Geographic Feature Detection Using Scale-Invariant Image 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 reference components in a data library to identify common patterns like road signs, using techniques like SIFT to generate scale-invariant features, allowing for the detection and identification of geographic features regardless of scale, rotation, or brightness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to collect and identify geographic features, then data accuracy can be maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical data collection methods with an automated image processing system that uses computer vision algorithms to detect and identify geographic features from images captured by cameras or other imaging devices, dramatically improving productivity while reducing time loss
Solution Approach 2:
The system creates digital copies of geographic features through image capture and processing, allowing multiple analyses and identifications to be performed on the same data without additional field work, thereby increasing efficiency and reducing time requirements
2Measurement precision
If scale-variant feature detection is used, then more detailed information can be captured, but detection accuracy varies with object size in the image
Solution Approach 1:
The patent transforms the detection parameters from scale-dependent to scale-invariant by using features such as shape descriptors, aspect ratios, and relative positioning that remain consistent regardless of the object's size in the image, thereby maintaining detection accuracy across varying scales
Solution Approach 2:
The system transitions from detecting absolute size measurements to detecting dimensional relationships and proportions, using features like aspect ratios, relative positions, and shape characteristics that are invariant to scale transformations, enabling consistent detection across different object sizes
3Measurement precision
If comprehensive image processing is performed on all collected images, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the image processing task into distinct segments: image acquisition, preprocessing, feature extraction, feature comparison, and identification. Each segment handles specific operations independently, reducing overall system complexity while maintaining comprehensive processing capability and high accuracy
Solution Approach 2:
The system extracts only the relevant invariant features from images (such as shape characteristics, relative positions, and dimensional proportions) rather than processing all image data, thereby reducing computational complexity while preserving the information necessary for accurate identification
Data Source
AI summary
Systems, devices, features, and methods for detecting common geographic features in images, such as, for example, to develop a navigation database are disclosed. For example, a method of detecting a common text pattern, such as for a road or path sign, 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 correspond to an object about the road or path in the selected image are determined. In one embodiment, the determined components are independent or invariant to scale of the object. The determined components are compared to reference components in a data library. If the determined components substantially match the reference components, the object in the selected image is identified to be a common pattern, such as for a standard road or path sign, corresponding to the reference components in the data library.