Image Normalization for Low-Power Object State Classification
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Solution Overview
Problem
Existing image processing methods struggle to distinguish different configuration states of objects, especially under low computing power conditions, such as in gesture recognition scenarios, due to the arbitrary orientation and varying sizes of objects.
Innovation Solution
A method involving image preprocessing, orientation determination, region-of-interest selection, and normalized image generation using affine transformation, followed by histogram-based feature extraction and classification, allows for robust differentiation of object configurations with minimal computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex pattern recognition algorithms and extensive machine learning are applied to distinguish object configurations, then classification accuracy is improved, but computing power requirements increase
Solution Approach 1:
The patent transforms the input image through geometric normalization (scaling, rotating, translating) to standardize object orientation and size parameters before classification. This preprocessing converts variable orientation and scale parameters into a fixed reference frame, enabling simpler classification algorithms to achieve high accuracy without requiring complex machine learning models
Solution Approach 2:
The patent performs preliminary geometric normalization and feature extraction before classification. By pre-processing the image to establish a standardized coordinate system and extract invariant features (such as normalized pixel coordinates and histogram data), the system eliminates the need for computationally intensive learning during the classification phase itself
2Power
If geometric normalization and histogram-based feature extraction are used, then computing power requirements are reduced, but handling of arbitrary object orientations becomes more challenging
Solution Approach 1:
The patent applies affine transformation parameters (scaling factor, rotation angle, translation vector) to convert images with arbitrary orientations into a standardized coordinate system. By changing the geometric parameters of the image representation, the system achieves orientation invariance without requiring complex adaptive algorithms
Solution Approach 2:
The patent extends the 2D image coordinate system by introducing normalized coordinates that are invariant to rotation and scaling. This dimensional transformation allows the system to handle arbitrary orientations by mapping all variations into a unified reference frame, effectively adding orientation normalization as an implicit dimension
Data Source
AI summary
A method for distinguishing different configuration states of an object based on an image representation of the object involves acquiring original image data representing an original image comprising a picture of the object; detecting the object within the original image; determining an orientation of the detected object; determining a region-of-interest within the original image representing the picture of the object at least in parts; generating normalized image data representing a two-dimensional normalized image by transforming the ROI of the original image based on the determined orientation of the object to a reference frame in which the ROI is represented by the normalized image with a predefined standard orientation and scaling; extracting characteristic features of the object as represented by the normalized image data; and classifying the extracted characteristic features to determine a specific configuration state of the object associated with the classification.