Optical Object Recognition Reference Image Acquisition
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Solution Overview
Problem
Existing optical object recognition systems face challenges in efficiently obtaining reference images that isolate target objects from environmental noise and interference, leading to suboptimal recognition accuracy.
Innovation Solution
A method involving a display panel with driven light sources, an object recognition sensor, and environmental sensors to capture reference images without the target object, and then selecting an effective image by matching environmental conditions, allowing for robust object image restoration and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference images are obtained using all light sources in the display panel, then the reference images contain comprehensive environmental information, but the complexity of selecting the appropriate reference image increases and recognition accuracy decreases due to environmental variations
Solution Approach 1:
Environmental information is obtained in advance and stored together with reference images. This preliminary action allows the system to pre-process and organize data so that during object recognition, the already-stored environmental information can be quickly compared with current environmental conditions to select the most appropriate reference image, avoiding complex real-time analysis
Solution Approach 2:
The system obtains current environmental information during object recognition and compares it with stored environmental information from multiple reference images. This feedback mechanism enables dynamic selection of the reference image that best matches current conditions, ensuring optimal recognition accuracy while maintaining manageable complexity through systematic comparison
2Measurement precision
If reference images are obtained without considering environmental conditions, then the acquisition process is simple and fast, but recognition accuracy deteriorates due to environmental noise and interference
Solution Approach 1:
The system merges the reference image acquisition process with environmental information measurement by obtaining both simultaneously using the same light sources and sensor. This combining approach ensures that each reference image is paired with its corresponding environmental data without requiring separate measurement steps, maintaining acquisition efficiency while improving recognition accuracy through environmental context
Solution Approach 2:
Environmental information is measured and stored in advance together with reference images during the acquisition phase. This preliminary preparation of environmental data eliminates the need for complex post-processing or real-time environmental analysis during object recognition, maintaining high productivity while ensuring accurate environmental compensation
3Measurement precision
If environmental sensors are added to measure and match environmental conditions, then recognition accuracy improves through environmental compensation, but device complexity increases
Solution Approach 1:
The display panel's light sources and the object recognition sensor serve multiple functions: they are used both for displaying information and for obtaining reference images and measuring environmental information. This multi-functionality allows environmental compensation without adding dedicated hardware, improving recognition accuracy while minimizing increases in device complexity
Solution Approach 2:
The system uses its own existing components (display panel light sources and object recognition sensor) to perform environmental measurement and reference image acquisition. This self-service approach eliminates the need for entirely separate environmental sensing systems, achieving environmental compensation with minimal additional complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient optical object recognition by isolating pure object information from noise and interference, enhancing recognition accuracy and reliability.
Implementation Method 1
receiving, using an object recognition sensor, light reflected off a first target object through the object recognition window
Data Source
AI summary
Various example embodiments are directed towards systems, apparatuses, and/or methods of obtaining a reference image for optical object recognition, the method including driving a subset of light sources of a plurality of light sources, receiving, using an object recognition sensor, light reflected off a first target object, obtaining a first reference image based on the reflected light, obtaining a first target image associated with the first target object based on the reflected light, obtaining at least one first environment information using at least one environmental sensor while driving the subset of light sources, the first environment information associated with a surrounding environment, storing the first reference image and the first environment information together, and obtaining a first effective image for the first target object based on the first target image and the first reference image.