Frustum-Based Image Density Mapping for Landmark Identification
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Solution Overview
Problem
Organizing and sorting large quantities of images across different sources is challenging due to inconsistent and general tagging, making it difficult to identify and categorize important landmarks or representative images.
Innovation Solution
The method involves defining a frustum for each image, overlaying these frustums in two, three, or more dimensions to create a density map, which allows for the identification and grouping of images based on points of interest, enabling the selection of representative images without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually tag and categorize images, then images can be organized, but the process becomes time-consuming and inconsistent across different users
Solution Approach 1:
The system automatically performs image categorization and organization without requiring manual user intervention. The automated frustum-based classification system processes images independently, eliminating the time-consuming and inconsistent manual tagging process while maintaining consistent categorization across all images.
Solution Approach 2:
The patent replaces manual mechanical image sorting with an automated computational system. By using frustum geometry and density mapping algorithms, the system automatically determines image relationships and categories, substituting human labor with automated processing that is both consistent and time-efficient.
2Productivity
If general tags are used for images, then users can quickly post images, but the tags become difficult to search and categorize
Solution Approach 1:
The system segments image categorization into multiple levels of specificity. Instead of using single general tags, the frustum-based system creates hierarchical categories based on spatial relationships, density maps, and geometric analysis, enabling both quick posting and detailed searchability through multiple classification levels.
Solution Approach 2:
The patent transforms image organization from text-based tagging to geometric parameter-based classification. By using frustum dimensions, spatial coordinates, and density metrics as classification parameters, the system achieves both rapid image processing and precise, searchable categorization that general text tags cannot provide.
3Quantity of substance
If images from multiple sources are combined, then the volume of images increases, but sorting and categorizing becomes more difficult
Solution Approach 1:
The frustum-based classification system provides universal applicability across images from any source. The same geometric and density-based algorithms work consistently on images regardless of their origin, maintaining uniform categorization standards across large volumes of diverse images without increasing operational complexity.
Solution Approach 2:
The system manages large image volumes by transforming the sorting problem into geometric parameter space. By representing images as frustums with specific spatial and angular parameters, the system can efficiently sort and categorize massive datasets through computational geometry rather than complex manual or automated processing, reducing operational complexity despite increased quantity.
Data Source
AI summary
Images may be sorted and categorized by defining a frustum for each image and overlaying the frustums in two, three, or four dimensions to create a density map and identify points of interest. Images that contain a point of interest may be grouped, sorted, and categorized to determine representative images of the point. By including many images from different sources, common points of interest may be defined. Points of interest may be defined in two or three Euclidian dimensions, or may include a dimension of time.