Object Recognition Refining Search Space Hash Table
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Solution Overview
Problem
Existing object recognition techniques face significant processing time challenges when recognizing a large number of items from captured images, as they require matching feature amounts extracted from images with a large number of image masters.
Innovation Solution
An object recognition apparatus and method that includes a storage unit for associating feature amounts with objects, an object region detection unit, a feature amount extraction unit, a refining unit, and a matching unit, which detects object regions, extracts feature points, refines object candidates using a hash table, and matches feature points to efficiently recognize objects by reducing the number of matching processes through a refined search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature amounts extracted from captured images are matched with a large number of image masters to recognize individual items, then recognition accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the recognition process into two distinct phases: a refinement phase that narrows down candidates using a hash table structure, and a matching phase that performs detailed comparison only with refined candidates. This segmentation allows the system to maintain high recognition accuracy while dramatically reducing processing time by avoiding exhaustive comparison with all image masters.
Solution Approach 2:
The patent performs preliminary refinement of candidate objects before executing the full matching process. By using a hash table to pre-filter and refine candidates based on extracted feature amounts, the system prepares a reduced set of potential matches in advance, thereby reducing the computational burden of the subsequent matching operation and overall processing time.
2Reliability
If exhaustive matching is performed with all image masters to ensure accurate object recognition, then recognition reliability is improved, but processing efficiency deteriorates
Solution Approach 1:
The recognition process is divided into refinement and matching stages. The refinement stage uses a hash table to reliably narrow down candidates, while the matching stage performs reliable verification only on refined candidates. This segmentation maintains recognition reliability while improving processing efficiency by reducing the number of comparisons required.
Solution Approach 2:
The hash table serves as an intermediary structure between feature extraction and matching. It mediates the process by organizing image masters in a way that enables efficient refinement of candidates based on feature amounts, thereby maintaining reliability through systematic filtering while dramatically improving processing efficiency.
Data Source
AI summary
In an object recognition apparatus, a storage unit stores a table in that a plurality of feature amounts are associated with each object having feature points of respective feature amounts. An object region detection unit detects object regions of a plurality of objects from an input image. A feature amount extraction unit extracts feature amounts of feature points from the input image. A refining unit refers to the table, and refines from all objects of recognition subjects to object candidates corresponding to the object regions based on feature amounts of feature points belonging to the object regions. A matching unit recognizes the plurality of objects by matching the feature points belonging to each of the object regions with feature points for each of the object candidates, and outputs a recognition result.