COMPUTER VISION

Image Generation

Computer Vision

Definition

AI image generation creates original images from text descriptions or reference images. Tools like DALL-E, Midjourney, and Stable Diffusion produce photorealistic or artistic images. For businesses, it accelerates the creation of marketing content, product mockups, catalog images, and training materials.

Related terms

Computer Vision

DALL-E

DALL-E is the image generation model developed by OpenAI that creates images from text descriptions. DALL-E 3, integrated into ChatGPT, produces high-quality images with precise control over composition, style, and details. Companies use it to quickly create visuals for social media, presentations, and marketing materials.

Computer Vision

Stable Diffusion

Stable Diffusion is an open source image generation model that can be run locally on your own servers. Unlike DALL-E, it does not require sending data to cloud services, offering greater privacy and customization. It can be fine-tuned on specific company products to generate catalog images perfectly consistent with the brand.

Computer Vision

GAN (Generative Adversarial Network)

GANs are AI architectures composed of two neural networks that compete: one generates content (generator) and the other evaluates it (discriminator). This competition produces increasingly realistic results. GANs were pioneers in image generation and are also used for data augmentation, synthetic data creation, and scenario simulation.

AI Fundamentals

Generative AI

Generative AI includes models capable of creating new content: text, images, code, music, video. ChatGPT generates text, DALL-E generates images, Copilot generates code. For SMEs, generative AI accelerates the production of marketing content, technical documentation, business proposals, and personalized customer communications.

EXPLORE

More terms in Computer Vision

Computer Vision

Computer vision is the field of AI that enables computers to interpret images and video. It includes object recognition, quality inspection, license plate reading, and security video analysis. In Italian manufacturing, computer vision automates visual quality control, reducing defects reaching the customer and speeding up production lines.

Object Detection

Object detection is the computer vision technique that identifies and locates specific objects in an image or video, drawing a bounding box around each one. It is used to count products on a conveyor belt, detect defects in components, and monitor safety areas. Algorithms like YOLO enable real-time detection, even on edge devices.

Image Classification

Image classification is the task of automatically assigning a category to an image. For example: 'conforming part' vs 'defective part', or automatically classifying scanned documents by type. CNNs (Convolutional Neural Networks) achieve accuracies above 99% on many industrial classification tasks.

Segmentation

Image segmentation is the technique that classifies every single pixel of an image, precisely identifying the contours of each object. Unlike object detection which draws bounding boxes, segmentation delineates the exact shape. It is used for precise measurements, medical image analysis, and autonomous guidance of robots and vehicles in factories.

Face Recognition

Face recognition is the technology that identifies or verifies a person's identity by analyzing facial features. In businesses, it is used for access control, attendance tracking, and security. It is important to implement it in compliance with GDPR and European AI regulations, which place strict limits on the use of biometric recognition.

Quality Inspection

AI quality inspection uses computer vision to automatically verify product conformity on the production line. High-resolution cameras and deep learning algorithms detect scratches, dents, color defects, and dimensional irregularities with speed and consistency impossible for the human eye. It reduces returns and rework costs.

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