Unlocking the Potential of Computer Vision Models

Computer vision is an exciting branch of artificial intelligence that empowers machines to “see” the visual world. By leveraging machine learning and deep learning techniques, computer vision models analyze and interpret visual data. As a result, it aids professionals across such fields as health care, environmental science, security, and technology. Continue exploring the potential of computer vision by learning what it is, how you might already see it in action, and how to take your first steps in this field. 

What is computer vision?

As you might guess from the name, computer vision is a field that enables computers to perceive and interpret visual information. This branch of artificial intelligence (AI) involves a combination of algorithms and machine learning techniques to mimic human vision. As a result, computers can analyze images from a range of sources and extract meaningful insights. 

Computer vision tasks

You can use computer vision algorithms for a variety of image analysis techniques, each designed to take on unique challenges. Selecting the right one for your application ensures you design an algorithm optimized for your goals. A few core computer vision tasks include:

• Image classification: Categorizing images into previously defined groups. Models are trained using labeled data sets until they can accurately sort new images into the correct categories.

  • Example: Recognizing specific facial expressions

• Object detection: Identifying specific objects within an image, including the location of the objects.

  • Example: Detecting pedestrians in an autonomous vehicle 

• Image segmentation: Dividing an image into multiple sections and labeling each one.

  • Example: Labeling normal and abnormal cells within a medical image  

Types of computer vision models

While convolutional neural networks are the most popular computer vision models, you can choose to combine them with other models or opt for alternative solutions. Consider three popular designs to decide which is right for you.

1. Convolutional neural networks

One of the most powerful computer vision models is the convolutional neural network (CNN). CNNs are deep learning models designed to process grid-like data, such as images. 

These models consist of multiple layers that work together to find patterns within the image, allowing them to identify objects in the frame. This algorithm is widely used for tasks such as image classification, video analysis, image segmentation, and object detection. It extends its capabilities to fields such as health care, autonomous driving, retail, and social media. 

Read more: 8 Common Types of Neural Networks

2. Deep learning networks

Beyond CNNs, deep learning networks such as recurrent neural networks (RNNs) and generative adversarial networks (GANs) contribute to computer vision tasks. RNNs are particularly effective for analyzing sequential data like text, audio, and video, capturing temporal patterns for applications like video analysis, and generating image captions when combined with CNNs. 

GANs, on the other hand, are most commonly used for image generation. You can use GANs to create novel content, like images, or transform existing ones (such as converting a daytime image to a nighttime scene). This ability to create synthetic images is important for computer vision algorithm development, as it helps to overcome a lack of labeled training data.

3. Feature descriptor models

Feature descriptor models manually extract features (like edges or shapes) to classify images rather than using deep learning methods. Algorithms such as SIFT (scale-invariant feature transform) and HOG (histogram of oriented gradients) identify features within an image that they can use to recognize certain objects. This makes these algorithms particularly useful for object tracking or image classification tasks. 

Applications of computer vision

Computer vision applications span across industries, transforming computers' role in our daily lives. By allowing machines to interpret and analyze video data, we can use computer vision to power innovations that enhance safety and efficiency across professional tasks. Some notable applications gaining popularity include:

Health care

For example, in health care, you can use computer models to analyze medical images such as X-rays, MRIs, and CT scans to enable faster and more accurate diagnoses for patients. By identifying abnormalities that the human eye may miss, computer video aids in the early detection of disease, assists in treatment planning, helps to monitor disease progression, and reduces the workload for medical professionals.

Autonomous vehicles

With self-driving cars, computer vision allows vehicles to perceive road signs and objects on the road, providing the information needed to navigate safely. In many cases, autonomous vehicles have sensors that can perceive necessary information on the road, such as pedestrians, traffic signs, lanes, and objects. Computer vision algorithms interpret this sensor data in real-time, allowing the vehicles to make safe decisions and movements. 

Facial recognition systems

Facial recognition is an application of computer vision that many people use every day. Many smartphones now use “Face ID” to unlock devices and authenticate purchases, allowing users to move through their daily tasks more conveniently. This computer vision algorithm works by reading the geometry of the face in the image and analyzing the distance between facial features. This creates a unique pattern of facial features, allowing the algorithm to identify a specific person. 

Satellite monitoring

Computer vision aids in analyzing satellite images to track global environmental changes. Computer vision algorithms use satellite data to identify specific image features like vegetation, water, and urban areas. The technology provides real-time reports for mineral mapping, climate change tracking, wildlife conservation, and deforestation identification. These algorithms also reduce noise in the data, enhance images, and allow for more accurate analysis. This leads to more effective resource allocation and intervention design.

Preventing misinformation

AI-generated deepfakes are increasingly common, making it difficult for people to discern which images or videos are actually real. Computer vision algorithms may be able to spot minute differences between authentic and inauthentic media, making them an important tool for preventing the spread of false information or propaganda. 

Strengths and limitations of computer vision

While computer vision has exciting applications across industries, it’s not a perfect science. Like any other technological application, it has its strengths and weaknesses. Consider the following when deciding if this algorithm is right for your application. 

Strengths

• Used for a variety of tasks, including object detection, segmentation, feature matching, pattern detection, and edge detection. 

• Can detect nuances missed by the human eye.

• Able to predict future outcomes and trends based on large sets of historical data.

Limitations

• May be resource-intensive due to the need for a high volume of training data.

• Not always accurate, especially with distorted or unclear images.

• Constantly evolving field, meaning certain equipment or applications may become outdated quickly.

Start learning computer vision

It’s a good idea to learn computer vision to build a strong foundation in key technical areas before moving to more complex topics. To start, focus on developing a solid foundation in machine learning and computer programming. Since computer vision is deeply connected to artificial intelligence and deep learning, having a solid grasp of these concepts will help you understand the principles and nuances of computer vision algorithms. 

Once you’re comfortable with basic programming syntax, consider exploring libraries and packages designed for computer vision. Programming languages, such as Python, often have computer vision packages, making it easy for you to experiment with different techniques. To try out different hands-on projects, you can complete guided computer vision projects on a learning platform, such as Coursera.

Keep learning about computer vision on Coursera

Computer vision is an exciting field tied with artificial intelligence and deep learning that allows computers to analyze and interpret vision information. Because of the complexities of this area, you might benefit from completing a structured course offered by a learning university or organization, such as the ones offered on Coursera. Consider completing the First Principles of Computer Vision Specialization by Columbia University. This five-course series is designed for you to complete at your own pace.