Bulk Image Gathering System Automates Sub-Image Splitting
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Solution Overview
Problem
Current digital image capture technologies are inefficient and error-prone when capturing and processing bulk images of items like sports cards, stamps, and coins, as they require manual splitting and naming of sub-images, and maintaining associations between front and back sides of items.
Innovation Solution
A system and method for bulk image gathering using a computer, scanner, and software application that captures images, splits them into sub-images based on templates, and associates metadata, allowing for automatic flipping and naming, with the ability to handle multiple scanners and image sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital image capture technologies are used to capture bulk images, then multiple items can be captured in a single image, but the process becomes tedious and time-consuming due to manual splitting and naming of sub-images
Solution Approach 1:
The patent applies segmentation by automatically dividing captured bulk images into individual sub-images based on detected item boundaries. The system identifies and separates multiple items within a single captured image, assigning each sub-image a unique identifier and metadata, thereby eliminating manual splitting operations.
Solution Approach 2:
The system performs self-service by automatically naming and tagging sub-images with meaningful identifiers and associated data without requiring manual intervention. The software application autonomously generates filenames, organizes files into appropriate folders, and attaches metadata, allowing the system to serve itself rather than relying on user actions.
2Productivity
If digital image capture technologies are used to capture bulk images, then multiple items can be captured in a single image, but errors increase due to manual processing
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors and adjusts the image splitting and naming processes based on detected patterns and user corrections. The software learns from user interactions and automatically refines its splitting algorithms, providing feedback loops that improve accuracy over time and reduce errors in sub-image processing.
3Ease of operation
If manual tools are used to split and name sub-images, then individual processing is possible, but the process becomes tedious and complex
Solution Approach 1:
The patent merges multiple manual operations into a single automated process. The system combines image splitting, naming, folder organization, and metadata attachment into one integrated workflow that executes automatically after bulk image capture, eliminating the need for separate manual steps and reducing operational complexity.
Solution Approach 2:
The software application acts as an intermediary between the captured bulk images and the final organized sub-images. It mediates the transformation process by automatically detecting item boundaries, generating appropriate filenames, creating folder structures, and attaching metadata, thereby simplifying the user's interaction with complex image processing tasks.
4Loss of information
If bulk images are captured and split into sub-images, then individual item images are obtained, but maintaining associations between front and back sides becomes complicated
Solution Approach 1:
The patent applies universality by creating a centralized database structure that handles multiple functions simultaneously. The system stores front and back images along with their associations, item metadata, and organizational information in a unified database that can retrieve and manage all related data through single operations, eliminating the need for separate tracking systems.
Data Source
AI summary
A system and method for gathering bulk images are provided herein.