Stereo Camera Finger Gesture Detection via Offset Image Processing
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Solution Overview
Problem
Current input mechanisms for computing systems, such as keyboards and mice, are not natural and have been difficult to implement effectively for user control.
Innovation Solution
A method using stereo cameras to detect objects, specifically fingers, by receiving images from multiple perspectives and applying machine-learning classifiers to accurately identify and track finger gestures, enabling more natural user interaction through augmented reality interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input mechanisms (keyboards and mice) are used for user control, then device compatibility and reliability are maintained, but user interaction naturalness and ease of operation deteriorate
Solution Approach 1:
The patent replaces mechanical input devices (keyboards, mice) with an optical-based gesture recognition system using stereo cameras and machine learning classifiers. This substitution enables natural hand and finger gesture recognition, improving ease of operation while eliminating the need for complex mechanical input mechanisms.
2Ease of operation
If stereo camera-based gesture recognition is implemented, then user interaction naturalness is improved, but system complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent applies machine learning classifiers in advance to train the system to recognize specific hand and finger gestures. This preliminary training enables the system to accurately detect and measure gesture patterns, reducing the difficulty of object detection while maintaining high recognition accuracy for natural user interactions.
Solution Approach 2:
The patent introduces machine learning classifiers as an intermediary between the stereo camera input and the gesture recognition output. These classifiers process the complex stereo image data and translate it into recognizable gesture patterns, simplifying the detection and measurement process while improving accuracy.
3Measurement precision
If offset images are processed through machine-learning classifiers, then object detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies multiple machine learning classifiers to different aspects of the offset images (hand detection, finger detection, gesture classification). By distributing the detection task across multiple specialized classifiers rather than using a single comprehensive classifier, the system achieves high accuracy while optimizing processing efficiency through parallel evaluation.
Data Source
AI summary
A method of object detection includes receiving a first image taken from a first perspective by a first camera and receiving a second image taken from a second perspective, different from the first perspective, by a second camera. Each pixel in the first image is offset relative to a corresponding pixel in the second image by a predetermined offset distance resulting in offset first and second images. A particular pixel of the offset first image depicts a same object locus as a corresponding pixel in the offset second image only if the object locus is at an expected object-detection distance from the first and second cameras. The method includes recognizing that a target object is imaged by the particular pixel of the offset first image and the corresponding pixel of the offset second image.