Marginal Space Learning for 3D Object Detection
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Solution Overview
Problem
Detecting objects in high-dimensional spaces is computationally expensive due to the need to search and train classifiers across numerous parameters, making it challenging and time-consuming, especially when dealing with 3D similarity transformations.
Innovation Solution
The system employs marginal space learning to reduce the search space by training classifiers in progressively smaller dimensional spaces, starting from the object center location, then orientations, and finally scale, using classifiers like Probabilistic Boosting Trees and 3D Haar features to identify candidate object poses efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire high dimensional space is searched for object detection, then detection accuracy is improved, but computational cost becomes prohibitive
Solution Approach 1:
The patent segments the high-dimensional search space into multiple lower-dimensional marginal spaces. Instead of searching all 9 parameters simultaneously, the system performs sequential searches in reduced parameter spaces (e.g., position only, then position+orientation, then position+orientation+scale), significantly reducing computational cost at each stage while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the high-dimensional search problem into a series of lower-dimensional problems by changing the dimensionality of the search space. By projecting the 9-dimensional similarity transformation space into lower-dimensional marginal spaces, the system achieves tractable computation while preserving the essential detection capability through the cascade structure.
2Reliability
If multiple levels of bootstrapping are employed to handle negative examples, then classifier training is improved, but system speed decreases
Solution Approach 1:
The patent segments the classifier training process into multiple stages, each trained on a specific marginal space. This allows the system to handle negative examples efficiently at each stage without requiring multiple levels of bootstrapping, maintaining both training quality and system speed through the cascade architecture where each stage processes only relevant examples.
3Ease of operation
If coarse-to-fine strategies are used, then detection is simplified, but search time remains excessive
Solution Approach 1:
The patent implements a segmented search strategy where the search space is divided into multiple marginal spaces processed in sequence. Each stage searches a reduced parameter space, making the overall process both simple to implement and fast, as each individual search operation operates on a manageable subset of parameters rather than the full high-dimensional space.
Data Source
AI summary
A system and method for detecting an object in a high dimensional image space is disclosed. A three dimensional image of an object is received. A first classifier is trained in the marginal space of the object center location which generates a predetermined number of candidate object center locations. A second classifier is trained to identify potential object center locations and orientations from the predetermined number of candidate object center locations and maintaining a subset of the candidate object center locations. A third classifier is trained to identify potential locations, orientations and scale of the object center from the subset of the candidate object center locations. A single candidate object pose for the object is identified.