Local Differentiating Color Vector for Multi-Channel Feature Detection
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Solution Overview
Problem
Existing image feature detection systems lose significant information when transforming multi-channel images into single-channel grayscale images, leading to contrast loss between pixels with distinct colors having the same grayscale representation.
Innovation Solution
A computing device implements a low-complexity non-iterative algorithm to compute a local differentiating color (LDC) vector, allowing for multi-channel feature detection by representing response functions as linear or quadratic forms, enabling the use of existing single-channel feature detectors with adapted inner kernels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If multi-channel images are transformed into single-channel grayscale images for feature detection, then the detection process becomes simpler and compatible with existing algorithms, but significant image information is lost and contrast between pixels with distinct colors but same grayscale representation deteriorates
Solution Approach 1:
The patent transitions from single-channel grayscale detection to multi-channel color space detection by introducing a local differentiating color vector that operates in an extended dimensional space. This allows the system to maintain color information while performing feature detection, effectively adding a dimension to the traditional grayscale approach without increasing computational complexity significantly.
Solution Approach 2:
The patent applies local quality by computing a local differentiating color vector specific to each image point's neighborhood. Instead of using a global color-to-grayscale mapping, the system determines color weights locally based on the statistical properties of the color channels in each neighborhood, preserving local color contrast and information while enabling feature detection.
2Loss of information
If global color to grayscale mapping is used to preserve color information, then some color characteristics are maintained, but information loss still occurs and local color contrast is degraded
Solution Approach 1:
The patent replaces global color mapping with local color differentiation. For each image point, a local differentiating color vector is computed based on the covariance matrix of color channels in the neighborhood. This local approach preserves color information specific to each region while maintaining accurate local color contrast, avoiding the information loss inherent in global mapping approaches.
Solution Approach 2:
The patent changes the parameters used for color representation from fixed global weights to dynamic local weights. The local differentiating color vector's components are determined by the eigenvalues and eigenvectors of the local color covariance matrix, allowing the system to adapt to local color characteristics and preserve information that would be lost in global mapping.
Data Source
AI summary
Technologies for multi-channel feature detection include a computing device to determine a filter response of each image channel of a multi-channel image for one or more image filters. The computing device determines a local differentiating color vector based on the filter responses, applies the filter responses to the local differentiating color vector to generate an adapted response, and determines a total response of the multi-channel image based on the adapted response.