Object Recognition via Feature Signature Indexing
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Solution Overview
Problem
Existing image recognition solutions face challenges in accuracy and scalability, struggling to identify objects under geometric and photometric transformations, and are not efficient in searching millions of images in real-time.
Innovation Solution
An object recognition system that generates a signature for input images by detecting feature points, computing descriptions based on dominant gradient directions, and using index mapping to match against training images, facilitating fast and accurate object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing image recognition solutions are used, then object identification can be performed, but accuracy deteriorates under geometric and photometric transformations
Solution Approach 1:
The patent segments the image processing into distinct functional units: feature detection unit identifies key points, feature description unit computes descriptors, and signature generation unit creates compact representations. This segmentation allows each unit to specialize in handling specific aspects of transformation robustness independently.
Solution Approach 2:
The patent changes parameters by computing feature descriptors based on dominant gradient directions and organizing them into histograms that are invariant to geometric transformations. The signature generation transforms the feature space into a compact representation that maintains accuracy under photometric and geometric variations.
2Reliability
If exhaustive image search is performed, then complete object identification is achieved, but processing speed deteriorates when searching millions of images
Solution Approach 1:
The patent extracts essential visual features into compact signatures that capture the most discriminative information. By taking out only the dominant gradient directions and organizing them into histograms, the system reduces the data dimensionality while preserving identification accuracy, enabling fast comparison across millions of images.
Solution Approach 2:
The patent changes the parameter representation from full image data to compact feature signatures. The signature generation unit transforms complex image features into condensed representations that maintain discriminative power, allowing efficient indexing and rapid search through large image databases.
3Measurement precision
If detailed feature descriptions are computed for all images, then recognition accuracy is improved, but computational complexity and storage requirements increase
Solution Approach 1:
The patent extracts only the most salient feature information by computing descriptors based on dominant gradient directions and organizing them into histograms. This extraction process removes redundant information while preserving the essential characteristics needed for accurate recognition, reducing computational complexity and storage requirements.
Solution Approach 2:
The patent segments the feature description into manageable components: detection of feature points, computation of local descriptors, and aggregation into global signatures. This segmentation allows the system to process detailed features in a computationally efficient manner by handling them in discrete stages rather than as a monolithic operation.
Data Source
AI summary
The present invention discloses methods and systems for recognizing an object in an input image based on stored training images. An object recognition system the input image, computes a signature of the input image, compares the signature with one or more stored signatures and retrieves one or more matching images from the set of training images. The matching images are then displayed to the user for further action.