Topics
Introduction, challenges
What is Computer Vision and why is it needed?
Computer Vision is a field of Artificial Intelligence that allows computers to “see” and understand digital images and videos, much like the human visual system. It’s about teaching computers to extract meaningful information from visual inputs and then use that information to make decisions or take actions.
Computer Vision is needed because of the massive amount of visual data being generated every day. Manually processing all these images and videos is impossible. Computer vision automates this process, making it faster and more efficient. It also helps us find insights in visual data that humans might miss, and it’s crucial for tasks that are dangerous or repetitive for people.
Briefly describe the scope and some key application areas of Computer Vision.
Computer Vision has a very broad scope and is used in many different fields. Here are some key application areas:
- Healthcare: Analyzing medical images like X-rays and MRIs to help diagnose diseases.
- Autonomous Vehicles: Self-driving cars use it to “see” the road, detect other cars, pedestrians, and traffic signals.
- Manufacturing: Inspecting products on assembly lines to find defects and ensure quality.
- Retail: Tracking inventory, analyzing customer behavior in stores, and even enabling cashier-less checkouts.
- Security: Facial recognition systems and surveillance cameras use it to identify people and detect suspicious activities.
- Agriculture: Monitoring crop health, detecting diseases in plants, and estimating yields.
- Robotics: It is also being used to make robots better at interacting with the world. For example, a robot might use computer vision to identify and grasp objects.
Describe some of the major challenges faced in the field of Computer Vision.
- Variability in the Real World:
- Lighting: Changes in lighting (shadows, glare) make it hard for computers to recognize objects consistently.
- Occlusion: Objects can be partially or fully hidden, making them difficult to identify.
- Viewpoint: The same object looks different from different angles.
- Scale: Objects appear different when they are close up or far away.
- Intra-class Variation: Objects within the same category can look very different. For example, there are many breeds of dogs, and they all look different.
- Data Challenges:
- Need for Labeled Data: Training computer vision systems often requires a huge amount of labeled images, which is time-consuming and expensive to get.
- Data Bias: If the training data isn’t diverse enough, the computer vision system might be biased and perform poorly on certain types of images.
- Computational Challenges:
- Processing Power: Computer vision algorithms can be very computationally demanding.
- Real-time Processing: Many applications, like self-driving cars, need to process images very quickly in real-time.
- Understanding and Interpretation:
- Context: It’s hard for computers to understand the context of a scene and the relationships between objects.
- Common Sense: Computers lack the common-sense knowledge that humans have, which makes it difficult for them to interpret images in the same way that we do.