Stereo Camera Disparity Analysis for Foreground Background Separation
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Solution Overview
Problem
Conventional gesture recognition systems for portable computing devices face computational challenges in distinguishing and separating user features from background objects, leading to resource-intensive processing, delayed responses, and battery drain.
Innovation Solution
The use of stereo imaging with a pair of cameras to determine disparity between images, allowing for the separation of foreground and background objects with reduced processing requirements by analyzing objects with less than a specified disparity level, thereby reducing computational load and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-camera gesture recognition is used, then the device can capture images, but it cannot effectively separate foreground features from background objects, leading to computationally expensive processing
Solution Approach 1:
The patent transitions from single-camera 2D imaging to stereo 3D imaging by adding a depth dimension. Two cameras capture images from slightly different positions, creating disparity maps that encode depth information. This dimensional change enables automatic foreground-background separation based on depth cues, reducing processing complexity while improving separation accuracy.
Solution Approach 2:
The patent introduces disparity maps as an intermediary representation between raw images and gesture recognition. By computing disparity (horizontal pixel offset) between stereo images, the system creates an intermediate depth map that highlights foreground objects. This intermediary step simplifies subsequent feature detection by pre-separating foreground from background.
2Measurement precision
If full disparity map generation is performed, then complete depth information is obtained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential disparity information needed for foreground-background separation rather than generating complete disparity maps for the entire image. By focusing computation on regions containing potential gestures and using thresholding to identify foreground objects, the system obtains sufficient depth information while minimizing processing time and resource consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces resource consumption, enhances processing speed, and improves accuracy in gesture recognition by efficiently distinguishing user features from background objects, minimizing blind spots, and maintaining device performance.
Implementation Method 1
a pair of cameras can concurrently capture image information from two different perspectives in order to provide, for example, stereo imaging capability
Data Source
AI summary
Locating features in image information captured by multiple cameras of a computing device can be facilitated by removing background objects from the image information. A distance past which objects should be considered to be background objects is determined, and an amount of disparity for that distance calculated. Any objects having less than that amount of disparity in the image information can be removed, leaving objects that should be treated as foreground objects. An advantage of such an approach is that objects can be determined to be foreground objects even when those objects are only viewable by one of the cameras and disparity information cannot be calculated for those objects from the image information. Such an approach can be combined with other approaches as well to improve accuracy.