Visual Object Search Using Straight Line Segments
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Solution Overview
Problem
Current computer vision systems face challenges in handling variations in object presentation, lighting, and size due to the need for controlled environments and prior knowledge, making them impractical for unconstrained environments and increasing development time and costs.
Innovation Solution
A method that uses straight lines as salient features to match learned images with target images by determining translation, rotation, and scaling transformations, utilizing edge detection algorithms and transform matrices to assess the quality of fit between contour points, allowing for robust object recognition across different views and orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems use controlled lighting and positioning to improve recognition accuracy, then measurement precision is improved, but device complexity and development costs increase
Solution Approach 1:
The patent transforms the recognition problem by changing parameters from intensity-based pixel values to geometric line features. By representing objects as collections of lines with attributes (position, orientation, length), the system achieves invariance to lighting changes and positioning variations, improving measurement precision without requiring controlled environments
Solution Approach 2:
The patent extracts only the essential geometric features (lines) from complete images, discarding redundant information such as lighting variations, colors, and textures. This extraction of salient line features simplifies the data representation and enables robust recognition in unconstrained environments
2Measurement precision
If computer vision systems process complete images with all visual details, then measurement precision is improved, but computing time increases
Solution Approach 1:
The patent extracts only line features from complete images, reducing the data volume significantly. Instead of processing all pixels in an image, the system identifies and processes only the salient line segments that define object structure, thereby reducing computing time while maintaining recognition accuracy
Solution Approach 2:
The patent segments images into discrete line features rather than processing continuous pixel data. By breaking down objects into their constituent lines and treating each line as an independent feature with specific attributes, the system enables efficient processing and matching operations
3Adaptability or versatility
If computer vision systems are designed to recognize multiple object variations in unconstrained environments, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent changes the representation parameters from intensity-based to geometry-based, making the system inherently adaptable to lighting and positioning variations. By using line features with attributes (position, orientation, length) that are invariant to these transformations, the system achieves environmental adaptability without complex preprocessing or controlled conditions
Solution Approach 2:
The patent creates a universal line-based representation that can handle multiple object types and variations through the same matching framework. The line feature extraction and matching process works consistently across different objects, lighting conditions, and orientations, providing multi-functional capability without requiring separate processing pipelines
Data Source
AI summary
Disclosed is method of visual search for objects that include straight lines. A two-step process is used, which includes detecting straight line segments in an image. The lines are generally characterized by their length, midpoint location, and orientation. Hypotheses that a particular straight line segment belongs to a known object are generated and tested. The set of hypotheses is constrained by spatial relationships in the known objects. The speed and robustness of the method and apparatus disclosed makes it immediately applicable to many computer vision applications.