this field.

Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The Homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.

The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-time) and Smart DVRs (Real-time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.

The course tutorials are divided into 320+ videos along with detailed code notebooks. The videos are available in HD, and the total runtime of the videos is 27 hours+.

Now is the perfect time to learn computer vision. Get started with this best-in-class course without any further delay!

Teaching is our passion:

In this course, we apply the proven learning by doing methodology. We build the interest of learners first. We start from the basics and focus on helping you understand each concept clearly. The explanation of each theoretical concept is followed by practical implementation. We then encourage you to create something new out of your learning.

Our aim is to help you master the basic concepts of CV before moving onward to advanced concepts. The course material includes online videos, course notes, hands-on exercises, project work, quizzes, and handouts. We also offer you learning support. You can approach our team in case of any queries, and we respond in quick time.

Course Content:

The comprehensive course consists of the following topics:

1. Introduction

a. Intro

i. What is computer vision?

2. Image Transformations

a. Introduction to images

i. Image data structure

ii. Color images

iii. Grayscale images

iv. Color spaces

v. Color space transformations in OpenCV

vi. Image segmentation using Color space transformations

b. 2D geometric transformations

i. Scaling

ii. Rotation

iii. Shear

iv. Reflection

v. Translation

vi. Affine transformation

vii. Projective geometry

viii. Affine transformation as a matrix

ix. Application of SVD (Optional)

x. Projective transformation (Homography)

c. Geometric transformation estimation

i. Estimating affine transformation

ii. Estimating Homography

iii. Direct linear transform (DLT)

iv. Building panoramas with manual key-point selection

3. Image Filtering and Morphology

a. Image Filtering

i. Low pass filter

ii. High pass filter

iii. Band pass filter

iv. Image smoothing

v. Image sharpening

vi. Image gradients

vii. Gaussian filter

viii. Derivative of Gaussians

b. Morphology

i. Image Binarization

ii. Image Dilation

iii. Image Erosion

iv. Image Thinning and skeletonization

v. Image Opening and closing

4. Shape Detection

a. Edge Detection

i. Definition of edge

ii. Naïve edge detector

iii. Canny edge detector

1. Efficient gradient computations

2. Non-maxima suppression using gradient directions

3. Multilevel thresholding- hysteresis thresholding

b. Geometric Shape detection

i. RANSAC

ii. Line detection through RANSAC

iii. Multiple lines detection through RANSAC

iv. Circle detection through RANSAC

v. Parametric shape detection through RANSAC

vi. Hough transformation (HT)

vii. Line detection through HT

viii. Multiple lines detection through HT

ix. Circle detection through HT

x. Parametric shape detection through HT

xi. Estimating affine transformation through RANSAC

xii. Non-parametric shapes and generalized Hough transformation

5. Key Point Detection and Matching

a. Corner detection (Key point detection)

i. Defining Corner

ii. Naïve corner detector

iii. Harris corner detector

1. Continuous directions

2. Tayler approximation

3. Structure tensor

4. Variance approximation

5. Multi-scale detection

b. Project: Building automatic panoramas

i. Automatic key point detection

ii. Scale assignment

iii. Rotation assignment

iv. Feature extraction (SIFT)

v. Feature matching

vi. Image stitching

6. Motion

a. Optical Flow, Global Flow

i. Brightness constancy assumption

ii. Linear approximation

iii. Lucas–Kanade method

iv. Global flow

v. Motion segmentation

b. Object Tracking

i. Histogram based tracking

ii. KLT tracker

iii. Multiple object tracking

iv. Trackers comparisons

7. Object detection

a. Classical approaches

i. Sliding window

ii. Scale space

iii. Rotation space

iv. Limitations

b. Deep learning approaches

i. YOLO a case study

8. 3D computer vision

a. 3D reconstruction

i. Two camera setups

ii. Key point matching

iii. Triangulation and structure computation

b. Applications

i. Mocap

ii. 3D Animations

9. Projects

a. Change detection in CCTV cameras (Real-time)

b. Smart DVRs (Real-time)

After completing this course successfully, you will be able to:

· Relate the concepts and theories in computer vision with real-world problems.

· Implement any project from scratch that requires computer vision knowledge.

· Know the theoretical and practical aspects of computer vision concepts.

Who this course is for:

· Learners who are absolute beginners and know nothing about Computer Vision.

· People who want to make smart solutions.

· People who want to learn computer vision with real data.

· People who love to learn theory and then implement it using Python.

· People who want to learn computer vision along with its implementation in realistic projects.

· Data Scientists.

· Machine learning experts.

Unlock the fascinating world of Computer Vision and take your first step towards becoming an expert in this field.

Enroll now and embark on a learning journey that combines theory and hands-on projects. Start mastering Computer Vision today!

List of Keywords:

Image Processing

Deep Learning for Computer Vision

Artificial Intelligence in Computer Vision

Machine Learning Models for Image Analysis

Object Detection and Recognition

Image Filtering and Enhancement

Shape Detection Algorithms

Key Point Detection and Matching Techniques

Optical Flow and Motion Analysis

3D Computer Vision and Reconstruction

Real-time Computer Vision Applications

Change Detection in CCTV

Smart DVR Systems

Computer Vision Projects

Image Segmentation

Feature Extraction in CV

Harris Corner Detector

Scale-Invariant Feature Transform (SIFT)

RANSAC Algorithm

YOLO (You Only Look Once)

3D Reconstruction from Images

Structure from Motion (SfM)

Mocap (Motion Capture)

Computer Vision for 3D Animation

Computer Vision for Data Scientists

Computer Vision for Machine Learning Practitioners

Who this course is for:

• Learners who are absolute beginners and know nothing about Computer Vision.

• People who want to make smart solutions.

• People who want to learn computer vision with real data.

• People who love to learn theory and then implement it using Python.

• People who want to learn computer vision along with its implementation in realistic projects.

• Data Scientists.

• Machine learning experts.

Python for Computer Vision with OpenCV and Deep Learning

Python for Computer Vision with OpenCV and Deep Learning, Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!

• BESTSELLER
• Created by Jose Portilla
•  English
•  English [Auto-generated]
• 13.5 hours on-demand video
• 3 articles
• 2 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Understand basics of NumPy
• Manipulate and open Images with NumPy
• Use OpenCV to work with image files
• Use Python and OpenCV to draw shapes on images and videos
• Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
• Create Color Histograms with OpenCV
• Open and Stream video with Python and OpenCV
• Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
• Create Face Detection Software
• Segment Images with the Watershed Algorithm
• Track Objects in Video
• Use Python and Deep Learning to build image classifiers
• Work with Tensorflow, Keras, and Python to train on your own custom images.

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Autonomous Cars: Deep Learning and Computer Vision in Python

Online Courses Udemy - Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars

• Created by Sundog Education by Frank Kane, Frank Kane, Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard
• Last updated 4/2019
•  English
•  English [Auto-generated]
• 13 hours on-demand video
• 1 article
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Automatically detect lane markings in images
• Detect cars and pedestrians using a trained classifier and with SVM
• Classify traffic signs using Convolutional Neural Networks
• Identify other vehicles in images using template matching
• Build deep neural networks with Tensorflow and Keras
• Analyze and visualize data with Numpy, Pandas, Matplotlib, and Seaborn
• Process image data using OpenCV
• Calibrate cameras in Python, correcting for distortion
• Sharpen and blur images with convolution
• Detect edges in images with Sobel, Laplace, and Canny
• Transform images through translation, rotation, resizing, and perspective transform
• Extract image features with HOG
• Detect object corners with Harris
• Classify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVM
• Classify data with artificial neural networks and deep learning

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Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs, Use Python & Keras to do 25 Projects - Recognition of Faces, Emotions, Age, Gender, Object Detection, Face Aging & more!

• Created by Rajeev Ratan
• Last updated 4/2019
•  English
•  English [Auto-generated]
• 14 hours on-demand video
• 11 articles
• 16 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Learn by completing 26 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits , Simpsons Characters and many more!
• Learn Advanced Deep Learning Computer Vision Techniques such as Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net.
• Understand how Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs with my easy to follow explanations
• Become familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World
• Learn how to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)
• Learn how to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+
• Learn how to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups
• Learn how to use OpenCV with a FREE Optional course with almost 4 hours of video
• Learn how to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application
• Learn how to use TensorFlow's Object Detection API and Create A Custom Object Detector in YOLO
• Learn Facial Recognition with VGGFace

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Video Analytics Using OpenCV and Python Shells

Video Analytics Using OpenCV and Python Shells, Through this training we shall understand and learn how to perform video analysis with OpenCV.

• Created by EDU CBA
• 2 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

Description

OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces. Computer vision applications and technology are blowing up right now! With several apps and industries making amazing use of the technology.

Through this training we shall understand and learn how to perform video analysis with OpenCV.

The training will include the following;

• Object detection
• Color models- HSL model, HSV model, RGB model
• Image loading
• Image thresholding
• Blob detection
• Motion Detection
• Capture video from video
• Capture video from video file
• Background subtraction
• Saving video
• Colorspace based tracking
• Camshift algorithm
• Optical flow based tracking
• Face detection
• Contour based tracking

Who is the target audience?

• Beginner knowledge of Statistics

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