Foreground Background Image Segmentation Geodesic Distance
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Solution Overview
Problem
Existing camera-based user input systems face challenges in accurately segmenting foreground from background in real-time, leading to difficulties in interpreting user gestures and movements for control applications, such as games, due to the complexity of processing large image data and the limitations of threshold filters and flood fill techniques.
Innovation Solution
The implementation of geodesic distance transforms and machine learning classifiers to segment foreground from background, using seed regions and decision trees to improve accuracy and efficiency in image segmentation, allowing for more precise user movement analysis and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If threshold filters or flood fill techniques are used for image segmentation, then the processing is simpler, but the segmentation accuracy deteriorates due to bleeding and disconnected regions
Solution Approach 1:
The patent changes the parameter used for segmentation from simple intensity thresholds or flood fill boundaries to geodesic distance values. By calculating the shortest path distance from each pixel to seed regions in the foreground image, the method achieves more accurate segmentation that avoids the bleeding and disconnection problems of traditional methods, while maintaining computational efficiency through optimized distance transform algorithms.
2Measurement precision
If camera-based user input is implemented, then the user movement control accuracy is improved, but the computational complexity increases due to real-time image data processing
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: first identifying seed regions in the foreground image, then calculating geodesic distances from these seeds, and finally classifying pixels based on distance thresholds. This segmented approach to processing reduces the overall computational complexity compared to analyzing entire images, while maintaining accurate user movement detection.
Solution Approach 2:
The patent extracts only the essential information needed for control by using geodesic distance transforms to identify foreground regions of interest. Instead of processing all image data, the method extracts distance values from seed regions and uses these to classify pixels, thereby reducing computational complexity while preserving the accuracy needed for user movement control.
3Measurement precision
If geodesic distance transforms are used for segmentation, then the segmentation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary action by first identifying and marking seed regions in the foreground image before calculating geodesic distances. This preliminary step organizes the data structure in advance, allowing the distance transform algorithm to operate more efficiently on predefined regions rather than the entire image, thus maintaining processing speed while achieving accurate segmentation.
Data Source
AI summary
Foreground and background image segmentation is described. In an example, a seed region is selected in a foreground portion of an image, and a geodesic distance is calculated from each image element to the seed region. A subset of the image elements having a geodesic distance less than a threshold is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.