Digital Image Edge Pixel Indexing via Dynamic Thresholding
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Solution Overview
Problem
Existing methods for organizing and retrieving large digital image files are cumbersome, relying on barcodes or fingerprints, which require pre-processing and are inefficient for finding images after changes in organization.
Innovation Solution
A method that converts digital images to gray-scale, identifies pixel differences to detect edge pixels, and uses dynamic thresholding to distinguish edge pixels from non-edge pixels, allowing for indexing and retrieval based on image content without prior handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If barcodes or fingerprints are used to mark pictures for later retrieval, then the pictures can be found later, but the process is cumbersome and requires pre-processing
Solution Approach 1:
The patent extracts the essential visual content features (edge pixels, color histograms, texture patterns) directly from the image data itself, eliminating the need for separate barcode or fingerprint marking processes. The image content is analyzed and indexed directly, removing the cumbersome pre-processing step while maintaining reliable retrieval capability.
Solution Approach 2:
The image itself serves as both the content and the identifier. By analyzing edge pixels, color distributions, and texture patterns within the image data, the system enables the image to be self-indexed without requiring external markers or pre-processing operations. The image's own visual characteristics are used for recognition and retrieval.
2Reliability
If conventional techniques are used to mark pictures, then pictures can be found later, but the process is inefficient for finding images after changes in organization
Solution Approach 1:
The patent transforms images into multiple parameter representations including edge pixel patterns, color histograms, and texture feature vectors. These parameter transformations create robust indices that remain effective even when image organization changes, enabling efficient retrieval by querying these transformed parameters rather than relying on original file organization or artificial markers.
Solution Approach 2:
The system adds multiple dimensional representations of image content (edge spatial patterns, color distribution histograms, texture frequency spectra) beyond the original image data. This multi-dimensional indexing allows efficient retrieval through various query dimensions, improving both accuracy and efficiency regardless of how images are organized or renamed.
3Loss of information
If all pixels are analyzed for indexing, then comprehensive image content is captured, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most informative pixel characteristics - specifically edge pixels, color histogram summaries, and texture pattern representatives - rather than processing every pixel in detail. This selective extraction maintains comprehensive image content representation while dramatically reducing processing time and computational requirements.
Solution Approach 2:
The system applies partial analysis by focusing on statistically significant pixel features (edges, color outliers, texture variations) rather than uniformly processing all pixels. This partial action approach captures the essential image content for indexing while avoiding the time-consuming detailed analysis of every pixel, achieving a good balance between information completeness and processing efficiency.
Data Source
AI summary
The method is for recognizing and indexing a digital image. The digital image is converted to a gray-scaled image. A first pixel value of a first pixel, a second pixel value of a second pixel and a third value of a third pixel are identified. A first difference between the first pixel value and the second pixel value and a second difference between the second pixel value and the third pixel value are determined. The first difference is compared with the second difference. A number of occurrences of each pixel difference are determined. A threshold difference is identified that corresponds to a dynamic threshold value of the number of occurrences. Each difference that is greater than the threshold difference is determined to be an edge pixel.