Scalable Vector Cages Guide Pixel-Level Object Part Classification
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Solution Overview
Problem
Existing object part classification methods, particularly in image-based and 3D mesh techniques, are limited by object complexity, image quality, object variability, and human bias, leading to inaccurate and computationally expensive processes.
Innovation Solution
Utilizing Scalable Vector Cages (SVC) that employ vector-based outlines to accurately identify object components, combined with metadata in a vector and pixel format, enabling efficient and precise classification through one-shot learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image-based pixel-level segmentation techniques are used for object part classification, then the method can handle complex objects, but the computational cost increases and accuracy decreases due to image quality limitations
Solution Approach 1:
The patent creates a simplified vector copy (cage) of the object that captures the essential geometry and part structure without the complexity of pixel-level details. This vector cage serves as a computationally efficient representation that can be accurately segmented and used to guide pixel-level classification, resolving the contradiction between handling complex objects and maintaining segmentation accuracy.
Solution Approach 2:
The patent segments the object representation into two distinct components: a vector cage that defines the overall object boundary and part structures, and pixel-level data that captures detailed surface information. This dual-level segmentation allows the system to handle complex objects through the flexible vector representation while maintaining accuracy through the detailed pixel data, avoiding the computational burden of pure pixel-level segmentation.
2Measurement precision
If 3D mesh based techniques are used for object part classification, then the method can provide accurate part identification, but the computational expense increases significantly
Solution Approach 1:
The patent replaces expensive 3D mesh processing with a lighter computational alternative: 2D vector cages that can be generated and processed much more efficiently. The vector cages serve as disposable, computationally inexpensive representations that capture the essential part structure without requiring the heavy computational resources needed for full 3D mesh processing, while still providing accurate part identification.
Solution Approach 2:
The patent extracts only the essential geometric information needed for part identification from the full 3D object data, representing it as a simplified vector cage. This extraction process removes the computationally expensive elements of full 3D mesh processing while retaining the critical part boundary and structure information, thereby reducing computational expense while maintaining part identification accuracy.
3Measurement precision
If manual annotation of training data is performed to improve machine learning accuracy, then the classification precision improves, but the time and cost for data collection increases
Solution Approach 1:
The patent enables the system to automatically generate its own training data by using the vector cage segmentation to create labeled examples. The vector cages provide structurally accurate part definitions that can be automatically converted into training annotations, eliminating the need for manual data annotation while maintaining high classification accuracy. This self-service approach to data generation resolves the contradiction between accuracy and data collection time.
4Reliability
If physical inspection methods are used for object assessment, then the inspection accuracy can be maintained, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces physical inspection methods with a computational system that uses vector cage segmentation and image processing to assess objects. The vector cages provide a structured framework for automatically identifying and evaluating object parts through digital image analysis, substituting the mechanical and manual processes of physical inspection with efficient computational methods that maintain accuracy while dramatically increasing inspection speed and reducing costs.
Data Source
AI summary
Improved and alternative processes for object segmentation segment from captured images are presented. According to an aspect there is provided, systems and methods for classifying segments of an object. The systems and methods include processing captured images using a plurality of cages to identify a cage for image alignment, the cage defining segments of the object, aligning the captured images onto the cage to identify segments of the object in the captured images, and detecting one or more defects in the segments of the object in the captured images.