Pictogram Recognition Using 3D Spatial Descriptor Analysis
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Solution Overview
Problem
Existing pictogram recognition technologies face challenges in accurately recognizing pictograms due to a small number of characteristic points, leading to reduced discrimination power and improper recognition, especially when descriptors from different pictograms appear similar.
Innovation Solution
The method involves acquiring a Histogram of Oriented Gradient (HOG) from image zones, analyzing descriptor characteristics in space, and using hyperspace division with hyperplanes to accurately recognize pictograms by grouping similar descriptors and determining their spatial arrangement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional descriptor methods are used for pictogram recognition, then the recognition process is simple, but the discrimination power is reduced and misrecognition occurs when descriptors from different pictograms appear similar
Solution Approach 1:
The patent transitions from traditional 2D descriptor representation to a 3D spatial arrangement representation. Descriptors are not only characterized by their visual features but also by their spatial coordinates (x, y, z) in a three-dimensional space, where z represents the depth or layer information. This dimensional expansion enables the system to distinguish between pictograms with similar 2D descriptors by utilizing their different spatial configurations.
Solution Approach 2:
The patent divides the pictogram recognition process into multiple independent stages: feature extraction, descriptor generation, spatial arrangement analysis, and classification. By segmenting the complex recognition task into manageable components, the system can apply specialized processing to each stage, improving overall accuracy without overwhelming computational complexity.
2Measurement precision
If more characteristic points are used to improve discrimination power, then recognition accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent extracts only the most discriminative spatial arrangement features from the complete set of possible characteristics. Instead of analyzing all possible descriptor combinations, the system selectively extracts key spatial relationships (such as relative positions, distances, and angular arrangements) that provide the highest discrimination power with minimal computational overhead.
Solution Approach 2:
The patent transforms the feature representation parameters from traditional 2D coordinates to 3D spatial parameters, and from raw pixel data to extracted gradient-based descriptors (such as HOG - Histogram of Oriented Gradients). This parameter transformation reduces the data dimensionality while preserving the most informative characteristics for discrimination.
3Productivity
If simple zone extraction is used, then the processing is fast, but similar descriptors from different pictograms cannot be properly distinguished
Solution Approach 1:
The patent creates a composite descriptor that combines multiple types of information: visual features (such as gradient orientations and magnitudes), spatial coordinates, and contextual relationships. This composite representation integrates heterogeneous data types into a unified descriptor structure that maintains processing efficiency while significantly improving the ability to distinguish between similar pictograms.
Data Source
AI summary
Disclosed are a pictogram recognition apparatus, a pictogram recognition system, and a pictogram recognition method, wherein the pictogram recognition method includes acquiring learning data, acquiring an image including a pictogram, extracting at least one zone from the image, acquiring a descriptor corresponding to the at least one zone, recognizing a pictogram by comparing the descriptor with the learning data and providing a user with information corresponding to the recognized pictogram.