Feature Point Detection Using Intensity and Depth Data Fusion
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Solution Overview
Problem
Existing feature point detection methods in images rely solely on intensity data, lacking robustness and accuracy in tracking feature points across sequences due to the absence of depth and confidence data, which limits their effectiveness in applications like 3D scene reconstruction and object recognition.
Innovation Solution
A feature point detection apparatus and method that utilizes both intensity and depth data from time-of-flight cameras, incorporating confidence data to enhance key point determination and feature description, allowing for more robust and accurate detection and tracking of feature points by combining structural information from both data types and weighting them based on reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only intensity data is used for feature point detection, then the detection process is simple, but the robustness and accuracy of feature point tracking deteriorates
Solution Approach 1:
The patent combines intensity data and depth data into a unified feature detection framework. The key point determination unit processes both data types simultaneously, and the feature determination unit generates feature vectors that incorporate both intensity-based and depth-based local environment descriptions, thereby improving tracking robustness while maintaining reasonable system complexity
Solution Approach 2:
The patent creates a composite feature representation by merging intensity data and depth data. The feature vector comprises both intensity-based features (from the intensity image) and depth-based features (from the depth image), forming a composite descriptor that leverages the complementary strengths of both data types for more reliable feature point tracking
2Device complexity
If only intensity data is used for feature description, then the data processing is straightforward, but the accuracy of local environment description deteriorates
Solution Approach 1:
The patent adds a depth dimension to the traditional two-dimensional intensity-based feature description. By incorporating depth data, the feature determination unit creates three-dimensional local environment descriptions that provide additional spatial context and discriminative power, improving the accuracy of feature point identification and matching
Solution Approach 2:
The feature vector is constructed as a composite of intensity-based features and depth-based features. This composite representation captures both the visual appearance and the spatial structure of the local environment, providing a more accurate and distinctive description than intensity data alone
3Reliability
If depth data is incorporated into feature point detection, then the robustness and accuracy of detection improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent divides the feature detection process into distinct functional modules: a key point determination unit that identifies candidate feature points using both intensity and depth data, and a feature determination unit that generates descriptive feature vectors. This segmentation allows the system to handle complex multi-data processing through organized, manageable components
Solution Approach 2:
The feature determination unit is designed to handle both intensity-based feature extraction and depth-based feature extraction through a unified framework. The same computational structure processes both data types, generating combined feature vectors that leverage multiple data sources without requiring completely separate processing pipelines
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the robustness and accuracy of feature point detection and tracking by leveraging depth data, reducing confusion in local environment descriptions and enhancing reliability, particularly in varying ambient conditions and resolutions.
Implementation Method 1
Modern time of flight, ToF, cameras provide, in addition to the intensity or color image, depth data
Data Source
AI summary
A feature point detection apparatus for detecting feature points in image data includes an image data providing unit for providing the image data, a key point determination unit for determining key points in the image data, a feature determination unit for determining features associated with the key points, each describing a local environment of a key point in the image data, and a feature point providing unit for providing the feature points. A feature point is represented by the position of a key point in the image data and the associated features. The image data comprise intensity data and associated depth data, and the determination of the key points and the associated features is based on a local analysis of the image data in dependence on both the intensity data and the depth data.