Visual Scene Element Filtering Using Context-Aware Detection
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Solution Overview
Problem
Existing electronic devices lack effective methods for identifying and filtering elements of interest within a visual scene, such as text and objects, to provide relevant information to users.
Innovation Solution
Implementing a method and system that uses computer vision, machine learning, and artificial intelligence to analyze a visual scene, identify elements, determine context, and apply user-defined filters to highlight or obscure elements based on user intent, geographic location, and Internet search data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computer vision and machine learning are used to identify elements in a visual scene, then information completeness is improved, but device complexity increases
Solution Approach 1:
The system segments the visual scene into multiple discrete elements (objects, text, landmarks) using computer vision algorithms. Each element is independently identified, classified, and annotated with metadata, allowing comprehensive information extraction without requiring the entire system to process all visual data simultaneously.
Solution Approach 2:
The patent introduces an intermediary layer of context determination that mediates between raw element identification and final information presentation. This intermediary context layer (including scene context, user context, and filter context) organizes and prioritizes information before delivery, reducing the complexity burden on the device while maintaining information completeness.
2Ease of operation
If multiple filters are applied to identify elements of interest, then user experience is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying and categorizing all elements in the visual scene before applying filters. Elements are pre-annotated with metadata and organized by type, allowing filter application to occur on already-processed data structures rather than requiring re-analysis of raw visual input, significantly reducing processing time.
Solution Approach 2:
The filter application process is designed to be dynamic and adaptive. The system determines which filters to apply based on contextual information and user preferences, rather than always applying all possible filters. This dynamic selection optimizes processing time by applying only necessary filters while maintaining enhanced user experience through relevant information delivery.
3Loss of information
If context determination is performed based on multiple elements, then information relevance is improved, but computational load increases
Solution Approach 1:
The context determination process applies local quality by focusing computational resources on specific regions or elements within the visual scene that are most relevant to user interests. Rather than uniformly processing all elements with equal computational intensity, the system identifies key areas (such as user-selected elements or previously identified points of interest) and concentrates context analysis there, reducing overall computational load while maintaining information relevance.
Data Source
AI summary
In a general aspect, a method can include receiving, by an electronic device, a visual scene; identifying, by the electronic device, a plurality of elements of the visual scene; and determining, based on the plurality of elements identified in the visual scene, a context of the visual scene. The method can further include applying, based on the determined context of the visual scene, at least one filter to identify at least one element of the plurality of elements corresponding with the at least one filter; and visually indicate, in the visual scene on a display of the electronic device, the at least one element identified using the at least one filter.