Texture Exemplar Generation via Heat Mapping
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Solution Overview
Problem
Conventional methods for extracting texture exemplars from images are inefficient, requiring significant user input and failing to achieve speed, simplicity, and accuracy in automatically identifying and isolating desired textures from unconstrained natural images.
Innovation Solution
A computer-implemented method that generates texture exemplars by receiving an image, determining a desired texture, creating a heat mapping to locate the texture, and generating tiles based on the mapping, using techniques such as Markov Random Fields and diffusion distance to produce an exemplar, which can be adjusted for scale and refined interactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional texture extraction methods are used, then texture exemplars can be obtained, but significant user input is required and the process is inefficient
Solution Approach 1:
The system performs automatic texture extraction by analyzing the input image itself to identify texture regions, eliminating the need for manual user specification. The algorithm autonomously processes the image to generate texture exemplars without requiring significant user intervention or input.
2Productivity
If manual texture transformation is used, then texture exemplars can be obtained, but the process is tedious and slow
Solution Approach 1:
The patent replaces manual mechanical transformation operations with an automated computational algorithm. The system uses image processing and analysis techniques to automatically identify and extract texture regions, substituting the tedious manual transformation process with a automated digital workflow that is both faster and simpler to operate.
3Adaptability or versatility
If inverse texture synthesis systems are used, then existing exemplars can be improved, but textures cannot be extracted from unconstrained natural images
Solution Approach 1:
The system changes the approach parameters by using heat mapping and diffusion distance calculations instead of traditional inverse texture synthesis methods. This allows the system to process unconstrained natural images with varying content and structures while maintaining accurate texture extraction through adaptive parameter adjustment and multi-scale analysis.
4Measurement precision
If texture extraction systems requiring user input are used, then desired textures can be located, but the process is complex and time-consuming
Solution Approach 1:
The system performs self-service by automatically analyzing the input image to identify and locate desired texture regions without requiring user input. The algorithm uses heat mapping and diffusion distance calculations to autonomously determine texture locations and boundaries, eliminating the need for manual specification while maintaining high localization accuracy.
Data Source
AI summary
A system, method and a computer-readable medium for creating texture exemplars from images are provided. The texture exemplars are created by receiving an image containing a plurality of pixels representing a plurality of textures, wherein each texture in the plurality of textures is configured to be selectable by a user, determining a desired texture in the plurality of textures contained within the image and defining a scale of the desired texture, generating a heat mapping of the image, wherein the heat mapping indicates location of the desired texture, generating, based on the heat mapping, a plurality of tiles corresponding to the defined scale of the desired texture, and generating an exemplar of desired texture.