Rotten fruits image classification is a task in computer vision that involves identifying whether a fruit in an image is fresh or rotten. This is typically accomplished using machine learning algorithms that are trained on a dataset of labeled images, where each image is annotated as either fresh or rotten.
Vehicle type classification is the process of categorizing vehicles into different groups based on their design, functionality, and purpose. This classification is used in various industries, including transportation, automotive, and insurance, among others, to analyze data and make informed decisions.
Vehicle damage assessment using computer vision is an active research and development area. The goal is to use computer algorithms to automatically detect and analyze vehicle damage from images or videos.
Gesture classification using computer vision involves recognizing and categorizing hand or body movements captured by cameras as input, with the goal of inferring the intended gesture. This can be achieved through various techniques such as image processing, machine learning, and deep learning.
The process starts with capturing video or image data of the gestures, followed by preprocessing and feature extraction. After that, the features are fed into a machine-learning model that has been trained to recognize gestures, resulting in the classification of the input gesture. This technology has various applications in human-computer interaction, gaming, sign language recognition, and other fields.
Human 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).