Image Capturing System Custom Dictionary Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image capturing devices lack the ability to detect specific subjects desired by users, as they rely on pre-defined dictionary data and network structures that are not easily customizable, limiting their flexibility and accuracy in subject detection.
Innovation Solution
An image capturing system that includes a teacher data input mechanism, network structure specification, and dictionary data generation capabilities, allowing users to input teacher data and specify network constraints to generate custom dictionary data for subject detection, enabling the system to detect any desired subjects and perform precise image capturing control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-defined dictionary data and network structures are used, then device complexity is reduced, but subject detection accuracy and flexibility for user-specific needs deteriorates
Solution Approach 1:
The system automatically generates custom dictionary data by acquiring images containing target subjects, extracting feature amounts from these images, and creating dictionary data without requiring manual user intervention. This self-service mechanism resolves the contradiction by automating the previously complex manual process while maintaining high detection accuracy through user-specific customization.
Solution Approach 2:
The system performs preliminary actions by acquiring and processing images to generate dictionary data before actual subject detection tasks. By pre-extracting feature amounts from images containing target subjects and creating dictionary data in advance, the system prepares customized detection resources that balance device complexity with detection accuracy.
2Measurement precision
If additional learning is performed to detect new subjects, then subject detection accuracy improves, but device complexity and difficulty in implementing additional learning increases
Solution Approach 1:
The system enables users to perform additional learning for new subjects through a simplified self-service process. Users only need to provide images containing the new target subjects, and the system automatically extracts features and generates dictionary data, eliminating the need for users to understand or implement complex additional learning procedures while maintaining high detection accuracy.
Solution Approach 2:
The system acts as an intermediary between user-provided images and the subject detection model. It mediates the complex process of additional learning by automatically processing images, extracting features, and generating dictionary data that can be integrated into the detection system without requiring users to directly implement additional learning algorithms.
3Measurement precision
If custom dictionary data is generated for specific subjects, then subject detection accuracy improves, but the burden of preparing teacher data increases
Solution Approach 1:
The system eliminates the user burden of preparing teacher data by implementing a self-service mechanism where users simply provide images containing target subjects. The system then automatically performs feature extraction and dictionary data generation, completely removing the need for users to manually prepare or process teacher data while maintaining high detection accuracy.
Solution Approach 2:
The system extracts the complex task of teacher data preparation from the user's responsibilities. By separating the simple image provision task from the complex feature extraction and dictionary generation processes, the system takes out the burdensome preparation work and automates it, leaving users with only the simple task of providing images.
4Ease of operation
If pre-defined dictionary data is used, then ease of operation is improved, but adaptability to detect any desired subject deteriorates
Solution Approach 1:
The system achieves universality by combining the simplicity of pre-defined dictionary usage with the flexibility of custom dictionary generation. Users can easily operate with pre-defined dictionaries for common subjects, and when needed, the same simple process of providing images extends to generating custom dictionaries for any desired subject, making the system both easy to operate and highly adaptable.
Solution Approach 2:
The system implements dynamics by allowing the dictionary data to transition from pre-defined static states to custom-generated states based on user needs. The same operational interface and simple image-provision process dynamically adapt whether generating dictionaries for common or custom subjects, maintaining ease of operation while enabling versatility.
Data Source
AI summary
An image capturing system configured to perform subject detection based on a neural network includes a teacher data input means, a network structure specification means, a dictionary generation means, and an image capturing device. The teacher data input means is configured to input teacher data for the subject detection. The network structure specification means is configured to specify a constraint on the network structure for the subject detection. The dictionary generation means is configured to generate dictionary data for the subject detection based on the teacher data and the constraint on the network structure. The image capturing device is configured to perform the subject detection based on the dictionary data generated by the dictionary generation means and perform predetermined image capturing control on a subject detected by performing the subject detection. The dictionary data includes, as header information, information regarding a count of teacher data used to generate the dictionary data.