Hierarchical Model Tree for Real-Time Object Recognition
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Solution Overview
Problem
Conventional object recognition systems face challenges in accuracy and speed, particularly in distinguishing similar objects and handling variations in shape, color, pose, illumination, and camera changes, while also requiring large databases and being slow for real-time applications.
Innovation Solution
A method that computes interest points and extracts tokens from training images, groups matched tokens into groups, and builds a hierarchical model tree to represent object models, enabling efficient object identification in target images by matching tokens and verifying hypotheses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object recognition systems use large databases of training pictures, then recognition accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The patent segments the object recognition process into two distinct phases: an offline training phase that builds a compact model tree from training images, and an online recognition phase that uses the pre-built model for fast matching. This segmentation allows the system to achieve high accuracy through thorough offline training while maintaining low complexity during online operation, resolving the contradiction between accuracy and database size requirements.
Solution Approach 2:
The system performs preliminary actions during the offline training phase by pre-processing training images to extract tokens, group them, and build the model tree structure before actual recognition occurs. This preliminary action transfers the computational burden to the offline phase, enabling fast and accurate online recognition without requiring large databases during runtime.
2Productivity
If conventional recognition systems process images in real-time, then speed is improved, but recognition accuracy decreases due to simplified processing
Solution Approach 1:
The patent divides the recognition system into offline training (accurate but slow) and online recognition (fast but simple) phases. The offline phase performs complex token extraction, grouping, and model tree construction to achieve high accuracy, while the online phase uses the pre-built model for rapid matching, thus achieving both high speed and high accuracy by segmenting the processing tasks.
Solution Approach 2:
The system performs all complex processing operations (token extraction, grouping, model building) as preliminary actions during the offline training phase. This allows the online recognition phase to operate quickly by simply matching tokens against the pre-built model tree, resolving the contradiction between processing speed and recognition accuracy.
3Measurement precision
If the system uses detailed token matching for accurate identification, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts essential features from training images in the form of tokens during the offline phase and organizes them into a hierarchical model tree structure. During online recognition, only token matching against the pre-organized model is required, which is computationally efficient. This extraction and organization of essential information beforehand resolves the contradiction between detailed matching for accuracy and processing time.
Solution Approach 2:
The system performs the time-consuming token extraction, grouping, and model construction as preliminary actions during offline training. This allows the online phase to achieve accurate identification through fast token matching operations, effectively separating the accurate but slow processing from the fast but simpler runtime operations.
4Adaptability or versatility
If the system builds a comprehensive model from all training images, then coverage of object variations is improved, but model complexity and memory requirements increase
Solution Approach 1:
The patent segments the model representation into a hierarchical tree structure where different levels capture different aspects of object variations. The model tree organizes tokens hierarchically, grouping similar tokens together and representing object variations at multiple levels of abstraction. This segmentation allows comprehensive coverage of variations while keeping each node's complexity manageable.
Solution Approach 2:
The system transforms the flat, comprehensive model into a hierarchical tree structure, adding a dimensional organization to the model representation. This hierarchical dimension allows the system to cover extensive object variations by distributing them across multiple tree levels and branches, reducing the complexity at any single level while maintaining overall comprehensiveness.
Data Source
AI summary
Object recognition techniques are disclosed that provide both accuracy and speed. One embodiment of the present invention is an identification system. The system is capable of locating objects in images by searching for local features of an object. The system can operate in real-time. The system is trained from a set of images of an object or objects. The system computes interest points in the training images, and then extracts local image features (tokens) around these interest points. The set of tokens from the training images is then used to build a hierarchical model structure. During identification/detection, the system computes interest points from incoming target images. The system matches tokens around these interest points with the tokens in the hierarchical model. Each successfully matched image token votes for an object hypothesis at a certain scale, location, and orientation in the target image. Object hypotheses that receive insufficient votes are rejected.