Signature Generator Spanning Elements for Robust Object Detection
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Solution Overview
Problem
Object detection processes are computationally intensive and sensitive to acquisition parameters such as angle and scale, necessitating robust and efficient methods for processing vast amounts of media units across various applications.
Innovation Solution
A method for configuring spanning elements of a signature generator involves receiving and processing sensed information units to generate representations, finding decorrelated elements, mapping them to unique object identifiers, and associating these identifiers with spanning elements, with iterative processes and dimension expansion followed by merge operations to enhance robustness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object detection methods are used to process vast amounts of media units, then object detection capability is provided, but computational resources and memory resources are excessively consumed
Solution Approach 1:
The patent segments the object detection process into multiple iterations, where each iteration processes a subset of media units and generates partial signatures. These partial signatures are then merged to form complete object signatures. This segmentation allows the system to process vast amounts of media units in manageable chunks, reducing peak computational and memory resource requirements while maintaining detection capability.
Solution Approach 2:
The patent transforms the detection problem by projecting media units into a high-dimensional signature space. Instead of directly comparing raw media units, the system generates sparse multidimensional representations (signatures) that capture essential features. This dimensional transformation reduces the computational complexity of comparisons and enables efficient processing of large volumes of data with reduced resource consumption.
2Reliability
If traditional object detection methods are used, then object detection is provided, but the process is sensitive to acquisition parameters such as angle and scale
Solution Approach 1:
The patent implements a dynamic signature generation process that adapts to different acquisition parameters. The signature generator dynamically adjusts the projection process based on the specific characteristics of each media unit, including its acquisition angle and scale. This dynamic adaptation allows the system to maintain robust object detection across varying conditions by generating signatures that are invariant to these parameter changes.
Solution Approach 2:
The patent changes the parameter space by transforming physical acquisition parameters (angle, scale) into a standardized signature space. Instead of detecting objects directly from images with varying parameters, the system projects all media units into a common signature domain where these variations are normalized. This parameter transformation enables the detection process to be insensitive to acquisition conditions while maintaining reliable object identification.
3Reliability
If iterative processes with dimension expansion and merge operations are used, then robustness and efficiency of object detection are improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary configuration of the signature generator by pre-determining the spanning elements and their associations with object identifiers based on training data or predefined patterns. This preliminary setup phase separates the complex configuration work from the actual detection process. During runtime, the pre-configured signature generator simply applies the established spanning elements, significantly reducing the operational complexity while maintaining the robustness provided by the iterative dimension expansion and merge operations.
Data Source
AI summary
Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.