- 1 Introduction
- 2 Computer Vision Applications
- 2.1 Object Classification
- 2.2 Image Classification and Localization
- 2.3 Object Detection
- 2.4 Semantic and Instance Segmentation
- 2.5 Object Tracking
- 2.6 Image Captioning
- 2.7 Image Question and Answer
- 2.8 Image Generation
- 2.9 Image Super Resolution
- 2.10 Image Enhancement
- 2.11 Image Style Transfer
- 2.12 Image Colorization
- 2.13 Sketch to Image Conversion
- 2.14 Image Construction
- 3 Conclusion
Nowadays, computer vision is trending technology. It is highly in demand in the security and surveillance industry, self-driven cars, entertainment apps to name the few. This surge in popularity of computer vision is largely due to the emergence of state of art deep learning technologies that are able to solve computer vision tasks with very high accuracy, something which was considered unachievable a decade back. In this post, we will see the most popular and creative computer vision applications.
We are not going to cover the technical details of them but just keep it a simple read for beginners. So let us begin.
Computer Vision Applications
The objective of object classification is to assign a label to an input image from a fixed set of categories or class. For humans, this task is effortless but for computers to classify an image is not a straightforward task. Yet the modern computer vision techniques are able to classify images with great accuracy.
2Image Classification and Localization
The objective of this task is to assign a label on an input image from a fixed set of categories and also show the location of the object in the image by drawing a box around the object. This problem is the next level problem after classification.
This application of computer vision is a more generalized version of the above task(Image classification and localization). In this task, an image may contain more than one object which needs to be classified and localized individually. Self-driving car technology fundamentally relies on object detection to navigate through the roads.
4Semantic and Instance Segmentation
We all know that image is a collection of different pixels. The aim of segmentation is to group together the pixels that have similar attributes. There are two types of segmentation – 1) Semantic Segmentation and 2) Instance Segmentation
In Semantic Segmentation, each and every pixel is classified into a class label. In simple words, semantic segmentation does a pixel-wise classification i.e label each pixel with a class.
Instance Segmentation is the combination of two task object detection and Semantic Segmentation. i.e. Instance Segmentation=Object detection+Semantic segmentation
Object tracking is the process of locating a moving object (or multiple objects) over time. Visual object tracking is an important element of many applications such as person-following robots, self-driving cars or surveillance cameras, etc.
The objective of image captioning is to generate the textual description of an image or in other words describing the content of an image. It is not only a Computer Vision task but also uses Natural Language Processing to generate the results.
7Image Question and Answer
Given an image and a question regarding the image, the objective of this computer vision application is to provide an accurate natural language answer.
The objective of this computer vision application is to generate new non-existent images from an existing dataset. These generated images look as authentic as the existing images.
9Image Super Resolution
The objective of this task is to reconstruct a higher-resolution image from the observed lower-resolution images.
The cameras have many limitations. The camera sensor responds linearly to the incoming light while human perception performs more non-linear mapping with the photons. So that’s why users are disappointed with the photographs they take because the photos do not match their expectations and visual experience. And also these issues become more when cameras have small sensors and compact lenses. Image enhancement methods attempt to address these issues with color rendition and image sharpness.
11Image Style Transfer
The objective of this task is to modify the style of an image based on another image, while still preserving its content.
The objective of this task is to convert the input grayscale (black and white) image to a colorized image that represents the semantic colors and tones of the input. Image colorization techniques are bringing old black and white photos to color.
13Sketch to Image Conversion
The objective of this task is to convert the hand-drawn sketch image into a realistic image. This is really helpful for non-artistic people to come up with an image by just drawing a rough sketch.
The objective of image reconstruction or image restoration is to recover the original clean images from noise ones. The noise may arise in various forms, such as motion blur, low resolution.
In this post, we saw plenty of computer vision applications right from the basics ones to the more advanced ones. The later examples would be difficult for beginners to understand straight away but it is worth knowing about them.