Human face emotion classification is the process of identifying and categorizing emotions in human expressions, human speech, or text. This can be done through various techniques, such as natural language processing, machine learning, and sentiment analysis. The goal of emotion classification is to understand and interpret human emotions in order to improve communication, decision-making, and overall emotional intelligence. Common emotions that are classified include happiness, sadness, anger, fear, surprise, and neutral.

In the previous blog, we discussed the applications of computer vision in the manufacturing industry.

This blog explains how to use navan.ai, a no-code computer vision platform to build an image classification model to classify damaged and intact medical packages.

Computer vision is a field of artificial intelligence that focuses on teaching computers to interpret and understand visual data from the world around them, such as images and videos.

In manufacturing, computer vision can be used to automate a variety of tasks, such as quality control and inspection. For example, a manufacturing company could use computer vision to automatically inspect products for defects or to monitor production processes to ensure they are running smoothly. This can help to improve the efficiency and accuracy of the manufacturing process, while also reducing the need for manual labor.

We all know AI is an ocean and in that ocean, it's very hard to know each and every marine organism. Likewise, it's very hard to know AI terminologies, their differences, and most importantly what data can be used to build different models. Let us understand a bit more about Image Classification, Image Detection, and Image Segmentation.

YOLO Algorithm and its Applications in Computer Vision

What is the YOLO Algorithm?

The YOLO algorithm is a computer vision technique that allows us to detect objects in images and videos. This algorithm is different from other object detection algorithms because it can identify multiple objects in an image or video frame. It was originally developed for human detection but has been extended to other tasks such as vehicle detection, face detection and pose estimation.

Ever wondered how smart our eyes and brain are? What if we could train a machine to become smart to a certain extent? For example, we can look at images of skin and figure out if there’s some disease. We could train a machine to look at images and classify them into different classes positive (skin has some disease) and negative (skin does not have a disease).

Challenges in AI development​

Artificial Intelligence market size is growing and it is said that it can grow up to $15.7 trillion by 2030, as quoted in the research paper https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf

As AI grows, the impact and challenges rise parallely as well. Let's see some of the most common challenges in Artificial Intelligence Development.

The pandemic has changed the way we live. People have become extremely conscious of every surface they touch and that worry takes up a significant part of the brain and leads to anxiety. There are some simple ways in which you can avoid this at spaces which require people to tap a RFID card or register their fingerprint to get access to the space. This can be done by using facial recognition along with the cameras placed at these entry points.