Arrow-Guided Region of Interest Specification for Image Analysis
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Solution Overview
Problem
Creating correct answer data for training object detection or segmentation models requires time-consuming manual specification of bounding boxes or masks for regions of interest in images, which is inefficient.
Innovation Solution
An image analysis apparatus that uses an arrow to specify a region of interest, generating region-of-interest candidates based on the arrow's direction and distance, calculates an interest degree for each candidate, and selects the region of interest, facilitating the creation of correct answer data for model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual specification of bounding boxes or masks is used to create correct answer data, then the accuracy of region of interest annotation is improved, but the time consumption increases significantly
Solution Approach 1:
The system enables automatic generation of correct answer data by having the learning model predict region of interest candidates from images with arrows, and then automatically calculating interest degrees and selecting regions, eliminating the need for manual annotation while maintaining accuracy through automated evaluation and selection processes
Solution Approach 2:
The system changes the parameter representation from manual bounding box/mask coordinates to automated interest degree scores, allowing the model to evaluate and rank region candidates based on calculated interest degrees rather than requiring precise manual coordinate specification
2Productivity
If automated methods are used to generate correct answer data, then the productivity is improved, but the annotation precision may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the learning model predicts region candidates, the system calculates interest degrees based on arrow directions and distances, and then uses this feedback to select and refine region specifications, iteratively improving accuracy while maintaining high productivity
Solution Approach 2:
The system performs preliminary actions by generating multiple region-of-interest candidate predictions before final selection, allowing automated evaluation and refinement of candidates based on interest degree calculations, ensuring high accuracy is achieved through pre-screening and selection rather than direct single-step annotation
Data Source
AI summary
An image analysis apparatus, an image analysis method, and a program for specifying a region of interest from an image in which an arrow is assigned to the region of interest are provided.The above problem is solved by an image analysis apparatus including at least one processor, and at least one memory in which an instruction to be executed by the at least one processor is stored, in which the at least one processor is configured to receive an image in which an arrow is assigned to a region of interest, specify the arrow, dispose one or more region-of-interest candidates that are candidates of the region of interest in accordance with a direction of the arrow and with a distance from the arrow, calculate an interest degree for each region-of-interest candidate, and specify the region of interest from among the region-of-interest candidates based on the interest degree.