Image Processing Apparatus Subject Inference Segmentation
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Solution Overview
Problem
Current technologies lack the ability to effectively infer the image capture subject when a person generates an image including a product, which is crucial for understanding the influence on consumer behavior from physical articles and media images.
Innovation Solution
An image processing apparatus and method that acquires captured images containing products, processes them to generate subject inference data indicating the type of image capture subject, and outputs this data to assist in determining the context and potential purchase locations of the product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is performed to infer image capture subjects, then the ability to estimate influence on consumer behavior is improved, but the complexity of the processing system increases
Solution Approach 1:
The image processing apparatus divides the inference task into separate functional modules: an image acquisition unit that captures images, an inference unit that processes images and infers subject types, and an output unit that delivers results. This segmentation allows each component to be optimized independently while working together to achieve accurate inference without requiring a monolithic complex system.
Solution Approach 2:
The inference unit acts as an intermediary between the image acquisition unit and the output unit. It receives raw images, processes them through inference algorithms, and generates subject inference data. This intermediary layer simplifies the overall system architecture by centralizing the complex inference logic in a dedicated module that can be independently developed and maintained.
2Adaptability or versatility
If comprehensive image processing is performed to determine purchase locations, then the usefulness for marketing analysis is improved, but the processing time increases
Solution Approach 1:
The system performs partial action by focusing the inference process on extracting specific useful information (subject type, purchase location) from images rather than analyzing every possible aspect. The inference unit processes only the necessary image regions and features required for marketing analysis, avoiding unnecessary processing time while maintaining high adaptability for various marketing scenarios.
Solution Approach 2:
The image acquisition unit captures images with pre-established parameters optimized for inference, and the inference unit processes images using pre-trained models. This preliminary preparation of processing parameters and models enables rapid analysis without time-consuming computations during actual marketing analysis tasks.
Data Source
AI summary
An image processing apparatus (10) includes an image acquisition unit (110), an inference unit (120), and an output unit (130). The image acquisition unit (110) acquires an image from a terminal (20). This image includes a product in a part of its area. The inference unit (120) processes the image acquired by the image acquisition unit (110), and thereby generates subject inference data indicating an inference result of a type of an image capture subject. For example, the inference unit (120) processes an area around the product in the image, and thereby generates the subject inference data. The output unit (130) performs output based on the subject inference data.