Object Classification Using Edge Energy and Intensity Variation
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Solution Overview
Problem
Collision avoidance systems fail to differentiate detected objects, such as automobiles and pedestrians, which is crucial for appropriate collision mitigation measures.
Innovation Solution
A method and apparatus that classify objects in a region of interest using edge energy information, intensity variation information, and other features like symmetry and histogram analysis, enabling the differentiation between pedestrians and vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sensor system is used to detect objects in front of a vehicle, then object detection capability is improved, but object classification capability deteriorates
Solution Approach 1:
The patent segments the object detection process into two distinct stages: first detecting objects using sensor systems (radar, infrared, cameras), then classifying detected objects by analyzing edge energy patterns and intensity variations in image regions. This segmentation allows each stage to optimize for its specific function without compromising the other.
Solution Approach 2:
The patent introduces image processing techniques as an intermediary between raw sensor data and object classification. By generating edge energy information and intensity variation data from sensor images, the system creates an intermediate representation that enables accurate object differentiation while maintaining detection capabilities.
2Reliability
If collision avoidance systems detect objects, then safety is improved, but the ability to differentiate between pedestrian and vehicle deteriorates
Solution Approach 1:
The patent applies local quality analysis by examining specific regions of interest in images for edge energy patterns and intensity variations. Different object types (pedestrians vs. vehicles) have distinct local visual characteristics that can be detected and measured, enabling the system to maintain both detection reliability and object type differentiation.
Solution Approach 2:
The patent changes the parameters analyzed from simple object detection to include edge energy information and intensity variation metrics. By transforming and analyzing these additional parameters from image data, the system gains the ability to differentiate object types while maintaining collision avoidance reliability.
3Device complexity
If basic object detection is implemented, then system complexity is reduced, but the precision of object classification deteriorates
Solution Approach 1:
The patent applies partial action by implementing object classification through analysis of specific visual features (edge energy, intensity variations) rather than requiring complete object modeling. This partial approach achieves sufficient classification precision without the excessive complexity of comprehensive object recognition systems.
Data Source
AI summary
A method and apparatus for classifying an object in an image is disclosed. Edge energy information and/or intensity variation information is generated for an object in a region of interest of the image. The object occupying the region of interest is classified based on the edge energy information and/or intensity variation information.