Computer vision is a branch of artificial intelligence that helps computers understand and analyze visual data from digital images, videos, and similar visual inputs. Using digital visual data obtained from various sources, we can teach computers to detect and interpret visual objects. It also plays a critical role in areas such as image recognition and object detection. There are many different tasks that computer vision can perform. In this article, we will discuss computer vision tasks in detail.
What are computer vision tasks?
Computers can use images and videos to learn and perform tasks using a set of techniques and algorithms. These techniques and algorithms help them understand the visual info by picking out important details from pictures and videos. There are many different computer vision tasks and let us discuss in detail the most common computer vision tasks and their applications in different fields.
Image Classification
One of the main responsibilities of computer vision is image classification. The primary goal is to assign a predefined label or category to an input image by identifying the main content of the specific image. The computer system predicts which class or category the main image content belongs to. Image classification mainly deals with a single object. For example, an image classification model could be trained to identify and label an image, if the image contains a cat, a dog, a car, a human or a specific object.
Explained below are different types of image classification, and how image classification works.
Types of Image Classification:
There are two main types of image classification for categorizing images into predefined classes:
- Single-Label Classification: In single-label classification, each image is assigned to one single category, where the goal is to predict one label per image. For example, classifying an image as containing a cat or a dog.
- Multi-Label Classification: Multiple-Label classification involves assigning multiple labels to an image which has multiple objects. For example, an image might contain a cat, a dog and a tree and the image classification recognizes all these objects and labels them.
Object Detection
One of the significant function in computer vision is Object detection. The main purpose of object detection is to identify and locate specific objects in the provided input sources like digital images or videos. Few examples for object detection are locating a pedestrian in a street or a car in a road traffic.
There is a two-part process namely object localization and object classification which combines to make the object detection process.
- Object Localization: Object Localization means locating objects. Here we detect or identify the objects by pinpointing their specific location within an image or video. The object detection in Computer Vision tasks use bounding box to mark the object locations in an image or tracking a moving object in a video.
- Object Classification: Once we know where the objects are, we move on to object classification. This means putting each object into a pre-defined category like 'human', 'car', or 'animal'.
Image Segmentation
Image Segmentation is an crucial task in computer vision for dividing an image into meaningful segments or regions. The divided segments can correspond to individual objects, parts of objects or regions with similar characteristics. This image segmentation process can break down an image into meaningful building blocks to help computer to identify and understand the content.
The main goal of image segmentation is to divide an image into distinct segments or regions which are related to meaningful objects, regions or even individual pixels.
There are 2 main types of image segmentation:
- Semantic Segmentation: Semantic segmentation in computer vision involves assigning a class label to each individual pixel in an image. Each pixel in an image is categorized and assigned a label based on the object it belongs. When an semantic segmentation is done on an image the output is a 'segmentation map' where each pixel's color represents its class.
- Instance Segmentation: Instance segmentation delves into the image at a more granular level by identifying and delineating each individual instance of those objects. It is something like, as an example of, having different coloured cats in an image. Another good example could be, imagine a group photo of students. Semantic segmentation labels everyone as 'person', and instance segmentation would identify and outline each individual person in the group photo.
There is also another segmentation type called Panoptic Segmentation which combines both semantic and instance segmentation to provide a complete understanding of every pixel in the image.
Image segmentation process is used in various applications like medical imaging, to identify tumours or organ health and in autonomous driving to assist in distinguishing between road, vehicles, and pedestrians.
Face and Person Recognition
Facial recognition and person recognition share a close connection. Both are interconnected technologies in computer vision used to identify individuals. The recognition process depends on machine learning algorithms like convolutional neural networks (CNNs). These play a crucial role in accurately and efficiently extracting features and classifying faces.
Facial recognition focuses the facial identities and features to identify an individual person. The facial recognition is done by comparing an individual person's image or video frame to a dataset of known faces labelled.
Person recognition is aimed at identifying people by extending beyond face by including the entire body, body shape and activities like gait, posture, clothing, and other personal attributes.
Edge Detection
Edge detection is one of the image process techniques in computer vision tasks to identify the boundaries between objects or different regions in an image. Edge detection works by highlighting areas in an image which is identified by the significant change in intensity or colour. By identifying edges in an image using edge detection method, computer vision systems can locate objects within an image and recognize them based on their shapes or structures which helps to divide an image into meaningful segments or region of individual objects.
Edge detection is used in feature detection or image classification and used in application such as autonomous vehicles and medical image analysis.
Image Restoration
Image restoration task in computer vision is a technical process, which helps to reconstruct or recover old and damaged, faded or corrupted images to a clearer and more visually appealing version by improving the image quality. This process involves removing noise, blur, scratches and other damages or imperfections and restore back to their original clarity and details.
Image restoration process is highly useful in fields like Digital Photography, Medical Imaging, Forensic Science and Satellite Imagery to enhance and improve visual quality of images.
Feature Matching
Feature matching process in computer vision is used to find corresponding, similar, identical features or points from one image to across multiple images. The feature matching is performed by using techniques like nearest neighbour search by finding the closest descriptor in one image to the descriptor in another image.
Feature matching is applied for object recognition, image stitching, 3D construction of a scene, motion tracking and in augmented reality. Using feature matching, computer vision systems can establish relationship between images for understanding and analysing visual data.
Scene Reconstruction
Scene reconstruction process in computer vision helps in creating a 3D model of a real-world scene. It is like creating a virtual replica of a room using multiple images taken of the room. Scene reconstruction process is very useful for capturing, analysing and manipulation the physical world in a digital format.
One of the real-world applications would be Crime Scene reconstruction which helps to understand how the crime unfolded and to identify the potential suspects. Other use cases include Virtual Reality, Augmented Reality, Autonomous Navigation and Film & Video Production.
There are two main reconstruction techniques used as below:
- Traditional Techniques: The traditional techniques generally rely on geometric principles and computer vision algorithms. The Structure from Motion (SfM) technique is the most reliable one in traditional method. The SfM is often combined with triangulation to compute 3D points from corresponding image features.
- Deep Learning Techniques: With the popular use of deep learning methods, Convolutional Neural Networks (CNNs) play a key role in image reconstruction tasks. The CNNs can learn to directly predict and capture complext patterns and structures from single images.
Video Motion Analysis
Video motion analysis in computer vision is a technique used in the process of detecting, tracking and interpretation of motion patterns in video sequences. This helps to analyse and understand the motion patterns of objects in a video sequence.
Conclusion:
In this article about computer vision tasks, we have discussed about different computer vision tasks in detail using images and videos by analysing and extracting meaningful information. We have also discussed about common applications in different fields and real-life scenarios in different fields and activities. Computer vision tasks are helping humans in numerous use cases and it grows by the day.
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