Image classification using Computer Vision is a powerful tool that can be used to identify half and fully-filled bottles. This technology uses machine learning algorithms to analyze digital images and classify them into different categories based on their visual characteristics.
How to Build a Computer Vision AI Model to Identify Lumpy Skin Disease Without Any Coding?
Lumpy skin disease (LSD) is a viral disease that affects cattle, and it is characterized by the formation of skin nodules or lumps on the animal's body. Computer vision can be helpful in identifying and monitoring the spread of LSD.
How to Build a Computer Vision AI Model to Identify Rotten Fruits Without Any Coding?
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.
How to Build a Vehicle Classification AI Model Without Any Coding?
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.
How to Build a Computer Vision AI Model to Identify Damaged Vehicles Without Any Coding?
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.
How to Build a Gesture Recognition AI Model Without Any Coding?
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.
How to Build an Emotion Recognition AI Model Without Any Coding?
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.
Quality Control in Manufacturing using No-Code Computer Vision -navan.ai
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.
Applications of Computer Vision in Manufacturing
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.
What is Image Classification, Detection, and Segmentation in Computer Vision?
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.