Descriptor Vector Computation via Wavelet Dot Products
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Solution Overview
Problem
Current image matching techniques face challenges in computing effective descriptor vectors, which affect the performance of image matching processes due to sensitivity issues related to object properties in images.
Innovation Solution
The method involves identifying and normalizing regions in a digital image using Gabor sampling, generating wavelets, computing dot products, and concatenating amplitudes to produce a descriptor vector, improving the quality of the image matching process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional descriptor computation methods are used, then the image matching process can be performed, but the accuracy and reliability of matching is insufficient due to sensitivity issues related to object properties
Solution Approach 1:
The patent transforms the descriptor computation by changing the mathematical parameters and operations used. Instead of traditional descriptor methods, the invention uses wavelet transforms with specific scaling factors, computes dot products between wavelets and image regions, and concatenates amplitude values to create enhanced descriptors that are less sensitive to object property variations while maintaining matching accuracy
Solution Approach 2:
The patent replaces traditional mechanical image processing operations with mathematical wavelet-based computations. The system substitutes conventional descriptor extraction mechanisms with wavelet transform operations, dot product calculations, and amplitude concatenation, resulting in more robust and reliable image matching performance
2Productivity
If descriptor vectors are computed using conventional methods, then processing can be performed, but the quality of the descriptor affects matching performance insufficiently
Solution Approach 1:
The patent segments the image into multiple local regions and processes each region independently through wavelet transforms. By dividing the image into discrete regions of interest and computing descriptors for each segment separately, the system enhances the overall quality of the descriptor vector while maintaining efficient processing through parallel computation of independent segments
Data Source
AI summary
Systems and methods for descriptor vector computation are described herein. An embodiment includes (a) identifying a plurality of regions in the digital image; (b) normalizing the regions using at least a similarity or affine transform such that the normalized regions have the same orientation and size as a pre-determined reference region; (c) generating one or more wavelets using dimensions of the reference region; (d) generating one or more dot products between each of the one or more wavelets, respectively, and the normalized regions; (e) concatenating amplitudes of the one or more dot products to generate a descriptor vector; and (f) outputting a signal corresponding to the descriptor vector.