Elevate Your Earning Potential as a Computer Vision Engineer with these Courses

Computer vision is expected to be increasingly important in 2023 for several reasons. Listed below are some of the key reasons why computer vision will be important in 2023 and beyond:

• Advancements in Technology
  
  Computer vision technology has been advancing rapidly over the past few years. Significant advancements have been made in the field of deep learning algorithms and the availability of large datasets. In the year 2023, we anticipate witnessing further advancements and an increased prevalence of advancements in computer vision technology, making it more accurate, efficient, and accessible.
   
• Increasing Demand for Automation
  
  As more industries seek to automate their processes, computer vision will be an essential technology for facilitating the ability of machines to perceive and comprehend their surroundings. This will be especially important in the manufacturing, logistics, and transportation industries.
   
• Growth of AI-Powered Devices
   
  With the proliferation of AI-powered devices such as smartphones, smart homes, and wearables, computer vision will be essential for enabling these devices to interact with their surroundings and provide personalized experiences to users.
   
• Emergence of New Applications
   
  As computer vision technology continues to evolve, new applications will emerge in fields such as healthcare, entertainment, and education. For example, computer vision could be used to assist with medical diagnoses, create more immersive gaming experiences, and enable personalized learning.

Computer vision has revolutionized the Computer science industry by its immense contribution and application in the modern world. Let us understand the working of Computer vision and the steps involved in building a computer vision application. Computer Vision works by processing digital images or videos using algorithms and techniques that enable machines to interpret and understand visual data.

The process typically involves the following steps:

• Acquisition
   
  The first step is to acquire the visual data, which may manifest in various forms, such as images or videos captured by cameras, sensors, or other devices.
   
• Pre-processing
   
  The acquired data is then pre-processed to remove any noise or distortions and to enhance the quality of the images or videos.
   
• Feature Extraction
   
  Next, the computer vision system extracts features from the pre-processed data, such as edges, shapes, textures, and colors. This is done using various techniques such as edge detection, segmentation, and feature extraction algorithms.
   
• Object Recognition
   
  Once the features are extracted, the computer vision system uses machine learning algorithms to identify and classify the objects in the image or video. This involves comparing the extracted features to a predefined set of features or patterns corresponding to known objects.
   
• Interpretation
   
  Finally, the computer vision system interprets the results of the object recognition process and makes decisions based on the information obtained. For example, it may identify potential hazards in a scene or track the movement of objects in a video.

Computer vision technology has a wide range of real-world applications in various industries. Listed below are some examples of how computer vision is being used today:

• Autonomous Vehicles
  
  Computer vision is essential for enabling autonomous vehicles to perceive and understand their surroundings, allowing them to navigate safely on the road.
   
• Security and Surveillance
   
  Computer vision technology in security and surveillance systems is used to detect and track suspicious activity and identify individuals.
   
• Healthcare
   
  Computer vision is being used in healthcare to analyze medical images and assist in medical diagnoses, such as detecting early signs of cancer.
   
• Manufacturing
   
  Computer vision is used in manufacturing to inspect products for defects and ensure quality control.
   
• Augmented and Virtual Reality
   
  Computer vision is essential for creating immersive augmented and virtual reality experiences, enabling devices to track the position and movement of the user in real-time.
   
• Retail
   
  Computer vision technology is being used in retail to provide personalized shopping experiences, such as analyzing facial expressions to detect emotions and preferences.
   
• Agriculture
   
  Computer vision is used in agriculture to analyze crops and soil conditions, allowing farmers to optimize crop yields and reduce waste.
   
• Robotics
   
  Computer vision in robotics is used to enable machines to perceive and understand their environment, allowing them to perform tasks that require human-like perception and dexterity.

One of the recent applications of computer vision in India is done by the Ministry of Road Transport and Highways by providing FastTag to vehicles that help to deduct the toll amount directly from the commuters' wallet by scanning the FastTag at the toll booth. This helped in reducing the time and traffic congestion at toll booths and facilitated free-flowing traffic.

A computer vision engineer is a professional who specializes in developing computer vision systems and applications. Computer vision entails instructing computers to analyze visual data extracted from their environment, including images, photographs, videos, and 3D scans. Computer vision engineers use machine learning algorithms, image processing techniques, and other tools with the aim of enabling computers to discern patterns and make informed decisions utilizing visual data.

To become a computer vision engineer, you typically need a degree in computer science, electrical engineering, or a related field. You should also have experience with computer vision techniques and tools, such as OpenCV, TensorFlow, or PyTorch. Additionally, having a strong background in mathematics, such as linear algebra and calculus, can help understand the underlying principles of computer vision.

The roles and responsibilities of a computer vision engineer can fluctuate depending on the particular role and from company to company. However, here are some common responsibilities:

• Develop and Implement Computer Vision Algorithms
   
  A computer vision engineer should have a deep understanding of computer vision algorithms and techniques and be able to develop and implement these algorithms to solve real-world problems.
   
• Design and Build Computer Vision Systems
   
  A computer vision engineer should be able to design and build computer vision systems from scratch, using programming languages like Python or C++ and frameworks like TensorFlow or PyTorch.
   
• Develop and Train Machine Learning Models
   
  A computer vision engineer should be proficient in developing and training machine learning models for image classification, object detection, and other computer vision tasks.
   
• Analyze and Improve Existing Systems
   
  A computer vision engineer should be able to analyze existing computer vision systems, identify their weaknesses and potential improvements, and implement changes accordingly.
   
• Collaborate with Cross-functional Teams
   
  A computer vision engineer should be able to work in collaboration with cross-functional teams, including software developers, product managers, and business stakeholders.
   
• Stay up-to-date with the Latest Developments
   
  A computer vision engineer should keep up with the latest developments in computer vision research and technology, attend conferences and workshops, and read relevant papers.
   
• Document and Present Findings
   
  Computer vision engineers should be able to document their work and present their findings to stakeholders and colleagues.

As a Computer Vision Engineer, gaining knowledge of Computer Vision Theory to develop and implement computer vision systems and applications is important. Here are five technical skills that are essential for computer vision engineers:

• Programming Skills
   
  You should have a strong programming background, with proficiency in languages such as Python, C++, or Java. You should be comfortable with software development tools such as Git and Jupyter Notebook and IDEs like Visual Studio or PyCharm.
   
• Machine Learning
   
  Machine learning is a critical component of computer vision. You should be familiar with common prominent machine learning methodologies, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and deep learning algorithms.
   
• Image and Video Processing
  
  You should have experience with image and video processing techniques, such as feature extraction, segmentation, and object detection. Familiarity with computer vision libraries like OpenCV or sci-kit-image is also essential.
   
• Optimization Techniques
   
  Computer vision systems often require real-time processing and optimization to improve their accuracy and performance. You should be familiar with optimization techniques such as parallel computing, distributed systems, and hardware acceleration.
   
• Data Analysis and Statistics
   
  You should have a strong understanding of data analysis and statistics, as computer vision systems often work with large datasets. You should be familiar with data preprocessing techniques, data cleaning, and statistical analysis methods such as hypothesis testing and data visualization.

These are just a few of the technical skills essential for a computer vision engineer. To excel in this field, you should also have good problem-solving skills, the ability to work autonomously as well as collaborate effectively within a team setting, and a willingness to stay current with the latest research and industry trends.

To become a computer vision engineer, it is imperative to possess a robust foundation in computer science, mathematics, and machine learning. Careervira provides the best computer vision courses that help in skill building. Here, we will provide a list of courses that you can pursue to become a computer vision engineer:
 

Learning Python

• Python Fundamentals by Datacamp
   
  This Learning Path consists of 5 Courses. If you are a novice and wish to be a pro of Python Programming and have a fair knowledge of how to code for Data Science applications using Python, then this is the perfect package for you. You will start by learning Basic Level Coding in Python and gradually move towards Intermediate Level. Once you have a good hold in Python Programming, you can move forward with Courses No 3,4 and 5 to build upon your Python programming knowledge and gain decent knowledge of using the Data Science toolbox in Python.
   
• Python Fundamentals by Pluralsight
  
  This is the perfect course to get you started and build up your knowledge in Python Programming.
   

Linear Algebra

• Linear Algebra for Data Science in Python by O’Reilly
   
  For developing a Computer vision model/algorithm, you need to have a good grasp of Linear Algebra principles such as linear transformation, matrices, matrix multiplication, scalers, vectors, dot products, etc. These are the concepts that will be used in every computer vision application. This course will help you gain a good knowledge of these concepts.
   
• Mathematics for Machine Learning: Linear Algebra by Coursera
   
  This course will introduce you to Jupyter Notebooks and code blocks in Python. These will not be long and will focus on the concepts. They will also help you if your code is new. This course will help you to understand vectors and matrices. This course aims to facilitate the bridging of the gap between machine learning and linear algebra.
   

Image Processing

Image processing involves the manipulation and alteration of digital images using mathematical algorithms and techniques to improve their quality, enhance their features, or extract useful information from them. Here are a couple of courses that you can pursue to learn image processing.

• Computer Vision and Image Processing Fundamentals by edX
   
  This is an intro-level course that will teach you about computer vision and the various applications it has across many industries. This course is designed to provide you with the knowledge and skills necessary to effectively utilize Python, Watson AI, OpenCV, and OpenCV for image processing and interaction with image classification models. Your own image classifiers will be built, trained, and tested.
   
• Image Processing with Python by Coursera
   
  This tutorial will show you how to create a Python program that reads from a list and modifies the size and appearance of the images. It will help you gain knowledge of how to read, write images, resize, blur, sharpen images and also how to batch process images.
   

Deep Learning

Computer vision is a part of machine learning that largely uses deep learning models like CNN, RNN, and ANN, just to name a few. You will need to have the conceptual and technical know-how of machine learning algorithms to classify images or detect objects. Hence, it is first necessary to understand the basics of Deep learning before jumping onto these advanced algorithms and techniques. 

• Introduction to Deep Learning in Python by DataCamp
   
  This is a free course that you can use to your advantage for building up fundamental knowledge of Deep Learning using Python. In the realm of Deep Learning, the current pool of trainers is quite limited, which is as good as Andrew Ng. He worked for tech giants like Google and Baidu. 
   
• Deep Learning Specialization by Coursera
   
  This is a course on Deep Learning that will give you immense exposure to the applications of various Computer vision algorithms and libraries like Tensorflow, CNN, RNN, Keras etc., along with Natural Language Processing (NLP) with real-life examples. You will be developing real-world applications with hands-on learning. After completing this course, you will be confident with Computer vision technology and ready to explore more on the industry on your own and explore Computer Vision Engineer jobs to land your dream role. 
   
• Deep Learning Nanodegree Program by Udacity
   
  This course is offered by Udacity to help learners to build and apply neural networks and deploy models. The professors associated with this course have degrees from renowned institutions and offer hands-on training to the learners.

The employment prospects for computer vision engineers are exceedingly favorable in 2023 and beyond. The demand for computer vision engineers has been steadily increasing over the past several years, driven by the growth of industries such as autonomous vehicles, robotics, and augmented reality. As these industries continue to expand and develop, the demand for computer vision engineers is expected to continue to grow.

As per the US Bureau of Labor Statistics, employment prospects of computer and information research scientists, which includes computer vision engineers, are projected to grow at 21% from 2021 to 2031, surpassing the average growth rate for all professions. This field exhibits notably accelerated progression. Additionally, a report from MarketsandMarkets predicts that the global AI in Computer Vision market will grow at a compound annual growth rate (CAGR) of 21.5% from 2023 to 2028.

Speaking of Job Opening in leading economies and Tech Hubs around the world, here are the opening for Computer vision for these leading economies:

United States

The United States alone has more than 66,000 Job Openings in the Computer Vision Industry, thanks to various tech giants and a large number of tech companies that are constantly in the process of developing and researching in this field, being one of the most advanced countries in terms of technology, the United States surely has a huge scope for computer vision professionals.

India

India is picking up its pace in the application of Deep Learning and Computer Vision, and as a result of this, there are more than 5,000+ job openings for Deep Learning and Computer Vision professionals. This number is expected to increase rapidly in the coming years as India is the world's largest exporter of Software.

UK

The UK has one of the leading Computer Science Industries in Europe. The Computer Vision/ Deep Learning industry currently has over 3000+ job openings. 

Job opportunities and openings in Computer Vision Industry are expected to increase rapidly in the coming times. Some of the industries that are expected to drive demand for computer vision engineers in 2023 and beyond include

• Autonomous Vehicles
  
  The development of self-driving cars and trucks is expected to create a significant demand for computer vision engineers who can design and develop systems that can interpret and respond to visual data from the road.
   
• Robotics
   
  As robots become more common in industries such as manufacturing, healthcare, and logistics, the demand for computer vision engineers who can design and develop vision-based systems that enable robots to navigate and interact with their environment is expected to grow.
   
• Healthcare
   
  Computer vision systems are increasingly being used in healthcare applications such as medical imaging and diagnostics. As the healthcare industry continues to adopt these technologies, the demand for computer vision engineers who can design and develop these systems is expected to grow.
   
• Augmented Reality and Virtual Reality
   
  The expansion of augmented reality (AR) and virtual reality (VR) technologies is expected to create demand for computer vision engineers who can design and develop systems that enable these technologies to recognize and respond to real-world environments.

Computer Vision Engineer is a highly skilled profession that requires deep knowledge and technical know-how of various complex Computer Science concepts along with good hands-on Programming Language. Due to its complexity and technical depth required, Computer Vision engineers attract one of the best packages in the I.T Industry.

Here are the salary insights of a Computer Vision Engineer:

USA

The remuneration received by a Computer Vision Engineer in the USA for a fresher is around $124,000, higher than other roles for freshers in the IT Industry. As for experienced professionals, the average salary can go up to more than $214,000.  Refer to the image below.
 

India

In India, Tech Hubs like Bengaluru offers a handsome package to Computer Vision Engineers right from the start of their career. The average salary for freshers in the computer vision field is ₹8 LPA, and as per experience, it can go to an average of above ₹22 LPA and well above that.
 

UK 

The UK has one of the most advanced industries in IT Industries in entire Europe, due to which it also provides a lucrative package.  A fresher in Computer Vision can earn, on average, about £37K, and a mid-level engineer will earn around £50K, which can go up to £76K with experience.

Computer Vision is a hot field in the IT Industry and will definitely be a buzz for years to come. With the advancement of Technologies and Artificial Intelligence, more and more organizations will jump into Computer Vision, which will generate more job openings and demands for Computer Vision engineers. Also, a professional with knowledge of Computer Vision can navigate to other related fields, like Deep Learning, Machine Learning and other areas of Artificial Intelligence, which will give them more exposure to the field and provide them with a promising career with a better pay scale. 

I trust that this article has provided you with a comprehensive understanding of the opportunities that computer vision has and how you can build your career on it with the help of the courses highlighted here. Wishing you a successful career and happy learning.