3D Image Transformation Selection Using Pixel Histograms
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Solution Overview
Problem
Existing machine vision systems face inefficiencies in identifying an optimal transformation type for converting 3D data into 2D maps for deep learning applications, particularly in real-time embedded devices, due to the time-consuming nature of brute force testing of multiple transformation options.
Innovation Solution
A method involving statistical analysis of pixel histograms is employed to identify a preferred transformation type for 3D data conversion to 2D maps, utilizing pre-trained 2D deep learning models, which reduces the need for extensive training and is suitable for embedded devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force testing of multiple transformation options is used to identify optimal transformation type, then accuracy in 3D image processing is improved, but computational time and resource requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing statistical features (histograms, mean, variance, skewness, kurtosis) of the 3D data before actual transformation testing. These pre-computed statistical characteristics are then used to predict the optimal transformation type without performing exhaustive brute force testing, thereby reducing computational time while maintaining accuracy in 3D image processing tasks.
Solution Approach 2:
The patent introduces statistical analysis as an intermediary between the raw 3D data and the transformation selection process. Instead of directly testing multiple transformation options on the original data, the system first computes statistical features that serve as intermediate representations. These statistical intermediaries enable efficient prediction of optimal transformation types, reducing the need for computationally expensive exhaustive testing.
2Measurement precision
If brute force testing of multiple transformation options is used to identify optimal transformation type, then accuracy in 3D image processing is improved, but resource requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing statistical features (histograms, mean, variance, skewness, kurtosis) of the 3D data before actual transformation testing. These pre-computed statistical characteristics are then used to predict the optimal transformation type without performing exhaustive brute force testing, thereby reducing computational time while maintaining accuracy in 3D image processing tasks.
Solution Approach 2:
The patent introduces statistical analysis as an intermediary between the raw 3D data and the transformation selection process. Instead of directly testing multiple transformation options on the original data, the system first computes statistical features that serve as intermediate representations. These statistical intermediaries enable efficient prediction of optimal transformation types, reducing the need for computationally expensive exhaustive testing.
3Measurement precision
If extensive training with multiple transformation types is performed, then model accuracy is improved, but training time and computational cost increase
Solution Approach 1:
The patent applies preliminary action by pre-computing statistical features (histograms, mean, variance, skewness, kurtosis) of the 3D data before actual transformation testing. These pre-computed statistical characteristics are then used to predict the optimal transformation type without performing exhaustive brute force testing, thereby reducing computational time while maintaining accuracy in 3D image processing tasks.
Solution Approach 2:
The patent applies parameter changes by transforming the 3D data into different statistical domain representations (histograms, mean, variance, skewness, kurtosis) to capture essential characteristics. By changing the representation parameters of the data, the system enables efficient prediction of optimal transformation types without requiring extensive training across multiple transformation configurations, thus improving training efficiency while maintaining model accuracy.
Data Source
AI summary
The techniques described herein relate to methods and systems for three-dimensional (3D) image processing using deep learning model pre-trained with two-dimensional (2D) images. The techniques include transforming a 3D representation to a 2D map, which can be input to the model. The output of the model could be a defect segmentation mask, or a probability of the input belonging to a given category. The input 2D image can result from a particular transformation configuration. The techniques described herein provide for an effective and efficient method of identifying a transformation type for applications such as 3D data-based classification, anomaly detection and segmentation. The techniques described herein perform statistical analysis over a set of training samples to identify a transformation type. The statistical analysis can include histograms of pixel values.