Hue-Based Image Encoding for Efficient Object Detection
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Solution Overview
Problem
Existing image processing techniques face challenges in efficiently managing and retrieving information from large amounts of digital image data, particularly in detecting objects and features across similar images or overlapping regions due to variations in illumination, shading, and other conditions.
Innovation Solution
A hue-based encoding system that clusters image pixels using an influence function to generate hue clusters, assigning hue values to pixels, and classifying them based on statistical maximum likelihood estimation, reducing the number of hues and representing significant areas in the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image processing techniques are used to detect objects and features in large amounts of digital image data, then detection accuracy can be maintained, but processing time and computational complexity increase significantly
Solution Approach 1:
The image processing task is segmented into distinct stages: color space conversion to HSV, influence function calculation, clustering-based hue identification, and object detection. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining processing speed.
Solution Approach 2:
The patent extracts and utilizes only the hue component from the HSV color space for object detection, ignoring saturation and value components. This extraction of essential information reduces the dimensionality of processing and decreases computational complexity while maintaining detection effectiveness.
2Measurement precision
If clustering algorithms are applied to identify hue clusters in image data, then color-based object detection accuracy improves, but computational power requirements increase
Solution Approach 1:
The patent applies clustering algorithms only to a subset of pixels (e.g., sample pixels or pixels meeting certain criteria) rather than all pixels in the image. This partial application maintains color detection accuracy for relevant regions while reducing overall computational power consumption.
Solution Approach 2:
The influence function automatically identifies significant hue clusters by measuring the influence of each pixel on its neighbors, eliminating the need for manual parameter tuning or external assistance. This self-organizing process reduces computational overhead while maintaining detection precision.
3Loss of substance
If the number of hue values in an image is reduced through clustering, then image compression efficiency improves, but detail information may be lost
Solution Approach 1:
The patent changes the parameter representation from individual pixel hues to cluster-based representative hues. By grouping pixels with similar hue values into clusters and using a single representative value per cluster, the data compression ratio improves while preserving the essential color information needed for object detection.
Solution Approach 2:
Different regions of the image are assigned to different hue clusters based on local color characteristics. This local quality approach ensures that color variations within different regions are preserved through appropriate clustering, maintaining image detail information while achieving compression through reduced color palette.
Data Source
AI summary
A system and method for encoding a digital image may include obtaining image data of the digital image, wherein the image data comprises a plurality of image pixels, selecting a plurality of sample pixels from the plurality of image pixels, clustering the plurality of sample pixels based on an influence function to generate a plurality of hue clusters, classifying the image pixels to the plurality of the hue clusters, and assigning hue values of the hue clusters to the plurality of the image pixels.