Physical Scene Target Demarcation Through Multidimensional Cell Classification
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Solution Overview
Problem
Existing classifiers struggle to accurately define the spatial boundaries of objects with weakly defined boundaries, such as regions of texture in natural environments or irregular processes on metal surfaces, leading to inefficiencies in classification and identification.
Innovation Solution
A system and method that constructs a multi-dimensional spatial array of cells within a predetermined coordinate space, classifies cells based on target-identifying criteria, and generates a composite image with boundary demarcations to visually outline targets of interest, using sample capture devices and processors to analyze spatial datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If classifiers are used to identify targets in spatial datasets, then classification capability is improved, but the ability to accurately define spatial boundaries of objects with weakly defined boundaries deteriorates
Solution Approach 1:
The spatial dataset is divided into discrete cells forming a multi-dimensional array, where each cell represents a specific spatial region. This segmentation allows the system to process and classify individual cells independently while maintaining spatial relationships, thereby improving both classification capability and boundary definition accuracy for distributed targets.
Solution Approach 2:
The patent extends the spatial representation from traditional 2D or 3D space to multi-dimensional coordinate space, adding dimensions for different data modalities and features. This dimensional expansion enables simultaneous classification of multiple characteristics while preserving precise spatial boundary information across all dimensions.
2Device complexity
If traditional classification methods are used, then processing simplicity is maintained, but the efficiency of isolating identifying characteristics in large datasets deteriorates
Solution Approach 1:
By segmenting the dataset into a structured multi-dimensional cell array, the system enables efficient processing through systematic traversal and classification of individual cells. This structured segmentation maintains processing simplicity while dramatically improving efficiency in large datasets compared to unstructured traditional methods.
Solution Approach 2:
The multi-dimensional cell array serves as an intermediary data structure between raw spatial data and classification results. This intermediary organization enables efficient access and processing of identifying characteristics while maintaining the simplicity of classification operations through standardized cell-based processing.
3Measurement precision
If classifiers process large datasets, then recognition accuracy is improved, but the time required for processing deteriorates
Solution Approach 1:
Segmenting the large dataset into smaller spatial cells enables parallel processing and reduces the computational burden on individual processing units. The system can classify cells independently and in parallel, maintaining high recognition accuracy while significantly reducing overall processing time compared to sequential processing of entire datasets.
Solution Approach 2:
The patent performs preliminary organization of spatial data into a multi-dimensional cell array structure before classification begins. This preliminary spatial indexing and organization enables rapid access and processing during classification, reducing the time required to process large datasets while maintaining accurate recognition through systematic cell-based analysis.
Data Source
AI summary
Captured samples of a physical structure or other scene are mapped to a predetermined multi-dimensional coordinate space, and spatially-adjacent samples are organized into array cells representing subspaces thereof. Each cell is classified according to predetermined target-identifying criteria for the samples of the cell. A cluster of spatially-contiguous cells of common classification, peripherally bounded by cells of different classification, is constructed, and a boundary demarcation is defined from the peripheral contour of the cluster. The boundary demarcation is overlaid upon a visual display of the physical scene, thereby visually demarcating the boundaries of a detected target of interest.