What you need to know as a computer vision engineer part 0

Photo by Alexandru Zdrobău on Unsplash

In this post i talk about what you need to know about computer vision to become a suitable engineer :

A computer vision engineer is a professional who uses computer vision techniques to analyze and interpret visual data from cameras and sensors. They work with hardware and software to develop applications that can recognize, track, and classify objects and events in real-time. Their responsibilities include designing and implementing algorithms, building machine learning models, collaborating with other engineers, and keeping up-to-date with the latest research in the field.

some examples of how computer vision changes the world around us :

• Medical imaging to enhance and analyze images
• Video surveillance to detect and track people/objects
• Autonomous vehicles to detect objects and make decisions
• Augmented reality to overlay virtual objects onto the real world
• Robotics to perceive and interact with the environment

Computer vision is not like many other engineering that doesn’t change in decades ,it’s evolving every day and as an engineer you should try to keep yourself up to date !

for example recently Facebook released SAM (segment anything model) which completely changes how we look at segmentation in the future . I talk about it and it’s implementation here and here .

1. Image Processing: Image processing is a fundamental skill for computer vision engineers. It involves a range of techniques to improve and analyze images, including filtering, smoothing, and edge detection. Image segmentation is the process of dividing an image into multiple segments or regions, while feature extraction is used to extract meaningful information from an image, such as texture, color, and shape. Object recognition involves identifying objects within an image or video stream.
2. Mathematics and Statistics: Mathematics and statistics are foundational skills for computer vision. Linear algebra is used to represent and manipulate images as matrices, while calculus is used to optimize functions and perform numerical analysis. Probability theory and statistics are used to model uncertainty and analyze data.
3. Programming Languages: A computer vision engineer needs to be proficient in programming languages such as Python, C++, and MATLAB. They also need to be familiar with computer vision frameworks such as OpenCV, TensorFlow, and PyTorch. These tools enable engineers to develop and implement algorithms and models for computer vision.
4. Machine Learning: Machine learning is a critical skill for computer vision engineers. Engineers must understand the theory and implementation of supervised and unsupervised learning, deep learning, and reinforcement learning. These techniques are used to train computer vision models to recognize and classify objects, detect and track motion, and perform other tasks.
5. Data Structures and Algorithms: Strong knowledge of data structures and algorithms is essential for computer vision engineers. They must be able to design and implement efficient algorithms and data structures for large-scale computer vision tasks. These include data structures for image representation and algorithms for image processing, feature extraction, and object recognition.
6. Computer Vision Techniques: A computer vision engineer must be knowledgeable about state-of-the-art computer vision techniques. These include object detection, tracking, segmentation, and 3D reconstruction. These techniques are used to develop computer vision systems for a range of applications, from surveillance and security to autonomous vehicles and robotics.
7. Hardware and Software Knowledge: A computer vision engineer must be familiar with the hardware components, sensors, and cameras used in computer vision systems. They must also be knowledgeable about operating systems, network protocols, and database management systems.
8. Communication and Collaboration Skills: A computer vision engineer must be able to work in a team, communicate effectively with stakeholders, and present complex technical concepts to non-technical audiences. They must be able to collaborate with software developers, hardware engineers, and project managers to deliver high-quality computer vision systems.

so in a series of posts I try to explain each part .

lets start with Image Processing .