AI That Finds Partially Hidden Objects in Images

This patent describes an AI system that uses multiple machine learning models to accurately identify objects in an image, even when parts of those objects are blocked by something else.

What it covers

This patent details a system for detecting objects in images, even when they are partially hidden. It starts by taking an input image and extracting 'object-based features' and 'context-based features'. These features are then processed by a deep convolutional neural network (DCNN) model. The DCNN's output is sent to two specialized models: an 'occlusion model' and a 'generative compositional model'. The occlusion model specifically identifies areas where an object is blocked by another object. The generative compositional model looks for different parts or components of objects that might be present. By combining the information from both these models, the system creates a 'likelihood map' to show if a particular object is depicted in the image, even with parts missing. For example, if a car is mostly hidden behind a tree, this system could still identify it by recognizing visible parts and understanding the context of the occlusion.

What it doesn't cover

• —Does not cover detecting objects that are completely hidden and have no visible parts or context clues.
• —Does not cover object detection methods that do not use a deep convolutional neural network (DCNN).
• —Does not cover systems that only use a single model for object detection without separate occlusion and compositional analysis.
• —Does not cover general image classification where the goal is to label the entire image, rather than locate specific objects.
• —Does not cover object detection that relies solely on traditional computer vision techniques without machine learning models.

The clever bit

The clever part is combining an 'occlusion model' that identifies blocked areas with a 'generative compositional model' that recognizes object parts. This dual approach allows the system to piece together what an object is, even when it's not fully visible, by understanding both what's missing and what's present.

Why it matters

Accurately detecting objects, even when they are partially obscured, is critical for many real-world applications. This technology improves the reliability of systems that need to 'see' and understand their environment, like self-driving cars or security cameras. It helps these systems make more informed decisions by reducing errors caused by visual obstructions.

Real-world examples

• 1.Autonomous vehicle perception systems identifying pedestrians or other cars partially blocked by obstacles.
• 2.Security camera systems detecting intruders partially hidden behind furniture or foliage.
• 3.Industrial automation robots identifying components on an assembly line that are partially stacked or covered.
• 4.Medical imaging analysis for detecting anomalies partially obscured by other tissues.