Computer vision

What is Computer vision

Images are part of our daily lives. Thanks to smartphones and other devices, taking a picture or video and sharing it on the internet has never been easier.
YouTube for example, currently the second largest search engine, has over hundreds of hours of video being uploaded every minute and billions of videos are watched every day.

Being able to recognize these images or videos and know what is in them is an easy task for a human, but when the task in hand requires analyzing thousands or even millions of images… Well, then the capability to automate that task becomes a necessity.

This is where computer vision software like Ai.dielmo comes into play.

What is computer vision?

Computer Vision (CV) is a field of Artificial Intelligence (AI) that focuses on creating digital systems that can process, analyze, and make sense of visual data, images or videos, in the same way humans would.

Computer vision is based on making a computer capable of processing an image at a pixel level and understanding it. This way, machines attempt to retrieve visual information, handle it, and interpret the results through special algorithms and software, such as AI.dielmo.

How does computer vision work?

Computer vision algorithms used today are based on pattern recognition. Putting AI.dielmo as an example, we train our models on a massive amount of visual data, and the software uses that data to process images, label objects on them, and find patterns in those objects.

Rather than discern and process the world in images and objects like humans do, machines interpret images as a series of pixels, each with their own set of color values. For example, below is a picture of Abraham Lincoln. Each pixel’s brightness in this image is represented by a single 8-bit number, ranging from 0 (black) to 255 (white). These numbers are what software sees when you input an image. This data is provided as an input to the computer vision algorithm that will be responsible for further analysis and decision making.

For example, if we send a million images of cars, AI.dielmo will analyze them, identify patterns that are similar to all cars and, at the end of this process, will create a “cars” model.

As a result, the software will be able to accurately detect whether a particular image has a car in it.

Computer vision use cases

Computer vision systems can be seen in all sorts of industry fields, such as retail analytics, security, automated vehicles, healthcare, agriculture crop, animal monitoring, banking, and industrial technologies.
But overall, if we had to categorize the type of use cases, we could divide them into the most common tasks:

Object classification. The system parses visual content and classifies the object on a photo/video to the defined category. For example, the system can find a dog among all objects in the image.
Object identification. The system parses visual content and identifies a particular object on a photo/video. For example, the system can find a specific dog among the dogs in the image.
Object tracking. The system processes video finds the object (or objects) that match search criteria and tracks its movement.

Relevant computer vision fields

Image classification

Image classification forms the fundamental building block of Computer Vision. Computer Vision engineers often start with training a Neural Network to identify different objects in an image. Training a network to identify the difference between two objects in an image implies building a binary classification model. On the other hand, if there are more than two objects in an image, then it is a multi-classification problem.

It is important to note that to successfully build any image classification model that can scale or be used in production, the model has to learn from enough data. Transfer learning is an image classification technique that leverages existing architectures that have been trained to learn enough from huge data samples. The learned feature or task is then utilized to identify similar samples. Another term for this is knowledge transfer.

With the idea of transfer learning, Computer Vision engineers have built scalable solutions in the business world with a small amount of data. Existing architectures for image classification include ResNet-50, ResNet-100, ImageNet, AlexNet, VggNet and more.

Image processing

Image processing is a key aspect of vision systems because it deals with transforming images in order to extract certain information. Basic image processing techniques include smoothing, sharpening, contrasting, de-noising or colorization.

Image preprocessing is used to remove unnecessary information and help the AI model learn the images’ features effectively. The goal is to improve the image features by eliminating unwanted falsification and achieve better classification performances.

A common application of image processing is super-resolution. This technique typically transforms low-resolution images into high-resolution images. Super-resolution is a major challenge most computer vision engineers encounter because they often get the model information from low-quality images.

Character Recognition

Optical character recognition or optical character reader (OCR) is a Computer Vision technique that converts any kind of written or printed text from an image into a machine-readable format.

Existing architectures for OCR extractions include EasyOCR, Python-tesseract, or Keras-OCR. This technology is widespread and used for Number Plate Recognition as an example.

Image segmentation

While image classification aims to identify the labels of different objects in an image, instance segmentation tries to find the exact boundary of the objects in the image.

There are two types of Image Segmentation techniques: Instance segmentation and semantic segmentation.
Instance segmentation differs from semantic segmentation in the sense that it returns a unique label to every instance of a particular object in the image.

Object detection

This aspect of computer vision deals with detecting an object in an image and then tracking the object through a series of frames.

Object Detection is often applied to video streams, whereby the user is trying to track multiple objects at the same time with unique identities. Popular architectures of object detection include YOLO, R-CNN, or MobileNet.

Pose estimation

Pose Estimation makes computers understand the human pose. Popular architectures around Pose Estimation include PoseNet, DensePose, or MeTRAbs. These have been applied to solve real-world problems like, for example, crime detection via poses.

What features can be found in AI.dielmo

The main features that can be found in AI.dielmo are as follows:

Draw detections in images: With it, you can detect specific objects in an image, outline them and add a label of the detected object class.
Blur detections in images: This task is used to blur objects in an image.
Crop best object to a new image: Crop the best object detected in an image, based on the various configurable criteria, and obtain a new image with the cropping.
Draw detections in a video: Detection, tracking, and contouring of objects detected in a video.
Blur detections in a video: Task for the detection, tracking, and blurring of objects detected in a video.
Vectorize detections: Vectorize objects detected in images.
Customizable: Ai.dielmo is fully customizable to your project’s needs.

Conclusion

Computer vision is a growing field. Tremendous amounts of data that we create daily, which some people think of as a curse of our generation, are actually used for our benefit, the data can teach computers to see and understand objects.
This technology is not only limited to big corporations, thanks to software like Ai.dielmo, anyone can use computer vision, taking a step toward creating artificial intelligence projects that help them fulfill their daily tasks.