Object Orientation Detection Using Weighted Classifier Responses
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Solution Overview
Problem
Existing methods for determining the angle of orientation of an object in an image are not robust to variations in object shape and topography, and are sensitive to image quality issues like noise and non-uniform illumination, which limits their accuracy for applications such as robotic grasping.
Innovation Solution
A method that uses multiple classifiers trained for different specific orientations, with their responses used to calculate a weighted average to determine the object's orientation, reducing sensitivity to shape and image quality variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sparse feature extraction methods are used to determine object orientation, then the method is simple to implement, but the measurement precision deteriorates due to sensitivity to noise and illumination variations
Solution Approach 1:
The patent divides the object detection task into multiple segments by training separate classifiers for different orientation ranges (e.g., 0-30 degrees, 30-60 degrees, etc.). Each classifier focuses on a specific segment of the orientation space, improving precision within each segment while maintaining overall system simplicity through modular classification.
Solution Approach 2:
The patent transforms the continuous orientation estimation problem into a discrete multi-dimensional classification problem by introducing orientation bins and training multiple classifiers for different bins. This dimensional transformation allows the system to achieve higher precision by leveraging the discriminative power of multiple specialized classifiers rather than relying on a single sparse feature extraction approach.
2Device complexity
If single classifier methods are used for object orientation detection, then the device complexity is low, but the reliability deteriorates due to poor robustness to shape and topography variations
Solution Approach 1:
The patent segments the orientation detection task into multiple classifiers, each handling a specific orientation range. This segmentation allows each classifier to specialize in detecting objects within its designated range, improving reliability for objects with varying shapes and topographies by matching them to the most appropriate classifier.
Solution Approach 2:
The patent creates a universal orientation detection system where multiple classifiers work together to handle diverse object types and orientations. Each classifier is trained on a specific orientation range but the overall system can detect objects across all orientations, providing multi-functionality and robustness to various object shapes and topographies.
3Speed
If traditional feature-based methods are used, then the computational speed is fast, but the measurement precision deteriorates due to sensitivity to image quality variations
Solution Approach 1:
The patent applies preliminary action by pre-training multiple classifiers on labeled data covering different orientation ranges before deployment. This preliminary training phase allows the classifiers to learn robust features and decision boundaries, enabling fast real-time detection without sacrificing precision, as the heavy computational work is done during the offline training phase.
Solution Approach 2:
The patent transforms the continuous orientation estimation into discrete classification bins, allowing the use of efficient classification algorithms that operate faster than continuous optimization methods. This dimensional discretization maintains computational speed while improving precision by reducing sensitivity to image quality variations through the robustness of classification-based approaches.
Data Source
AI summary
A method of determining an orientation of an object within an image. The method determines the responses of at least two classifiers in at least one region of the image corresponding to the object. The classifiers have been trained to identify a given object in different specific orientations. The orientation of the object is determined as an average of the specific orientations, weighted by the responses of their respective classifiers.