3D Animal Identification via Dynamic Model Orientation
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Solution Overview
Problem
Existing image recognition systems for identifying animals are ineffective when images are taken from different angles or positions, leading to low matching accuracy, as they rely on comparing two-dimensional pictures and fail to account for variations in animal orientation and position.
Innovation Solution
A system that generates three-dimensional models of animals with movable parts, allowing dynamic configuration to match the orientation and position of incoming images, and iteratively adjusts these models to minimize variance between synthetic and actual images, enabling accurate identification by comparing synthetic images with incoming pictures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If two-dimensional picture comparison is used for animal identification, then the system is simple to implement, but the matching accuracy deteriorates when images are taken from different angles or positions
Solution Approach 1:
The patent transforms the identification approach from two-dimensional image comparison to three-dimensional model comparison. By creating 3D models of animals with movable parts that can be dynamically configured, the system can match animals regardless of their orientation or position in 2D images. This dimensional upgrade resolves the contradiction by maintaining implementation simplicity while dramatically improving matching accuracy across different viewing angles.
Solution Approach 2:
The patent introduces dynamic configurability to the identification model by implementing movable parts that can be adjusted to match different animal positions and orientations. Instead of using static 2D images, the system employs 3D models with adjustable components that adapt to various poses, thereby maintaining high matching accuracy across different image conditions without significantly increasing system complexity.
2Measurement precision
If 3D models with movable parts are generated and dynamically configured, then the matching accuracy is improved, but the device complexity increases
Solution Approach 1:
While moving to 3D models increases complexity, the patent manages this by using a modular approach where the 3D model consists of discrete movable parts that can be independently adjusted. This modular 3D structure allows the system to achieve high matching accuracy without requiring an entirely complex system architecture, as the complexity is distributed across manageable components.
Solution Approach 2:
The patent creates a universal 3D model framework that can handle multiple animal types and positions through a single system. The movable parts in the 3D model serve multiple functions: representing different body parts, accommodating various orientations, and adapting to different species. This multi-functionality reduces overall system complexity by eliminating the need for separate identification systems for different scenarios.
3Reliability
If iterative model adjustment is performed to minimize variance, then the identification reliability is improved, but the processing time increases
Solution Approach 1:
The patent implements an iterative adjustment process where the 3D model is continuously refined by comparing synthetic images with actual images and minimizing variance. This feedback mechanism improves identification reliability by ensuring the model accurately represents the target animal. The iterative nature allows for progressive refinement, balancing reliability improvement with acceptable processing time by stopping when sufficient accuracy is achieved.
Solution Approach 2:
The patent performs preliminary configuration of the 3D model by pre-defining movable parts and their ranges of motion based on anatomical knowledge. This preliminary setup reduces the search space for iterative adjustment, allowing the system to achieve high reliability with fewer iteration cycles, thereby reducing processing time while maintaining accurate identification.
Data Source
AI summary
A system includes an identification system and an information database for linking an unidentified animal (UA) to a registered animal. A requester sends the media corresponding to the UA to the identification system. The UA images are used for determining an orientation and position of the UA. A selected 3DSM of the registered animal may be reconfigured using a 3DSM generator by moving the movable joints of the 3DSM to have the same orientation and position as the UA images. A 2DSM generator is used to generate 2D synthetic images (2DSIs) of the selected 3DSM. The images of the unidentified animal are compared to the 2DSIs of the selected 3DSM. When a match probability exceeds a threshold probability, a match is reported to a requester. The requester can obtain permission from an owner to get access to the information pertaining to the UA.