Computer Vision in artificial intelligence is a field that has gained tremendous advancement with the increased accuracy rates of object identification and classification. Computer Vision, which is in simple terms, trains computers to understand and interpret the visual world. It is a science that combines theory and technology to build artificial systems for obtaining information from images or multi-dimensional data. There are many purposes for it. A significant application is moving a robot through some environment. Computer Vision in artificial intelligence provides a robot with a vision sensor and information about the surrounding environment. The concept of Computer Vision is not new, and it was first commercially used for recognizing handwritten texts using optical character recognition.

Today advanced mobile technology, affordable computing power, hardware related to Computer Vision analysis, and several neural networking algorithms have opened a new sphere for computer vision in artificial intelligence.

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What is Computer Vision in artificial intelligence?

You can find multiple definitions of Computer Vision AI. As per the definition provided by Prof. Fei-Fei Li, computer vision is “a subset of mainstream artificial intelligence that deals with the science of making computers or machines visually enabled, i.e., they can analyze and understand an image.”  Computer Vision emulates human vision using digital images.

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How does computer vision work - steps

Computer Vision in artificial intelligence follows three consecutive processes that execute one after another.

- Image acquisition
- Image processing
- Image analysis and understanding

Image acquisition

Image acquisition translates the analog images into digital images, which means it transforms a normal image into binary data (combinations of zeros and ones). Different tools like webcams, embedded cameras, digital compact cameras, laser range finders, etc., are used to build such datasets. However, the process does not end here. Often these raw data are post-processed to achieve more efficiency for the next step.

Image processing

Some low-level processing is performed on digital images using advanced applied mathematics algorithms or AI image processing algorithms in this step. Information related to the geometric elements of the objects in an image is extracted at this step, which includes:

- Edge detection
- Segmentation
- Classification
- Feature detection and matching

Image analysis and understanding

This is the final step of the Computer Vision, where high-level algorithms are applied to the processed data to perform the data's actual analysis. This also helps in decision making. Some of the analysis performed in this step is –

- 3D scene mapping

- Object recognition

- Object tracking

Deep learning for Computer vision

Deep Learning has a big impact on computer vision, and it started with AlexNet that describes ImageNet classification with Deep convolutional Networks(CNN). Following computer vision problems have been addressed using Deep learning.

Image Classification/recognition –

So far, we have an overview of the Computer Vision where we see it takes input as an image and provides an output that could be categorized in some specific object category.

Input: An image with a single object, such as a photograph.

Output: A class label (e.g. one or more integers that are mapped to class labels).

However, it is the most complicated thing in the Computer Vision part because an image can be classified into more than one category. ImageNet is the most popular dataset used in this context, composed of millions of classified images utilized in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Image classification assigns a class label to an image or predicts the class of one object. 

Deep learning uses the concept of photo classification datasets, which follows CIFAR. Deep learning helps to create a convolutional neural network that recognizes images with more accuracy. This network works similarly to neurons in the human brain.

However, before Deep learning is applied for image classification, we need to perform supervised learning on the computers. It is nothing but feeds object patterns, for example, more images of dogs so that the computer can build its own cognition.

Image Classification With Localization

Image classification with localization assigns a class label to an image and the location of the object in the image. It uses a bounding box around the object. However, it is more challenging as it may involve adding bounding boxes around multiple types of the same object in the image. The ILSVRC2016 Dataset for image classification with localization is popular.

Object Detection

Object detection goes one step further than object classification. In this case, multiple instances of the same class or different classes are assigned to the same image. Object detection is a more challenging task as there could be multiple objects in different types' images.

Object segmentation, or semantic segmentation

Image segmentation splits up an image into segments. Though Object detection is also sometimes referred to as object segmentation. However, Object segmentation does not involve bounding boxes. Instead, it identifies specific pixels in the image which belong to the object.

Computer vision Applications

Computer Vision is an emerging field in artificial intelligence which has many applications in different fields like

-Robotics

-Human-computer interaction and visualization

Few of the applications are -

- Augmented reality

- Motion recognition

- Domestic/service robots

- Autonomous cars

- Image restoration such as denoising

- Recognizing handwriting text and drawings.

Challenges in Computer Vision

Being a part of artificial intelligence, Computer Vision in artificial intelligence is data-dependent. So, algorithms related to Computer Vision can face different challenges related to the nature of the data. It can receive data which could be –

- Incomplete

- Noisy

- Real-time

- May have limited resources like memory or power.

So, to address this algorithm related issues they need to be more robust and efficient.

Final thought

Computer Vision is used across the industries and enhancing the consumer experience with reduced cost and increased security. Computer Vision's market is progressing as fast as its capacities and is estimated to reach $26.2 billion by 2025. This is almost a 30% increase every year. In recent technologies, if Artificial intelligence is the future, then Computer Vision will be the most amazing appearance of it.