Metric-Based Object Recognition for Monochrome Fidelity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object recognition technologies struggle to efficiently recognize objects with little color variation, known as monochrome objects, especially under different lighting environments, as they lose recognizable features when converted to grayscale, leading to reduced recognition fidelity with image processing algorithms.
Innovation Solution
The use of object-specific metric maps that map RGB values from each pixel of a digital representation to a single metric channel, allowing unmodified image analysis algorithms to enhance descriptor detection and recognition of monochrome objects by converting color data into tailored metric values, independent of lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image data is converted to grayscale for object recognition, then processing compatibility with standard algorithms is improved, but recognition fidelity for monochrome objects deteriorates
Solution Approach 1:
The patent introduces an intermediary color space transformation process that converts RGB image data through a custom color space with enhanced chroma channels before feeding to standard grayscale-based algorithms. This intermediary transformation preserves color information that would otherwise be lost, acting as a mediator between color-rich input and grayscale-processing algorithms.
Solution Approach 2:
The patent modifies the color space parameters by defining a custom color space with enhanced chroma channels (Cb and Cr) that preserve color variation information. This parameter change allows standard algorithms to operate while maintaining the ability to distinguish monochrome objects through preserved chroma data.
2Measurement precision
If color data is preserved in RGB format, then object resolution power for monochrome objects is improved, but processing efficiency deteriorates due to lack of algorithm optimization
Solution Approach 1:
The patent performs preliminary color space transformation before the main recognition process, converting RGB data to a custom color space with enhanced chroma preservation. This preliminary action ensures that color information is preserved upfront, allowing subsequent standard algorithms to process the data efficiently without requiring modification.
Solution Approach 2:
The custom color space transformation serves multiple functions simultaneously: it preserves chroma information for monochrome object detection, maintains compatibility with standard grayscale algorithms, and enables efficient processing through existing optimized image processing pipelines.
3Device complexity
If standard image processing algorithms are used without modification, then system complexity is reduced, but recognition accuracy for monochrome objects deteriorates
Solution Approach 1:
The patent segments the processing pipeline into two distinct stages: (1) color space transformation with enhanced chroma preservation, and (2) standard algorithm processing. This segmentation allows each component to be optimized independently while maintaining overall system simplicity and leveraging existing well-tested algorithms.
Data Source
AI summary
Apparatus, methods and systems of object recognition are disclosed. Embodiments of the inventive subject matter generates map-altered image data according to an object-specific metric map, derives a metric-based descriptor set by executing an image analysis algorithm on the map-altered image data, and retrieves digital content associated with a target object as a function of the metric-based descriptor set.