Texture Histogram Calculator for Image Search
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Solution Overview
Problem
Current electronic devices face challenges in efficiently analyzing information due to increased demands for processing power and hardware resources, leading to economic impacts and operational inefficiencies, particularly when handling complex digital image data.
Innovation Solution
A system and method that employs a texture histogram calculator to convert images into grey-scale, divide them into pixel blocks, perform Discrete Fourier Transform, and generate horizontal and vertical texture histograms to detect similar images by comparing peak values across these histograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enhanced device functionality and performance are implemented to analyze complex digital image data, then image analysis capability is improved, but processing power requirements and hardware resources increase
Solution Approach 1:
The image is divided into multiple pixel blocks, and the Discrete Fourier Transform is applied to each block separately. This segmentation approach enables efficient processing of large images by breaking them into manageable chunks, improving image analysis capability without requiring excessive processing power for the entire image at once.
Solution Approach 2:
The patent extracts only the essential texture information by computing DFT coefficients and identifying peak values in the frequency domain. By focusing only on the most significant frequency components that represent texture characteristics, the system achieves accurate image analysis while minimizing the computational resources needed compared to processing all image data.
2Adaptability or versatility
If enhanced device capability to perform advanced operations is implemented, then device functionality is improved, but control and management of device components becomes more demanding
Solution Approach 1:
The patent introduces texture histograms as an intermediary representation between the original image and the final analysis results. The histograms aggregate DFT peak values from multiple pixel blocks into a compact statistical representation, simplifying the control and management of image data while maintaining the enhanced capability to perform advanced texture analysis operations.
3Measurement precision
If more system processing power is allocated to analyze information, then analysis accuracy is improved, but production costs and operational inefficiencies increase
Solution Approach 1:
The patent extracts only the most significant DFT coefficients (peak values) that represent texture characteristics, discarding redundant frequency information. This selective extraction maintains high analysis accuracy for texture-based image comparison while significantly reducing the computational energy required compared to processing the complete frequency spectrum.
Solution Approach 2:
The patent discards redundant image data by converting to grayscale (discarding color information not essential for texture analysis) and by selecting only peak DFT coefficients. This selective discarding of non-essential information reduces processing energy while preserving the critical texture features needed for accurate image analysis.
Data Source
AI summary
A system and method are disclosed for performing an image search procedure in which a histogram calculator initially performs image analysis procedures to construct horizontal and vertical texture histograms corresponding to texture characteristics of subject images. A feature detector may then perform the image search procedure by comparing the texture histograms to identify one or more matching images in which texture characteristics are most similar.