Object Detection Using Range Cluster Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting objects in digital images primarily rely on two-dimensional information, leading to inaccuracies in object detection, such as false positives or missed detections, especially when range information is not utilized effectively.
Innovation Solution
A method that involves receiving a digital image, identifying range information, generating a cluster map based on this information, and using it to accurately detect and segment objects and background by grouping pixels by their distances from a viewpoint, thereby improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only two-dimensional image information is used for object detection, then the detection process is simple, but the detection accuracy is low with many false positives and missed detections
Solution Approach 1:
The patent applies dimensionality change by integrating three-dimensional range information (depth data) with traditional two-dimensional image data. The ranging capture device captures depth information for each pixel, creating a third dimension that enables more accurate object detection and segmentation, resolving the contradiction between detection accuracy and process complexity.
2Reliability
If range information is not utilized, then the detection algorithm is simpler to implement, but object detection accuracy deteriorates with increased false positives and missed detections
Solution Approach 1:
The patent merges range information (depth data) with traditional two-dimensional image data into a unified detection framework. By combining these different types of information, the system achieves more reliable and accurate object detection while managing algorithmic complexity through integrated processing.
3Measurement precision
If traditional face detection algorithms are used without range information, then the implementation is straightforward, but detection errors increase significantly
Solution Approach 1:
The patent extends traditional two-dimensional face detection algorithms by incorporating three-dimensional range information. This additional dimensional data enables the detection system to distinguish actual faces from false positives more effectively, significantly improving face detection accuracy while adding manageable complexity to the detection method.
Data Source
AI summary
A system and method for detecting objects and background in digital images using range information includes receiving the digital image representing a scene; identifying range information associated with the digital image and including distances of pixels in the scene from a known reference location; generating a cluster map based at least upon an analysis of the range information and the digital image, the cluster map grouping pixels of the digital image by their distances from a viewpoint; identifying objects in the digital image based at least upon an analysis of the cluster map and the digital image; and storing an indication of the identified objects in a processor-accessible memory system.