Gesture-Controlled Electronic Apparatus Using Magic Hand Color Mapping
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Solution Overview
Problem
Current electronic devices lack intuitive methods for users to control image processing, particularly in interactive and creative tasks like drawing or coloring, as existing interaction techniques can be cumbersome and non-intuitive, especially for children.
Innovation Solution
An electronic apparatus that uses a neural network to analyze input or captured images to generate a 'magic hand' image, allowing users to control image processing through hand gestures, enabling intuitive color manipulation and collaboration across multiple users via screen sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional control methods are used for image processing, then device functionality is maintained, but user interaction becomes cumbersome and non-intuitive
Solution Approach 1:
The patent replaces traditional mechanical control interfaces (remote controls, buttons, touch screens) with a gesture-based control system. The system captures hand images via camera, processes them through neural networks to recognize gestures, and translates these into image processing commands, substituting physical interaction mechanisms with optical and computational ones.
Solution Approach 2:
The patent introduces an intermediary processing system between the user's hand gesture and the image processing function. This intermediary includes image capture devices, neural network processing units, and gesture recognition algorithms that translate natural hand movements into controlled image manipulation commands, making the interaction more intuitive.
2Ease of operation
If gesture recognition is implemented for intuitive control, then ease of operation improves, but system complexity and processing requirements increase
Solution Approach 1:
The system employs self-service mechanisms where the neural network automatically learns and adapts to gesture patterns without requiring manual programming of each gesture. The system self-calibrates by processing captured hand images through trained neural networks that automatically recognize and classify gestures, reducing the need for complex manual configuration.
Solution Approach 2:
The patent implements preliminary action by pre-training neural networks with gesture data before actual use. The system performs offline training and model preparation in advance, so that during runtime, gesture recognition can proceed efficiently without real-time complex processing, reducing the computational burden during actual interaction.
3Productivity
If real-time color manipulation is enabled, then user experience and interactivity improve, but processing time and computational load increase
Solution Approach 1:
The system implements periodic action by updating color manipulations at optimized intervals rather than continuously. The gesture recognition and color application occur in periodic cycles, processing hand images at strategic moments to achieve real-time effect while minimizing continuous computational overhead, balancing responsiveness with processing efficiency.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary portions of images for color manipulation based on detected gestures. Instead of processing entire images or all possible color parameters simultaneously, the system processes only relevant regions and color attributes identified through gesture analysis, reducing overall computational load while maintaining perceived real-time performance.
Data Source
AI summary
An electronic apparatus including a processor configured to analyze at least one of an input image or an captured image and obtain a recommended color distribution including at least one color based on the at least one of the input image or the captured image, the captured image being obtained by capturing an image of an environment; display the input image on a display, obtain a hand image by capturing an image of a hand of a user and generate a magic hand by mapping a color to the obtained hand image based on the recommended color distribution, and detect a gesture of the hand of the user and control colors of the input image displayed on the display based on the detected hand gesture and a color mapping of the magic hand.