Image Processing Apparatus for Adaptive Object Detection Parameter Optimization
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Solution Overview
Problem
Current image recognition systems face challenges in setting optimal image process parameters for detecting objects, as these parameters depend on the target object, environmental conditions, and require user experience or trial-and-error, leading to inefficiencies and inaccuracies, especially when training and input images are captured under different conditions.
Innovation Solution
An image processing apparatus that includes a learning unit to create a dictionary for object detection based on initial image process parameters, a detection unit to detect objects using this dictionary, and determination units to optimize both training and input image process parameters separately, allowing for accurate parameter setting without user-provided ground truth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image process parameters are set by user experience or trial and error, then the parameters can be adjusted for different target objects and conditions, but the user burden increases and the setting process becomes time-consuming
Solution Approach 1:
The system automatically determines optimal image process parameters by analyzing detection results and comparing them with ground truth, eliminating the need for manual user configuration. The parameter determination unit autonomously adjusts parameters based on performance metrics, making the system self-configuring and reducing user burden while maintaining adaptability to different objects and conditions
Solution Approach 2:
The system implements a feedback loop where detection results are compared with ground truth, and the comparison information is used to automatically adjust image process parameters. This closed-loop feedback mechanism enables continuous optimization of parameters without manual intervention, resolving the contradiction between adaptability and ease of operation
2Ease of operation
If the same image process parameters are applied to both training images and input images, then the system is simpler to operate, but detection accuracy decreases when images are captured under different conditions
Solution Approach 1:
The system separates the parameter optimization process into two independent stages: one for training images and another for input images. The first parameter determination unit optimizes parameters for training images, while the second parameter determination unit optimizes parameters for input images based on detection results. This segmentation allows each stage to be optimized independently, improving overall detection accuracy while maintaining operational simplicity through automated parameter setting
Solution Approach 2:
The system applies different image process parameters to training images and input images, recognizing that each type of image has specific requirements. By allowing localized parameter optimization for each image type rather than using a uniform parameter set, the system achieves higher detection accuracy while the automated determination process keeps the overall system simple to operate
3Extent of automation
If ground truth is provided by the user for parameter optimization, then the parameter determination process can proceed, but the operation becomes complicated and the reliability of determined parameters decreases due to subjective errors
Solution Approach 1:
The system automatically generates and utilizes ground truth through its own detection mechanism rather than relying on user-provided ground truth. The parameter determination unit compares detection results with automatically generated ground truth, enabling the system to self-validate and self-optimize parameters. This eliminates subjective user errors and improves parameter reliability while maintaining high automation
Solution Approach 2:
The system implements a feedback mechanism where detection results are automatically compared with ground truth generated by the system itself. This internal feedback loop enables continuous parameter optimization without external user input, improving both automation extent and parameter reliability by eliminating subjective errors in ground truth provision
Data Source
AI summary
A dictionary for detection of an object is created from an image obtained by performing an image process, which depends on a first image process parameter, on the training image of the detection target object. The dictionary created based on the image process depending on the first image process parameter is determined, based on a result of detecting the object from an image obtained by performing an image process, which depends on the first image process parameter, on a photographed image based on the dictionary. A second image process parameter is determined, based on a result of detecting the object from an image obtained by performing an image process, which depends on the second image process parameter, on the photographed image using the determined dictionary.