Introduction

In the era of artificial intelligence, the world has witnessed fascinating advancements with immense automation capabilities. Among several of the sub-branches of artificial intelligence (AI), computer vision is gaining popularity across industries. Computer vision enables computers and systems to procure crucial information from digital images, videos, and other visual inputs.

The systems are capable of taking action and providing recommendations based on the acquired information. While AI capabilities let a system think, computer vision allows the system to see and interpret its observations.

Computer vision was based on inspiration from human vision and its ability to see and understand numerous factors within the environment and differentiate with the utmost ease. Therefore, computer vision machines are trained to automatically perform such functions from output fed in from cameras, data with powerful algorithms.

Today, computer vision is applicable in sports, business, automotive industry, robotics, manufacturing, energy, and healthcare industries.

With a growing demand for state-of-the-art technologies, computer vision has gained a strong foothold in the market, with an expected growth rate of $48.6 billion by 2022.

The staggering growth has led to a vast range of viable career opportunities in the job industry. Today, computer vision professionals and AI experts can relish a lucrative job opportunity with challenging opportunities and high job satisfaction, thereby providing a tremendous career boost in a growing field.

1. Computer Vision Basics by The State University of New York – Coursera

Computer Vision Basics by The State University of New York – Coursera

The course is focused on beginners and is available on the Coursera platform. It is essential for people who are starting or are looking for a refresher course for the basics of computer vision.

The prerequisite of the course includes necessary programming skills and familiarity with MATLAB. Additionally, an understanding of linear algebra, essential calculus, and probability is required.

At the end of the course, the learners can have a clearer understanding of computer vision and work with computer vision projects. Also, the learners will know the goals of computer vision, digital image processing, critical applications in the field of computer vision, and the essential mathematical techniques involved in computer vision tasks. The course curriculum includes:

  • Computer Vision Overview
  • Color, Light, and Image Formation
  • Low-Mid and High-Level Vision
  • Mathematics for Computer Vision

Instructor: RadhaKrishna Dasari

Level: Beginner/Intermediate

Video Lectures: NA

User Review: 4.2/5

Price: Free Enrollment (Additional Charges May Apply for Certificate)

2. TensorFlow: Advanced Techniques Specialization – Coursera

TensorFlow- Advanced Techniques Specialization – Coursera

The specialization course is 5-month duration offered by the well-known institute, DeepLearning.AI, on the Coursera platform.

The course emphasizes building the advanced concepts on TensorFlow, non-sequential model types, and its optimization in various environments. The learners can understand the use of multiprocessors and chip types that are involved in such tasks.

Additionally, the learners will have a clear understanding of computer vision scenarios such as object detection, image segmentation, interpreting convolutions, and deep generative learning with GANs, VAEs, and autoencoding.  Also, the concepts of custom loss functions and layers, gradient tape, and autograph are essentials that are explored in-depth.  The course modules are:

  • Custom Models, Layers, and Loss Function with TensorFlow
  • Custom and Distributed Training with TensorFlow
  • Advanced Computer Vision
  • Generative Deep Learning with TensorFlow

Instructor: Laurence Moroney and Eddy Shyu

Level: Intermediate/Advanced

Video Lectures: NA

User Review: 4.8/5

Price: Free Enrollment (Additional Charges May Apply for Certificate)

3. Computer Vision Fundamentals with Watson and OpenCV – edX

Computer Vision Fundamentals with Watson and OpenCV – edX

IBM offers the course on the edX platform. The course introduces the fundamentals of computer vision and its applications across industries.

The learners will gain mastery over Python, Watson AI, and OpenCV used for image processing, interacting with images, and classification purposes. Besides, the learners will understand how to build, train and test their custom image classifiers for targeting computer vision problems.

The course offers hands-on experience through multiple labs and exercises. At the end of the course, the learners will build their computer vision web app and learn to deploy it in the cloud environment. The prerequisite of the course includes the basic knowledge of Python programming skills.

Instructor: Yi Leng Yao, Sacchit Chadha, and Nayef Abou Tayoun

Level: Introductory

Video Lectures: NA

User Review: 4.5/5

Price: Free Enrollment (Additional $99 for Certification)

4. Applied Artificial Intelligence: Computer Vision and Image Analysis – FutureLearn

Applied Artificial Intelligence- Computer Vision and Image Analysis – FutureLearn

The course is available on the Futurelearn platform. It is offered by CloudSwyft and is accredited by Microsoft. The course is a part of the Advanced and Applied AI on Microsoft Azure Expert Track. However, it can be enrolled separately.

The topics covered in the course are current image segmentation techniques, image features, traditional image processing techniques, object classification and detection, and in-depth image segmentation.

By the end of the course, the learners will be able to master image analysis techniques with the likes of edge detection, watershed and distance transformation, and k-means clustering.

The learners will compare deep learning object classification techniques, implement algorithms using the OpenCV library, and apply Microsoft ResNet and deep CNN to perform object classification using the Microsoft Cognitive toolkit.

Instructor: Industry Professionals

Level: Introductory

Video Lectures: NA

User Review: NA

Price: 7-day Free Trial ($39/Month after Trial Period Ends)

5. Deep Learning: Advanced Computer Vision (GANs, SSD, and more) – Udemy

Deep Learning- Advanced Computer Vision (GANs, SSD, and more) – Udemy

The course is an advanced course on computer vision that is available on Udemy. The prerequisite of the course includes the knowledge of building and training computational models using CNN, proficiency with Python, and understanding the fundamental theoretical aspects of convolution and neural networks.

The learners can understand how to use CNN for object detection, classification, and identification of the location of the object in the image and prediction of image labels.

Object localization concepts are explored in the course as it is one of the essential steps in object detection—also, the algorithms such as SSD and neural style transfer. Finally, the concepts of GAN and its uses are covered in-depth.

At the end of the course, the learners will have a thorough understanding of transfer learning, advanced computer vision algorithms, GANs, convolutional neural networks such as VCG, ResNet, and Inception, class activation maps applying for neural style transfer. The course modules are:

  • Introduction
  • Machine Learning Basics
  • Artificial Neural Networks
  • CNN
  • VCG and Transfer Learning
  • ResNet and Inception
  • Object Detection (RetinaNet)
  • Neural Style Transfer
  • Class Activation Maps
  • GANs
  • Object Localization Project
  • Keras and TensorFlow
  • Setting up the Environment
  • Python
  • Learning Strategies
  • Conclusion

Instructor: Lazy Programmers Inc.

Level: Advanced

Video Lectures: 119 Video Lectures

User Review: 4.7/5

Price: $6.2 Approximately (Price Varies According to the Region)

6. Python for Computer Vision with OpenCV and Deep Learning – Udemy

Python for Computer Vision with OpenCV and Deep Learning – Udemy

The course is available on Udemy. In this course, the learners will understand the Python libraries and image manipulation using NumPy. The course further explores the OpenCV library to work with images.

Furthermore, the learners will gain insights into color effects, color mappings, blending, gradients, and threshold concepts. Besides, the learners will have a thorough understanding of object tracking, facial detection, and object detection using various techniques.

All the advanced, in-depth learning topics that are essential for computer vision are explored in-depth. Finally, the learners will understand how to work with deep learning networks for computer vision such as YOLO. The course modules are:

  • NumPy
  • Images with NumPy
  • Image and Video Basics with NumPy
  • Color Mappings
  • Blending and Pasting Images
  • Image Thresholding
  • Blurring and Smoothing
  • Morphological Operators
  • Gradients
  • Histograms
  • Streaming video with OpenCV
  • Object Detection
  • Template Matching
  • Corner, Edge, and Grid Detection
  • Contour Detection
  • Feature Matching
  • Watershed Algorithm
  • Face Detection
  • Object Tracking
  • Optical Flow
  • Deep Learning with Keras
  • Keras and Convolutional Networks
  • Customized Deep Learning Networks
  • State of the Art YOLO Networks

Instructor: Jose Portilla

Level: Beginner/Intermediate

Video Lectures: 92 Video Lectures

User Review: 4.6/5

Price: $6.2 Approximately (Price Varies According to the Region)

Computer Vision Challenges

Computer vision has many benefits, and the rapid development in this field ensures smarter systems in the future. However, as a professional in the field, one must be aware of the possible obstacles on its way to becoming a leading technology.

Logical Reasoning

The lack of reasoning capabilities in a computer vision model makes it difficult for complicated tasks when the experts are looking for possible attributes in an image or video. Also, the possibility of finding a way to train millions of parameters without making the model complex is yet another challenge.

Ethics

Face recognition is prohibited due to the possibility of misuse in some countries. Besides, if powerful computer vision models are used for surveillance without legal approval, it poses a severe threat to individuals’ privacy.

Lack of Trustable Content

As deep learning and computer vision techniques allow the building of fake datasets for training purposes, the technology can be threatening if misused. Such fake outputs are unrecognizable for the human eye. Therefore fake images, videos, or text content may result in illegal activities.

Data Leakage

Computer vision models require a large volume of data to train. Also, most people are unaware that AI models are prone to data leakage if not trained appropriately. However, unauthorized access to sensitive data can be a severe threat with the increasing number of cybersecurity attacks and breaches.

Conclusion

Computer vision is the technology of the future. From fraud detection to object detection to motion estimation, computer vision is everywhere. Today, computer vision can become a useful tool for security surveillance, e-commerce, virtual assistants, the fashion industry, sports, and many more.

From assisting in appropriate referee-based decisions to healthcare, the potential of computer vision is endless.

Computer vision has gained popularity due to its implication in customer-oriented products, business applications, manufacturing, and assistance. Technology has been playing a significant role in providing road safety through driving assistance systems.

The data that we produce daily has become image-centric data, be it personal usage, business-related images, or healthcare diagnosis. Thus, computer vision has played a crucial role in bridging the gap of image analysis through different angles, motions, and objects of varied shapes and sizes.

The recreation of images from deformed images, identifying objects from blurry images, and video analysis are crucial contributions to this technology.

With such potential, the demand for computer vision experts is increasing, while there is an imbalance in the demand and availability of experts in the industry. To establish a career in computer vision, it is highly essential to stay upgraded with the recent trends and technologies in the market.

There is no scope of slowing down in the fast-paced industry, and constant upskilling will ensure stable growth in the industry.  While technical skills are essential, one must improve analytical and critical thinking capabilities and excellent interpersonal skills.

Finally, it is imperative to opt for certified courses promptly to stay updated; however, industrial Certification from tech giants offers more value in the industry.

With constant salary hikes in this profession, it is the opportune moment for all the aspirants and professionals to dive into a future-proof career.

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Kaushik Das

Kaushik Das

Kaushik is an artificial intelligence researcher and a data scientist with expertise in medical image processing, intelligent automation, computer vision, and big data engineering—a technical and a scientific writer by passion. He is also passionate about sports, photography, traveling, and exploring new technologies.

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