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’.

Computer Vision Tasks

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.

Table of Content

  • What are computer vision tasks?
  • Image Classification
  • Object Detection
  • Image Segmentation
  • Face and Person Recognition
  • Edge Detection
  • Image Restoration
  • Feature Matching
  • Scene Reconstruction
  • Video Motion Analysis
    • Conclusion:

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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....

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....

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....

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....

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....

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....

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....

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....

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....

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