Object Recognition Template Storage for Textured and Textureless Images
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Solution Overview
Problem
Current systems for object recognition in robotics face inefficiencies in storage resources and recognition speed due to the need to manage both textured and textureless templates, with textureless templates being temporary and textured templates being long-term, leading to increased storage demands and potential misidentification risks.
Innovation Solution
The system employs a dual template storage approach, using a short-term cache for textureless templates and a long-term database for textured templates, where textureless templates are cleared after task completion and textured templates are reused across tasks, optimizing storage and recognition speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If both textured and textureless templates are stored in the same storage space, then object recognition can handle both types of objects, but storage requirements increase and recognition speed decreases
Solution Approach 1:
The patent divides the template storage into two separate storage spaces: a first storage space for textureless templates and a second storage space for textured templates. This segmentation allows the system to manage different types of templates independently, reducing the storage burden on any single space and improving recognition speed by focusing searches on relevant template types.
Solution Approach 2:
The patent extracts textureless templates from the general template storage and places them in a dedicated first storage space. This extraction separates temporary templates (which are cleared after task completion) from long-term templates, reducing overall storage requirements while maintaining comprehensive object recognition capability.
2Speed
If textureless templates are stored long-term, then recognition speed improves, but storage resources are wasted on temporary templates
Solution Approach 1:
The patent implements dynamic management of textureless templates in the first storage space, where templates are automatically cleared after task completion. This dynamic approach allows the storage space to adapt to current task requirements, maintaining fast recognition speed for active templates while freeing storage resources when templates are no longer needed.
Solution Approach 2:
The patent discards textureless templates from the first storage space after they have served their temporary purpose of facilitating object recognition for current tasks. This discarding and recovering mechanism ensures that storage resources are not permanently occupied by temporary templates, optimizing the balance between recognition speed and storage efficiency.
3Quantity of substance
If textured templates are cleared frequently, then storage space is freed, but recognition accuracy decreases due to loss of long-term templates
Solution Approach 1:
The patent segments templates into textureless (first storage space) and textured (second storage space) categories, with different retention policies for each. Textured templates in the second storage space are preserved long-term to maintain recognition accuracy, while textureless templates in the first storage space are managed dynamically to optimize storage space availability.
Solution Approach 2:
The patent applies different quality characteristics to different storage spaces: the first storage space is designed for temporary, frequently updated textureless templates, while the second storage space is designed for stable, long-term textured templates. This local quality differentiation ensures that recognition accuracy is maintained for textured objects while storage space is efficiently managed.
4Device complexity
If a single template storage space is used, then system complexity is reduced, but recognition speed decreases due to searching through all templates
Solution Approach 1:
The patent segments the template storage system into two distinct storage spaces with clear functional differentiation. This segmentation, while slightly increasing structural complexity, dramatically improves recognition speed by enabling the system to search only within the relevant storage space (first for textureless, second for textured) rather than searching through all templates in a single unified space.
Data Source
AI summary
A computing system and method for object recognition is presented. The method includes the computing system obtaining an image for representing the one or more objects, and generating a target image portion associated with one of the one or more objects. The computing system determines whether to classify the target image portion as textured or textureless, and selects a template storage space from among a first and second template storage space, wherein the first template storage space is cleared more often relative to the second template storage space. The first template storage space is selected in response to a textureless classification, and the second template storage space is selected as the template storage space in response to a textured classification. The computing system performs object recognition based on the target image portion and the selected template storage space.