Image Region Selection Using Previous Action Data
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Solution Overview
Problem
Existing image processing techniques are inaccurate for delineating regions of interest with complex shapes and require specific parameters for image features, making them inefficient and limiting user capabilities, especially in achieving near-pixel level precision with touchscreen interfaces.
Innovation Solution
A method and apparatus that receive user input data and previous action data to determine candidate actions for image processing, using an associative memory and processor to provide output indicating the intended action, which adapts to user feedback and integrates previous experiences for improved precision and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized input devices or multiple user inputs are used to achieve near-pixel level precision, then measurement precision is improved, but ease of operation deteriorates and productivity decreases
Solution Approach 1:
The system automatically determines the user's intended action by analyzing the user input and comparing it with previous action data, eliminating the need for the user to manually specify precise parameters or navigate through multiple selection steps. The system serves itself by inferring intent from the input pattern alone.
Solution Approach 2:
The system uses previous action data as feedback to improve the determination of current user intent. By analyzing historical interactions between user inputs and subsequent actions, the system learns to more accurately predict what the user intends to do, thereby achieving high precision without requiring complex user input.
2Measurement precision
If multiple user inputs are used to iteratively zoom in on a region, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary analysis by storing and analyzing previous action data before the user makes their current selection. This preparation work is done in advance, allowing the system to quickly determine user intent from a single input without requiring iterative zooming or multiple selection steps, thereby maintaining high precision while improving productivity.
3Ease of operation
If edge snapping techniques or region growing algorithms are used, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system automatically determines the most appropriate region delineation method and parameters by analyzing the user input and previous action data, eliminating the need for users to manually configure algorithm-specific parameters. The system self-adjusts to achieve accurate region delineation while maintaining ease of operation.
Solution Approach 2:
The system uses previous action data to learn which region delineation methods and parameters work best for different types of inputs and images. This feedback mechanism allows the system to automatically adjust its processing to achieve high accuracy without requiring users to understand or configure complex algorithm parameters.
4Manufacturing precision
If parameters are configured for specific image features, then manufacturing precision is improved, but adaptability deteriorates
Solution Approach 1:
The system uses a universal approach by analyzing previous action data across different images and features to learn general patterns of user intent. Instead of requiring separate parameter configurations for each image feature, the system develops a multi-functional capability to accurately determine user intent across various image types and features, thereby achieving both precision and adaptability.
Data Source
AI summary
A method is for processing a user input in relation to an image. In an embodiment, the method includes receiving first input data derived from user input in relation to a first image, the first input data indicating a selection of at least a part of the first image; performing a determination process to determine, at least partly based upon the first input data received and previous action data, one or more candidate actions to perform in relation to the first image, the previous action data relating to previous image processing actions performed in relation to one or more images; and providing output data indicating at least a first action of the one or more candidate actions determined.