Visual Recognition Using Scale Invariant Feature Extraction
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Solution Overview
Problem
Current computer vision systems face challenges in recognizing objects in unconstrained environments due to lack of invariance to object presentation, lighting, occlusion, and background, requiring significant computational power and prior knowledge, limiting their applicability in real-world applications.
Innovation Solution
The method involves extracting unique points from images that are invariant to scale and orientation, normalizing image patches to a fixed size, and comparing them using a database of learned objects, allowing for robust recognition regardless of object presentation, lighting, or background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems are designed to recognize objects in unconstrained environments, then recognition accuracy improves, but computational power requirements increase dramatically
Solution Approach 1:
The patent segments the visual recognition task into distinct stages: feature extraction (identifying corners, edges, blobs), descriptor generation (creating scale and rotation invariant descriptions), and matching/comparison. This segmentation allows each stage to be optimized independently, reducing overall computational requirements while maintaining accuracy in unconstrained environments
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing scale and rotation invariant descriptors for objects during a training phase. These pre-computed features are then rapidly compared against query images, eliminating the need for exhaustive search and dramatically reducing real-time computational power requirements
2Adaptability or versatility
If computer vision systems are designed to handle general visual search in uncontrolled environments, then versatility improves, but system complexity and prior knowledge requirements increase
Solution Approach 1:
The patent implements universality through scale and rotation invariant features that can recognize objects across multiple transformations and viewing conditions. The same feature extraction and matching pipeline handles diverse object types and environmental conditions, providing versatile visual search capability without requiring separate specialized systems for each scenario
Solution Approach 2:
The patent changes parameters by transforming image data into scale and rotation invariant feature spaces. By representing objects in terms of their invariant geometric and photometric properties rather than raw pixel values, the system achieves versatility across different viewing conditions while simplifying the matching process through standardized feature comparisons
3Productivity
If computer vision systems limit object variability to reduce computational cost, then recognition speed improves, but adaptability to real-world scenarios deteriorates
Solution Approach 1:
The patent applies asymmetry by treating scale and rotation as independent dimensions that can be normalized separately. Rather than limiting object variability, the system asymmetrically decomposes the transformation space, normalizing scale through size estimation and rotation through orientation calculation, allowing rapid recognition across diverse object presentations while maintaining high recognition speed
Data Source
AI summary
A method for generating a signal based on a visual image includes photographing a target object with a digital camera to obtain a target image; receiving the target image into a processor that is in communication with the camera; cross-correlating the target image with a structure having a variety of scales across the target image; and based on cross-correlating the target image, generating a signal for output on a device associated with the camera. A visual recognition system is also disclosed.