Tattoo Detection Using Block Segmentation and Statistical Features
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Solution Overview
Problem
Tattoo detection systems face inaccuracies in low-quality images due to variations in illumination and other complicating factors, leading to suboptimal performance in automatic tattoo recognition.
Innovation Solution
The system employs a method that processes local and global information within an input image to accurately segment and detect tattoos, using a trained classifier to calculate confidence scores for statistical features and generate bounding boxes for candidate tattoo regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If skin-based detection techniques are used for tattoo segmentation, then the system can identify candidate tattoo regions, but the accuracy deteriorates in low quality images due to variations in illumination
Solution Approach 1:
The patent divides the image into multiple blocks and processes each block independently to calculate statistical features. This segmentation approach allows the system to handle local variations in illumination and image quality, improving segmentation accuracy in low quality images while maintaining overall detection capability.
Solution Approach 2:
The patent calculates different statistical features for each block based on local characteristics such as color distribution, texture, and illumination conditions. By adapting the feature extraction to local block properties rather than applying uniform processing, the system maintains accuracy across varying illumination conditions.
2Productivity
If only local information is used for tattoo segmentation, then processing is faster, but global geometric relationships between candidate regions are not utilized
Solution Approach 1:
The patent combines local block-level statistical features with global geometric relationships between candidate tattoo regions. By merging these two levels of information, the system achieves both fast local processing and accurate global classification, utilizing spatial arrangements and geometric patterns of candidate regions to improve overall detection accuracy.
3Measurement precision
If a trained classifier processes multiple statistical features for each block, then confidence scores improve accuracy, but computational complexity increases
Solution Approach 1:
The patent segments the image into blocks and processes each block independently through the classifier. This segmentation reduces the computational burden on the classifier by processing smaller, localized regions rather than the entire image at once, while still achieving high accuracy through aggregated block-level confidence scores.
Solution Approach 2:
The patent extracts multiple statistical features (color distribution, texture, illumination characteristics) for each block and feeds them to the classifier. By changing from simple pixel-based analysis to multi-parameter statistical feature analysis, the system achieves higher detection accuracy while the block-wise processing keeps computational complexity manageable.
Data Source
AI summary
In some implementations, a computer-implemented method is capable of automatically segmenting and detecting a tattoo within an image. An image may be initially obtained. A block coverage pattern that identifies multiple blocks within the obtained image may be determined. A set of processing operations may then be performed for each block. The processing operations may include calculating a plurality of statistical features. A confidence score reflecting a likelihood that at least a portion of the block includes a predetermined graphical attribute associated with tattoos may be calculated. A subset of the multiple blocks of the image that have a respective confidence score greater than a predetermined threshold value may be identified. A portion of the image that includes one or more blocks from among the subset of the multiple blocks may then be determined to correspond to a tattoo.