Essential Computer Vision Skills

Computer vision is a sector of artificial intelligence (AI) that focuses on utilizing neural networks and machine learning (ML) to help teach computers how to extract meaning from images, videos, and other visual inputs. Computer vision essentially enables computers to see, perceive, and monitor visual inputs, replicating human sight. Discover the core competencies of computer vision and gain a deeper understanding of how to apply these skills in practice.

Core competencies in computer vision

Core competencies in computer vision include image processing techniques, computer vision libraries, and machine learning (ML) tasks. You can look more in-depth at these core competencies in the following sections.

Understanding image processing techniques

Image processing refers to the process of performing operations on an image to derive information from it or improve it. Some image processing techniques for ML tasks include edge detection, filtering, boundary detection, and image restoration.

• Edge detection: The process of determining where an edge is in an image and then deriving information from the image based on all of the edges found in the image. 

• Filtering: Altering an image's visual appearance by changing the colors of its pixels. 

• Boundary detection: Determines the boundaries between different objects within an image. 

• Image restoration: If an image has deteriorated or developed damage somehow, you can utilize computer vision techniques to restore the image. 

Familiarity with computer vision libraries

To establish a strong computer-vision skill set, you also want to gain an understanding of computer vision libraries. Some of the most popular computer vision libraries include: 

• OpenCV: This is the most popular computer vision library that enables users to implement facial recognition, object detection, segmentation, and more. 

• Scikit-image: A user-friendly Python library that enables users to classify data, create data sets, detect fraud, and more. 

• TensorFlow: Streamlines the process of creating AI models and implements the deployment of models. 

• Keras: A user-friendly Python library that allows users to build neural networks as well as segment images, implement image classification, cluster images, and more. 

Knowledge of machine learning for vision tasks

Professionals implement machine learning techniques to allow computers to teach themselves about the meaning behind visual data. ML algorithms enable computers and machines to learn autonomously without human intervention. ML and computer vision work together to enable computers to interpret visual data and spot patterns. For example, doctors can use ML and computer vision technology to identify and even diagnose tumors. 

Technical skills

Essential technical skills needed to excel in computer vision include proficiency in various programming techniques and an advanced understanding of neural networks and deep learning. 

Programming proficiency

Proficiency in programming libraries such as Python and C++ will help you implement computer vision techniques. 

• Python: Python provides various image processing libraries you can learn how to implement depending on your project requirements. 

• C++: OpenCV seamlessly integrates with C++ to implement computer vision techniques, such as real-time image processing and “eyesight” for autonomous vehicles. 

Understanding neural networks and deep learning

Understanding neural networks and deep learning can help you implement image processing and better understand the steps to process images. Professionals utilize deep learning algorithms to analyze images because of the technology’s outstanding ability to learn and work with complex patterns. You should be familiar with two types of neural networks: convolutional neural networks (ConvNets or CNNs) and generative adversarial networks (GANs). 

ConvNets consist of three layers: The convolutional layer (CONV), the pooling layer (POOL), and the fully connected layer (FC).

The CONV layer is the core of the ConvNet and implements convolution operation of an image, which means it ensures the outputs the model has created are accurate. POOL refers to the layer that minimizes the computational power to make the data easier to process. Typically the final layer of the ConvNet, the FC layer, connects each input to a set of neurons to process data. The classification of the image occurs in the FC layer. 

ConvNets help ML models break down images into pixels and utilize labels to interpret images while GANs apply learning techniques to help models implement image recognition and distinguish fake images from real images. 

Analytical skills

Data annotation, labeling, and model performance evaluation are analytical skills that will help you gain a deeper understanding of computer vision. 

Data annotation and labeling

Data annotation is an important aspect of computer vision for computers analyzing and processing visual elements. Data annotation implements labeling and categorizing visual data to describe and identify images. This process makes it easier for ML algorithms to interpret and analyze data. 

Evaluating model performance

Model performance evaluation is important in implementing various metrics to determine a model's strengths and weaknesses. Mean average precision (mAP) is a frequently used method of evaluating computer vision models. This process entails taking the average precision (AP) of specific classes within a multi-class classification task. The mAP measures how accurate a model is over various classes. 

How to gain more computer vision skills

If you want to establish a solid foundation for your computer vision skill set, consider earning a bachelor’s degree in computer science or a related subject. You can also enroll in a coding bootcamp to increase your knowledge of programming languages used in computer vision such as C++ and Python. Additionally, because deep learning libraries are an important component of computer vision, you can explore tutorials on the TensorFlow website. PyTorch, another deep learning library, also offers tutorials. You can learn more about monitoring machine learning tasks with a tutorial on Neptun.ai’s website. Online courses are another option for increasing your computer vision skills in machine learning, data structures, and image processing.

Networking with industry professionals 

Growing your network with fellow computer vision professionals will help you better understand this field of AI. Joining groups on social media, such as LinkedIn, will enable you to meet people and gain more knowledge about the field. Networking with industry professionals can kickstart your future career and potentially lead to mentorships or internships within computer vision. 

Develop your computer vision skills on Coursera

Computer vision is immensely beneficial in the field of artificial intelligence, particularly when it comes to training computers to interpret, understand, and analyze visual content. Discover more about machine learning, artificial neural networks, and Python programming on Coursera with Stanford and DeepLearning.AI’s Machine Learning Specialization. You might also consider the IBM AI Engineering Professional Certificate, where you can learn about computer vision, deep learning, and machine learning algorithms.