Image Material Identification Using Joint-Free Regions
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Solution Overview
Problem
Conventional image identification devices face reduced accuracy when identifying materials joined together due to the presence of joints, leading to false identifications, and requiring users to capture images without joints is inconvenient.
Innovation Solution
An image identification device that sets areas in an image, determines the presence of joints, and uses a discriminative model trained on images without joints to identify the type of materials in areas without joints, employing area repositioning and joint detection techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional devices identify materials by processing images including joints, then the device can handle real-world scenarios with joined materials, but the identification accuracy decreases due to false identifications caused by joints
Solution Approach 1:
The image processing system segments the input image into multiple candidate areas, each potentially containing material information. By dividing the image into regions and evaluating each separately, the system can identify areas without joints and prioritize those for accurate material identification, thus resolving the contradiction between handling jointed materials and maintaining accuracy
Solution Approach 2:
The system applies different processing qualities to different regions of the image. Areas without joints receive higher quality processing and are prioritized for identification, while areas with joints are either excluded or processed with lower priority. This local differentiation allows the system to maintain high accuracy while still handling images containing joined materials
2Measurement precision
If users capture images without joints to ensure accurate identification, then the identification accuracy improves, but the ease of operation decreases as users must carefully position and capture images
Solution Approach 1:
The system performs self-service by automatically analyzing the captured image to identify areas without joints and selecting appropriate candidate areas for material identification. This automation eliminates the need for users to manually ensure joint-free images, thereby maintaining high identification accuracy while significantly improving ease of operation
Solution Approach 2:
The system performs preliminary analysis of the captured image to identify and mark areas without joints before proceeding to material identification. This preliminary action ensures that only suitable areas are used for identification, maintaining accuracy while allowing users to capture images freely without worrying about joint positions
Data Source
AI summary
An image identification device includes a first determiner that determines whether each of one or more areas in an image includes a joint between two first materials, and an identifier that identifies the type of a first material by using a first discriminative model and outputs type information indicating the type of the first material, the type of the first material being identified by the first discriminative model receiving input on an area that has been determined by the first determiner as an area without the joint between the two first materials, out of the one or more areas in the image, the first discriminative model being created on the basis of first learning images that are images of different types of materials including the first material, the first learning images being images without a joint between two first materials.