Dimensional Reduction for Real-Time Object Recognition
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Solution Overview
Problem
Current object recognition technologies are limited in scope, requiring intensive computations and failing to efficiently recognize multiple objects simultaneously due to processing every dimension for discrimination, which leads to latency issues, especially in computationally weak devices like mobile devices.
Innovation Solution
A sensor data processing system with a controlled sensing environment and an image processing engine that derives recognition traits under defined environmental states, allowing for minimal computational resources usage by identifying and adjusting environmental parameters to reduce irrelevant dimensions, thus improving recognition speed and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all descriptor dimensions are processed for discrimination, then object recognition accuracy is improved, but computational time and processing load increase significantly
Solution Approach 1:
The patent extracts and removes irrelevant or redundant descriptor dimensions from the feature vector before processing. By identifying and taking out dimensions that do not contribute meaningful discrimination power, the system reduces computational load while maintaining recognition accuracy through selective processing of only essential features.
Solution Approach 2:
The patent changes the parameter of dimensionality by transforming the original high-dimensional descriptor into a reduced-dimensional representation. Through techniques like PCA or other dimensionality reduction methods, the system reparameterizes the feature space to retain only the most informative dimensions, thereby reducing processing time without sacrificing accuracy.
2Measurement precision
If intensive computations are performed for object recognition, then recognition quality improves, but device complexity and computational requirements increase
Solution Approach 1:
The system extracts only the essential computational operations needed for recognition by removing redundant calculations. By identifying which computations are necessary for accurate recognition and eliminating unnecessary ones, the system achieves high recognition quality with reduced computational complexity suitable for mobile devices.
Solution Approach 2:
Instead of processing all possible descriptor dimensions and features exhaustively, the patent applies partial action by selectively processing only the most relevant dimensions. This approach achieves sufficient recognition accuracy without the excessive computational burden of complete processing, optimizing the balance between quality and complexity.
3Productivity
If multiple objects are recognized simultaneously, then productivity improves, but processing time for each object increases due to computational load
Solution Approach 1:
The patent segments the object recognition task into independent processing of individual objects after dimensional reduction. By first reducing the dimensionality of features for all objects in an image, then processing each object independently with the reduced feature set, the system can efficiently recognize multiple objects simultaneously without excessive processing time for each individual object.
Solution Approach 2:
The system performs preliminary dimensionality reduction on the descriptor data before executing the main object recognition processing. This preliminary action prepares the data in advance by removing redundant dimensions, so that subsequent processing of multiple objects benefits from the pre-reduced feature space, enabling faster simultaneous recognition of multiple objects.
Data Source
AI summary
A sensor data processing system and method is described. Contemplated systems and methods derive a first recognition trait of an object from a first data set that represents the object in a first environmental state. A second recognition trait of the object is then derived from a second data set that represents the object in a second environmental state. The sensor data processing systems and methods then identifies a mapping of elements of the first and second recognition traits in a new representation space. The mapping of elements satisfies a variance criterion for corresponding elements, which allows the mapping to be used for object recognition. The sensor data processing systems and methods described herein provide new object recognition techniques that are computationally efficient and can be performed in real-time by the mobile phone technology that is currently available.