Adaptive 3D Generic Model for Object Recognition
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Solution Overview
Problem
Existing machine learning systems face challenges in efficiently recognizing objects with variable morphology due to the need for extensive manual annotation and the generation of unnecessary synthesized images, which can lead to gaps in the data set and reduced recognition rates.
Innovation Solution
A method is proposed that uses a generic three-dimensional model with defined parameters and landmarks to conform to real object images, storing variation ranges and synthesizing objects to complement the data set, thereby improving recognition rates without requiring extensive manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If synthesized images are generated from three-dimensional models to reduce manual annotation, then the initial configuration time is reduced, but the data set contains unnecessary images with unlikely morphologies that slow down recognition
Solution Approach 1:
The patent applies parameter changes by learning the distribution of morphological parameters from real image data and using this learned distribution to guide the synthesis process. This ensures that synthesized images have realistic morphologies rather than unlikely ones, resolving the contradiction between reducing manual annotation time and maintaining recognition speed by filtering out unnecessary synthesized images
Solution Approach 2:
The patent implements feedback by using real image data to learn and constrain the parameter distributions for synthesis. The system continuously refines the synthesized images based on feedback from real data characteristics, ensuring that only relevant morphologies are generated, thus improving both configuration efficiency and recognition performance
2Device complexity
If a 3D model with manageable parameters is used to reproduce object morphologies, then the model complexity is reduced, but gaps appear in the data set for objects with highly variable morphology
Solution Approach 1:
The patent learns the actual distribution ranges of morphological parameters from real image data and applies these learned distributions to the 3D model synthesis. This allows the model to cover the full range of realistic morphologies without requiring excessive parameters, resolving the contradiction between model simplicity and detection reliability
Solution Approach 2:
The patent makes the 3D model dynamic by allowing parameter variations within learned distributions rather than fixing parameters. This dynamic approach enables the model to adapt to different object instances and morphologies, improving detection accuracy while maintaining manageable complexity through statistically-guided parameter sampling
3Reliability
If manual annotation is performed to capture all morphology alternatives, then the recognition rate is improved, but the initial task becomes long and tedious
Solution Approach 1:
The patent uses copying by generating synthesized images from learned parameter distributions as substitutes for manually annotating all possible morphology alternatives. This copying approach from real data characteristics achieves comprehensive coverage of object variations without the time cost of manual annotation, resolving the contradiction between recognition rate and annotation time
Solution Approach 2:
The patent performs preliminary action by learning parameter distributions from real data before the actual recognition task. This preliminary learning phase captures all morphology alternatives automatically, eliminating the need for extensive manual annotation while ensuring high recognition rates through comprehensive data coverage
Data Source
AI summary
A method for configuring a system for recognizing a class of objects of variable morphology, comprising providing a machine learning system with an initial data set to recognize instances of objects of the class in a sequence of images of a target scene; providing a three-dimensional model specific to the class of objects, the morphology of which can be defined by a set of parameters; acquiring a sequence of images of the scene using a camera; recognizing image instances of objects of the class in the acquired image sequence; conforming the generic three-dimensional model to recognized image instances; storing variation ranges of the parameters resulting from the conformations of the generic model; synthesizing multiple three-dimensional objects from the generic model by varying the parameters in the stored variation ranges; and complementing the data set of the learning system with projections of the synthesized objects in the plane of the images.