How the usage of Computer Vision changes the future of the world?
The recent advantage of computing power has enabled us to make far stretched steps into delving right into the future with technologies like Artificial Intelligence and machine learning.
The introduction of computer vision has enabled us to conquer very big strides into real-time object detection. Let us see in this article how computer vision will help us to conquer the future.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), etc.
Important Features and usage of Computer Vision
- Face recognition: Snapchat and Facebook use face-detection algorithms to apply filters and recognize you in pictures.
- Image retrieval: Google Images uses content-based queries to search relevant images. The algorithms analyze the content in the query image and return results based on best-matched content.
- Gaming and controls: A great commercial product in gaming that uses stereo vision is Microsoft Kinect.
- Surveillance: Surveillance cameras are ubiquitous at public locations and are used to detect suspicious behaviors.
- Biometrics: Fingerprint, iris, and face-matching remain some common methods in biometric identification.
- Smart cars: Vision remains the main source of information to detect traffic signs and lights and other visual features.
Major Fields in which Computer Vision has made huge leaps:
Image Classification
The problem of image classification goes like this: Given a set of images that are all labeled with a single category, we’re asked to predict these categories for a novel set of test images and measure the accuracy of the predictions.
Computer Vision researchers have come up with a data-driven approach to solve this. Instead of trying to specify what every one of the image categories of interest looks like directly in code, they provide the computer with many examples of each image class and then develop learning algorithms
The most popular architecture used for image classification is Convolutional Neural Networks (CNNs) CNNs tend to start with an input “scanner” which isn’t intended to parse all the training data at once.
Object Detection
The task to define objects within images usually involves outputting bounding boxes and labels for individual objects. This differs from the classification/localization task by applying classification and localization to many objects instead of just a single dominant object.
For example, in-car detection, you have to detect all cars in a given image with their bounding boxes.
Object Tracking
Object Tracking refers to the process of following a specific object of interest, or multiple objects, in a given scene. It traditionally has applications in video and real-world interactions where observations are made following an initial object detection. Now, it’s crucial to autonomous driving systems such as self-driving vehicles from companies like Uber and Tesla.
Semantic Segmentation
Central to Computer Vision is the process of segmentation, which divides whole images into pixel groupings which can then be labeled and classified.
Particularly, Semantic Segmentation tries to semantically understand the role of each pixel in the image (e.g. is it a car, a motorbike, or some other type of class?). For example, in the picture above, apart from recognizing the person, the road, the cars, the trees, etc., we also have to delineate the boundaries of each object. Therefore, unlike classification, we need dense pixel-wise predictions from our models.
Instance Segmentation
Beyond Semantic Segmentation, Instance Segmentation segments different instances of classes, such as labeling 5 cars with 5 different colors. In classification, there’s generally an image with a single object as the focus and the task is to say what that image is. But in order to segment instances, we need to carry out far more complex tasks.
Conclusion
These 5 major computer vision techniques can help a computer extract, analyze, and understand useful information from a single or a sequence of images.
The above techniques are widely used to gain knowledge about the surroundings in-order for the AI agent to visually think and execute its functions in a proper manner.
