Automated Video GIS Matching via Visual Feature Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Geographic Information Systems (GIS) lack efficient methods to automatically associate video data with image data based on visual geographic features, limiting the ability to seamlessly link and display relevant video content with geographic locations.
Innovation Solution
A method for automated processing of image data in GIS systems, which involves segmenting video data, comparing image frames with image data structures, and generating associations based on histogram, color distribution, texture, and geometric features, allowing for the display of video segments linked to specific geographic locations through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual association methods are used to link video data with image data in GIS, then association accuracy can be maintained, but the system complexity and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting visual geographic features from video frames and matching them with image data in the GIS without requiring manual intervention. The automated extraction of histogram, color distribution, texture, and geometric features, followed by automatic association generation, eliminates the need for manual pairing while maintaining association accuracy through multi-feature comparison.
Solution Approach 2:
The patent replaces manual mechanical association operations with automated computational processes. Instead of manually comparing and matching video frames with GIS images, the system uses computer vision algorithms to automatically extract visual features and generate associations, substituting human mechanical work with automated image processing and pattern recognition systems.
2Productivity
If automated processing is implemented to link video data with image data, then processing efficiency improves, but the difficulty of detecting and measuring visual features increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of visual feature analysis into distinct components: histogram extraction, color distribution analysis, texture feature detection, and geometric feature measurement. Each visual feature type is processed separately through dedicated algorithms, making the overall detection process more manageable and systematic while maintaining high processing efficiency through parallel computation of multiple feature types.
Solution Approach 2:
The system changes parameters by transforming visual information from video frames into multiple different feature representations (histogram parameters, color distribution parameters, texture parameters, geometric parameters). This parameter transformation enables the system to capture comprehensive visual characteristics through multiple perspectives, improving detection accuracy while maintaining automated processing efficiency through standardized parameter extraction algorithms.
3Measurement precision
If multiple visual features are compared for association, then association accuracy improves, but the device complexity increases
Solution Approach 1:
The patent implements universality by designing a multi-functional comparison system that handles multiple visual feature types (histogram, color distribution, texture, geometric features) through a unified association framework. The same comparison architecture processes different feature types, allowing the system to leverage multiple features for improved accuracy without proportionally increasing system complexity, as the underlying processing structure remains consistent across different feature modalities.
Data Source
AI summary
Methods and systems permit automatic matching of videos with images from dense image-based geographic information systems. In some embodiments, video data including image frames is accessed. The video data may be segmented to determine a first image frame of a segment of the video data. Data representing information from the first image frame may be automatically compared with data representing information from a plurality of image frames of an image-based geographic information data system. Such a comparison may, for example, involve a search for a best match between geometric features, histograms, color data, texture data, etc. of the compared images. Based on the automatic comparing, an association between the video and one or more images of the image-based geographic information data system may be generated. The association may represent a geographic correlation between selected images of the system and the video data.