Adaptive Image Thresholding Using Perceptual Saliency Maps
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Solution Overview
Problem
Current document binarization techniques require manual tuning of parameters and multiple iterations, leading to inefficiencies in processing and quality of scanned images, especially for degraded documents.
Innovation Solution
A computer-implemented method for adaptive thresholding that uses perceptual information to generate a context for each pixel, creating a saliency map to automatically binarize images, allowing for spatially adaptable thresholding and improved image quality without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning of parameters and multiple iterations are used, then image quality can be improved, but processing time and complexity increase
Solution Approach 1:
The system performs self-adjustment by automatically analyzing the input image characteristics and selecting appropriate binarization algorithms and parameters without requiring manual user intervention or multiple iterative adjustments, thereby maintaining high image quality while reducing processing time
Solution Approach 2:
The system dynamically changes processing parameters based on the specific characteristics of the input image, selecting from multiple binarization algorithms and adjusting parameters automatically according to the document type and image quality requirements, eliminating the need for manual parameter tuning
2Manufacturing precision
If multiple binarization iterations are performed, then image quality improves, but productivity decreases
Solution Approach 1:
The system performs preliminary analysis of the input image to determine the optimal binarization algorithm and parameters before actual processing, preventing the need for multiple iterative adjustments and enabling single-pass high-quality binarization that maintains productivity
Solution Approach 2:
The system segments the binarization process into distinct stages: image analysis, algorithm selection, parameter determination, and execution. This segmentation allows the system to efficiently process different types of documents with appropriate algorithms without requiring multiple full iterations, thereby improving processing speed while maintaining quality
3Adaptability or versatility
If manual parameter configuration is required, then adaptability to different document types improves, but ease of operation deteriorates
Solution Approach 1:
The system automatically detects document characteristics and configures appropriate parameters without user intervention, maintaining high adaptability to different document types while dramatically improving ease of operation by eliminating manual configuration requirements
Solution Approach 2:
The system incorporates multiple binarization algorithms and automatic detection capabilities that make it universally applicable to various document types (text documents, images, forms, degraded documents) without requiring separate manual configuration for each type, thereby achieving both adaptability and ease of operation
Data Source
AI summary
Techniques are described for obtaining at least one image using at least one processor. The techniques may include selecting a plurality of objects defined by a plurality of pixels within the at least one image. The techniques may include determining perceptual information associated with each of the plurality of objects and generating a context for each of the plurality of pixels that define the plurality of objects. The techniques may also include automatically thresholding the at least one image to generate an output image that represents the at least one image.