Image Control System Gesture Recognition for Presentation Annotation
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Solution Overview
Problem
Existing image control systems for educational or presentation settings require manual assistance to highlight key points, which can lead to coordination issues between the speaker and assistants.
Innovation Solution
An image control system and method that uses image recognition to capture, recognize, and update images in real-time, allowing the speaker to intuitively control indicator patterns and screen changes without external assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual assistance (guide stick or laser pointer) is used to highlight key points, then the speaker can point to important targets, but the system requires additional assistants and coordination complexity increases
Solution Approach 1:
The system enables self-service by allowing the speaker to control the indicator pattern directly through gesture recognition, eliminating the need for external assistants. The speaker's hand gestures are automatically recognized and translated into screen annotations, making the system serve itself without requiring additional human operators.
Solution Approach 2:
The patent replaces the mechanical system of manual guide sticks or laser pointers with an automated image recognition and processing system. The physical manipulation of annotation tools is substituted by computer vision technology that automatically detects hand gestures and generates corresponding indicator patterns on the screen.
2Reliability
If manual assistance is used to control indicator patterns, then key points can be highlighted, but coordination between speaker and assistants becomes difficult
Solution Approach 1:
The system implements feedback by continuously monitoring the speaker's hand gestures through image capture and recognition, then immediately translating these gestures into corresponding indicator patterns on the screen. This real-time feedback loop ensures that the annotation always reflects the speaker's current intent without requiring coordination with external assistants.
Solution Approach 2:
The system makes the annotation process self-serving by automatically generating and updating indicator patterns based on the speaker's natural hand gestures. The speaker does not need to coordinate with anyone else - the system independently interprets gestures and creates appropriate visual annotations, improving reliability by eliminating human coordination errors.
3Loss of information
If the speaker uses oral description to explain subtle movements, then communication can occur, but the audience cannot focus on important targets effectively
Solution Approach 1:
The system applies segmentation by creating a focused indicator pattern that segments and highlights the specific region of interest on the screen. Instead of relying on oral description to convey spatial information, the system visually segments the relevant area with an annotation, allowing the audience to immediately focus on the important target without losing information about its location.
Solution Approach 2:
The system uses visual changes (indicator patterns such as circles, rectangles, or highlights) to draw attention to key areas. These visual modifications to the displayed image provide immediate visual cues that complement oral description, ensuring the audience can locate and focus on subtle movements or important targets being discussed.
Data Source
AI summary
A method for controlling the image display includes an image capturing process, an image recognition process, a feature determination process, an image updating process, and an image outputting process. The image capturing process is to capture an input image. The image recognition process is to recognize a specified feature in the input image and generate a recognized image. The feature determination process is to determine whether a target feature exists in the recognized image and when the target feature exists in the recognized image, it also determines whether there is an indication pattern recorded in the image control system and generates a first command or a second command accordingly. The image updating process is to generate an indicator pattern according to the first command and superimpose the indicator pattern on the input image to generate an output image. The image outputting process is to output the output image.