Tiered Item Identification Using Weight Classification
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Solution Overview
Problem
Current systems for tracking and identifying items in inventory management within materials handling facilities face inefficiencies due to the need to compare images of items against a vast database, leading to increased processing time and reduced accuracy.
Innovation Solution
Implementing a system that uses weight determining elements to categorize items into weight classes, combined with image processing techniques such as image matching, edge detection, and deep learning algorithms, to quickly and accurately identify items by comparing images only within similar weight classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image comparison is performed against a vast database of all items, then comprehensive item identification is achieved, but processing time increases and accuracy decreases
Solution Approach 1:
The patent segments the vast item database into multiple weight classes based on weight ranges. Instead of comparing images against all items in the database, the system first determines the weight of the item using weight determining elements, identifies the corresponding weight class, and then performs image comparison only within that specific weight class. This segmentation dramatically reduces the number of comparisons needed, thereby reducing processing time while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary weight determination and weight class identification before conducting image comparison. By pre-categorizing items into weight classes and using weight as a filtering criterion first, the system eliminates the need to compare images against the entire database. This preliminary action based on weight significantly reduces the search space for image matching, improving both speed and accuracy.
2Reliability
If image processing algorithms are applied to all candidate items, then thorough identification is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the candidate item set into weight-based groups. Image processing algorithms are then applied only to items within the relevant weight class rather than to all candidate items. This reduces computational complexity while maintaining reliability because the weight filter ensures that the algorithm processes only a relevant subset of items.
Solution Approach 2:
The system changes the parameter used for filtering candidate items from purely image-based to weight-based first. By using weight as a primary filtering parameter before applying image processing, the system reduces the number of items that require complex algorithmic analysis, thereby reducing processing complexity while maintaining identification reliability through the combined use of weight and image data.
Data Source
AI summary
This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.