Feature Vector Transform for Object Geometry Recognition
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Solution Overview
Problem
Current technologies for extracting features from sensed data, such as images or spectral sensors, face challenges in accurately determining object structure and recognition, especially in scenarios with limited feature points like skin or produce surfaces, where high signal-to-noise features are scarce.
Innovation Solution
The implementation of feature vector transforms, specifically the 'freckle' transform, which computes local feature signatures by comparing pixel values with neighboring pixels at different scales and orientations, producing dense feature vectors robust to intensity and contrast changes, enabling effective feature extraction and recognition even in low-feature scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feature extraction methods are used, then processing speed is maintained, but feature density and recognition accuracy deteriorate in low-feature scenarios
Solution Approach 1:
The image is divided into multiple overlapping patches, and feature vectors are extracted from each patch independently. This segmentation approach transforms the global feature extraction problem into local patch-level operations, enabling dense feature representation even in low-feature scenarios while maintaining computational feasibility through localized processing
Solution Approach 2:
The method transforms 2D image patches into N-dimensional feature vectors through the freckle transform, which computes local feature signatures by comparing pixel values with neighboring pixels at different scales and orientations. This dimensionality transformation creates dense feature representations that improve recognition accuracy by capturing multi-scale and multi-orientation information
2Quantity of substance
If feature vectors are extracted from every patch, then feature density improves, but computational load increases
Solution Approach 1:
The method extracts feature vectors from every patch, which is an excessive action compared to traditional methods that select only key patches. This excessive extraction of features from all patches ensures maximum feature density and coverage, particularly beneficial in low-feature scenarios, while the computational load is managed through efficient implementation of the freckle transform
Solution Approach 2:
The freckle transform uses multiple parameters including different scales and orientations to compute local feature signatures. By varying these parameters across patches, the method generates dense feature vectors that capture comprehensive local information, improving feature quantity while the parameterized approach allows for computational optimization
3Measurement precision
If the freckle transform is applied to all patches, then recognition accuracy improves, but processing time increases
Solution Approach 1:
Processing is segmented into independent patch-level operations where the freckle transform is applied to each patch separately. This segmentation enables parallel processing of patches, reducing overall processing time while maintaining the accuracy benefits of applying the transform to all patches rather than selecting a subset
Solution Approach 2:
The freckle transform computes local feature signatures by periodically comparing pixel values with neighboring pixels at different scales and orientations. This periodic comparison pattern efficiently extracts multi-scale and multi-orientation features, improving recognition accuracy while the regular computational pattern allows for optimization and parallel execution
Data Source
AI summary
A method for obtaining object geometry in which image frames of a scene (e.g., video frames from a user passing a smartphone camera over an object) are transformed into dense feature vectors, and feature vectors are processed to obtain geometry of an object in the scene. The object geometry is determined from the feature vectors. Feature vector transforms are leveraged in a signal processing method for object identification (e.g., using machine learning classification), digital watermark or bar code reading and image recognition.