Imaging System Dictionary Generation for Arbitrary Object Detection
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Solution Overview
Problem
Existing imaging devices using machine learning methods, such as neural networks, face limitations in detecting arbitrary objects due to restricted network structures and the need for additional learning, making it difficult to adapt to user-specific object detection and imaging control.
Innovation Solution
An imaging system that includes a processor or circuit with a training data input unit, network structure designation unit, and dictionary generation unit, allowing for the generation of dictionary data based on training data and network structure restrictions, enabling object detection and imaging control on arbitrary objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additional learning is performed to detect arbitrary objects, then object detection capability is improved, but device complexity and network structure restrictions increase
Solution Approach 1:
The patent segments the object detection system into two independent components: a pre-trained neural network for feature extraction and a separate dictionary learning module for custom object adaptation. This segmentation allows the system to avoid retraining the entire network while still achieving arbitrary object detection, thereby reducing device complexity while maintaining adaptability.
Solution Approach 2:
The patent performs preliminary training of the neural network on general object categories before deployment. This pre-training establishes a robust feature extraction capability that can be later adapted to arbitrary objects through dictionary learning without requiring full retraining, thus reducing the complexity burden of adaptability.
2Measurement precision
If dictionary data is switched according to situations, then detection accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements automatic dictionary selection based on situational context. The system autonomously determines which dictionary data to use by analyzing the input image characteristics and matching them with appropriate pre-loaded dictionaries, eliminating the need for manual user selection while maintaining high detection accuracy across different situations.
3Adaptability or versatility
If arbitrary user-specific object detection is enabled, then adaptability is improved, but training data requirements and processing time increase
Solution Approach 1:
The patent uses dictionary learning to create compressed representations (copies) of training data that capture essential features of user-specific objects. Instead of storing and processing large amounts of raw training images, the system learns compact dictionary matrices that can be quickly applied for detection, dramatically reducing training time while maintaining adaptability to arbitrary objects.
Solution Approach 2:
The patent transforms the object detection problem from learning complex image transformations to learning parameters of dictionary matrices. By changing the problem parameters from pixel-level operations to matrix-factorization parameters, the system achieves user-specific object detection with significantly reduced computational time and training data requirements.
Data Source
AI summary
An imaging system that performs object detection on the basis of a neural network includes: training data inputting unit configured to input training data for the object detection; network structure designation unit configured to designate a restriction of a network structure in the object detection; dictionary generation unit configured to generate dictionary data for the object detection on the basis of the training data and the restriction of the network structure; and an imaging device configured to perform the object detection on the basis of the dictionary data generated by the dictionary generation unit and performs predetermined imaging control on an object detected through the object detection.