Low Resolution Item Identification via Image Quantization
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Solution Overview
Problem
Current product identification processes using images are computationally and memory-intensive due to the large amount of data required to compare unknown items against a database of known items, exceeding 100,000 items, which necessitates significant processing power and storage, and are vulnerable to fraud techniques applicable to optical code systems.
Innovation Solution
The process involves capturing and quantizing images to reduce data, using a signal-to-noise ratio (SNR) comparison between unknown and known items' quantized image data, and preselecting a subset of known items to efficiently identify unknown items with high confidence, thereby reducing computational time and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current product identification processes compare image data for unknown items against a database of known items, then item identification accuracy is improved, but computational time and memory requirements increase significantly
Solution Approach 1:
The patent segments the image data into discrete pixel values and further processes them through quantization to create a simplified representation. By dividing the image into individual pixels and then grouping them into quantized bins, the system maintains identification accuracy while reducing computational complexity. This segmentation allows efficient comparison by working with discrete, quantized values rather than continuous pixel data.
Solution Approach 2:
The patent changes the parameter representation of image data by applying quantization transformations. Instead of storing and comparing full-resolution pixel values, the system transforms pixel values into quantized bins, changing the parameter space from high-dimensional continuous values to lower-dimensional discrete values. This parameter change dramatically reduces memory requirements and computational time while preserving the ability to distinguish between different items.
2Measurement precision
If full-resolution image data is stored for each known item in the database, then identification accuracy is improved, but memory requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information from full-resolution images by applying quantization. Instead of storing all pixel data, the system extracts representative quantized values that capture the essential visual characteristics needed for identification. This extraction process removes redundant information while retaining the key features necessary for accurate item identification, significantly reducing storage requirements.
Solution Approach 2:
The patent uses simplified quantized representations instead of storing expensive full-resolution images. The quantized data acts as a disposable, low-cost approximation that suffices for identification purposes. By replacing high-memory-consuming full-resolution images with compact quantized representations, the system achieves the same identification functionality with minimal storage resources.
3Productivity
If optical code scanning is used for item identification, then processing speed is improved, but vulnerability to fraud increases
Solution Approach 1:
The patent uses visual copying of item appearance through image capture and quantization instead of relying on optical codes. By creating a visual representation (copy) of the item's actual appearance and comparing it against stored quantized images, the system maintains fast processing while adding fraud resistance. The visual copy approach verifies the actual item rather than just reading a code that can be easily replicated or tampered with.
Data Source
AI summary
Methods and apparatus are provided for low resolution item identification. An image of an unknown item is captured and quantized to greatly lower the resolution of the image. The quantized image data of the unknown item is compared to a plurality of the quantized image data for known items. The comparison includes using a signal-to-noise ratio calculated using the quantized image data for both the unknown and known items. A match is found when the calculated signal-to-noise ratio is above a predetermined threshold value.