Watermark Detection in Image Subareas
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Solution Overview
Problem
Camera-based systems in retail and industrial settings face challenges in efficiently processing and identifying machine-readable indicia, such as digital watermarks, due to limited processing time and resources, especially when dealing with composite images captured from multiple viewpoints.
Innovation Solution
The technology analyzes reference imagery to determine high-probability areas for decodable watermark data, classifies image subareas as content or background, and triggers distortion correction or signal decoding, reducing processing time by focusing on specific areas identified as 'interesting locations' within the image frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the camera system processes the entire image frame for watermark detection, then the detection completeness is improved, but the processing time and resource consumption increase significantly
Solution Approach 1:
The patent divides the image frame into multiple subareas (e.g., 4 quadrants or regions) and processes only the subareas that contain content objects, excluding background regions. This segmentation approach maintains detection completeness for watermarks while significantly reducing processing time by avoiding unnecessary analysis of empty background areas.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their content characteristics. Content-containing subareas receive full watermark detection processing, while background subareas are skipped or processed minimally. This local quality approach optimizes processing efficiency by tailoring computational effort to actual needs.
2Measurement precision
If the camera system analyzes all image areas for watermark data, then the detection accuracy is improved, but the processing resources consumed increase
Solution Approach 1:
The patent segments the image into subareas and uses content detection to identify which subareas contain objects of interest. Only these content subareas are subjected to thorough watermark analysis, while background subareas are excluded from processing. This maintains detection accuracy for relevant regions while reducing overall computational resource consumption.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the image (content subareas) rather than the entire image frame. This partial processing approach achieves sufficient detection accuracy for practical purposes while significantly reducing processing resource consumption compared to full-image analysis.
3Reliability
If the system processes composite images from multiple viewpoints, then the product identification reliability is improved, but the processing complexity increases
Solution Approach 1:
The patent processes composite images by dividing them into subareas corresponding to different viewpoints or regions. Content detection is performed on each subarea independently, and only subareas containing relevant content are processed for watermarks. This segmentation reduces processing complexity by breaking down the complex task of analyzing multi-viewpoint composite images into smaller, manageable units.
Solution Approach 2:
The patent applies local quality by treating different subareas of composite images differently based on their content characteristics. Subareas containing product content receive full processing, while background subareas are skipped. This approach maintains product identification reliability through comprehensive analysis of relevant regions while reducing overall processing complexity through selective processing.
Data Source
AI summary
The present technology relates to image signal processing. One aspect of the present technology involves analyzing reference imagery gathered by a camera system to determine which parts of an image frame offer high probabilities of—relative to other image parts—containing decodable watermark data. Another aspect of the present technology whittles-down such determined image frame parts based on detected content (e.g., a cereal box) vs expected background within such determined image frame parts.