Natural Language Image Editing via Gesture and Speech Fusion
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Solution Overview
Problem
Conventional image editing applications are complex, making it difficult for users to locate and initiate specific operations, which can lead to frustration due to the multitude of choices and requirement of professional skills, even for novice users.
Innovation Solution
The implementation of natural language image editing techniques, where a natural language processing module parses user input and combines it with gesture recognition to initiate image editing operations, allowing users to specify operations through intuitive language and gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image editing applications provide a multitude of operations and functionalities, then the application capabilities and versatility are improved, but the device complexity and ease of operation deteriorate
Solution Approach 1:
The patent introduces a camera interface as an intermediary between the user and the complex image editing operations. When a user points the camera at a physical object, the system automatically identifies the object and presents context-relevant editing operations, thereby mediating the interaction and hiding the underlying complexity of the application's extensive functionality.
Solution Approach 2:
The system performs automatic object identification and operation recommendation without requiring users to manually search through or understand the full range of available operations. The application serves itself by automatically determining which operations are relevant based on the captured image content, thereby reducing the operational burden on users while maintaining access to extensive capabilities.
2Adaptability or versatility
If conventional image editing applications provide a multitude of operations, then the application capabilities are improved, but the ease of operation deteriorates due to difficulty in locating specific operations
Solution Approach 1:
The patent applies local quality by presenting only the specific editing operations that are relevant to the currently captured image content, rather than displaying all available operations uniformly. The system analyzes the image and selectively enables operations that match the detected objects and scenarios, making the appropriate functionality immediately accessible while keeping irrelevant options hidden.
Solution Approach 2:
The system performs preliminary analysis of the captured image to identify objects and determine relevant operations before presenting the user interface. This preliminary action allows the application to pre-configure the available operations based on the image content, so that users are immediately presented with the correct operations without needing to search or navigate through unrelated options.
3Manufacturing precision
If conventional image editing applications require professional skills to locate and initiate operations, then the manufacturing precision of editing results is improved, but the ease of operation deteriorates for novice users
Solution Approach 1:
The patent replaces the manual mechanical process of searching for and selecting operations with an automated system that uses image recognition and contextual analysis. Instead of requiring users to manually navigate through menus and understand technical operations, the system automatically identifies the appropriate operations based on the captured image and presents them in context, thereby substituting user skill with automated intelligence.
Solution Approach 2:
The camera interface and automatic operation recommendation system act as intermediaries that translate the user's simple act of capturing an image into the selection and execution of appropriate editing operations. This intermediary layer eliminates the need for users to have professional knowledge, as the system handles the complex decision-making process of which operations to apply based on the image content.
Data Source
AI summary
Natural language image editing techniques are described. In one or more implementations, a natural language input is converted from audio data using a speech-to-text engine. A gesture is recognized from one or more touch inputs detected using one or more touch sensors. Performance is then initiated of an operation identified from a combination of the natural language input and the recognized gesture.