Hand Part Labeling via Invariant Feature Vectors
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Solution Overview
Problem
Existing techniques for detecting and recognizing fingers in images are computationally intensive and inefficient, struggling with variability in hand position, orientation, size, and shape, which hinders robust and fast labeling in consumer electronic devices for gesture recognition and object detection.
Innovation Solution
The method involves detecting areas of interest in images using invariant feature vectors, generating feature vectors from 2D and 3D imaging data, and applying a pre-trained machine learning classifier to accurately label fingers and other hand parts, enabling robust and efficient gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques are used for detecting and recognizing fingers in images, then object detection and gesture recognition can be performed, but the computational complexity and memory consumption are substantial
Solution Approach 1:
The patent segments the hand detection problem into multiple stages: first detecting the overall hand region, then identifying individual fingers within that region. This hierarchical segmentation reduces the computational burden by breaking down the complex task of finger labeling into manageable sub-tasks, thereby improving accuracy without proportionally increasing overall system complexity
Solution Approach 2:
The patent performs preliminary hand region detection before proceeding to finger identification. By first establishing the hand boundary and then focusing computational resources on identifying fingers within that predefined region, the system avoids the need to analyze the entire image for each finger, thus reducing computational complexity while maintaining labeling accuracy
2Reliability
If existing techniques are used for detecting and recognizing fingers in images, then object detection and gesture recognition can be performed, but the processing speed is slow
Solution Approach 1:
By segmenting the detection process into hand region identification followed by finger identification within that region, the patent reduces the search space for each processing stage. This allows for faster processing speeds as the system doesn't need to analyze the entire image for each finger, while still achieving accurate finger labeling through the multi-stage approach
Solution Approach 2:
The preliminary detection of the hand region serves as a filtering step that prepares the data for subsequent finger identification. This preliminary action accelerates processing by pre-defining the area of interest, allowing the system to quickly locate and label fingers within the already-identified hand boundary without re-analyzing the entire image
3Adaptability or versatility
If existing techniques are used for detecting and recognizing fingers in images, then object detection can be performed, but the techniques struggle with variability in hand position, orientation, size, and shape
Solution Approach 1:
The patent employs dynamic adaptation by training the machine learning classifier on diverse training data that encompasses various hand positions, orientations, sizes, and shapes. This dynamic training approach enables the system to adapt to different hand configurations and maintain robust labeling performance across variable conditions, rather than relying on fixed detection parameters
Data Source
AI summary
Techniques related to labeling component parts and detecting component properties in imaging data are discussed. Such techniques may include generating a feature vector including invariant features associated with an area of interest within an image of an object such as an image of a hand and providing a component label such as a hand part label for the area of interest based on an application of a machine learning classifier to the feature vector.