Geographic Feature Detection Using Invariant Components
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Solution Overview
Problem
Collecting and identifying geographic features such as road signs and markings for navigation systems is a time-consuming and intricate process, requiring efficient methods to detect and associate data for effective navigation.
Innovation Solution
A method involving the collection of images along roads, where invariant components are determined and compared to a reference library to identify text characters and other features, enhancing the development of navigation databases through scale-invariant feature transforms and gradient vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to collect and identify geographic features, then comprehensive data can be obtained, but the process is time-consuming and intricate
Solution Approach 1:
The patent segments the geographic feature identification process into distinct components: image collection, invariant component extraction, component comparison, and feature identification. This segmentation allows parallel processing and automation, significantly improving productivity while reducing the time required for manual feature identification
Solution Approach 2:
The patent performs preliminary actions by pre-processing images to extract invariant components before actual feature identification. By pre-computing scale-invariant features and organizing them in a systematic manner, the system prepares data structures that enable rapid matching and identification, reducing overall processing time
2Measurement precision
If scale-variant feature detection is used, then simple matching can be performed, but detection accuracy decreases across varying scales
Solution Approach 1:
The patent applies parameter changes by transforming image features into scale-invariant representations. By changing the parameter space from raw pixel coordinates to normalized gradient orientations and magnitudes, the system maintains detection accuracy across different scales while adapting to various viewing conditions and distances
3Measurement precision
If detailed feature analysis is performed, then identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent extracts only the essential invariant components from images - specifically scale-invariant gradient vectors and key geometric features - rather than processing entire images. This extraction approach maintains high identification accuracy by focusing on discriminative features while significantly reducing processing complexity and computational requirements
Data Source
AI summary
Systems, devices, features, and methods for detecting geographic features in images, such as, for example, to develop a navigation database are disclosed. For example, a method of detecting text from collected images includes collecting a plurality of images of geographic areas along a road or path. An image of the plurality of images is selected. Components that represent a feature about the road or path in the selected image are determined. In one embodiment, the components are independent or invariant to scale of the feature. The determined components are compared to reference components in a data library. If the determined components substantially match with the reference components, the feature in the selected image is identified to be a text character (e.g., of a road sign) corresponding to at least some of the reference components in the data library.