Inverse Colorspace Object Detection for Edge Accuracy
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Solution Overview
Problem
Current image processing technologies face challenges in optimizing object detection in environments due to variations in colorspace models, leading to suboptimal edge detection results and security vulnerabilities in verification processes.
Innovation Solution
The implementation of a system that determines the most appropriate colorspace for a given environment and object, using colorspace conversion techniques to optimize detection by setting cameras or scanning devices to an inverse colorspace associated with the object's color distribution, enhancing detection accuracy and security through the use of ultraviolet and infrared layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional colorspace models are used for object detection, then the detection process is simple, but edge detection accuracy deteriorates due to variations in environment colorspace
Solution Approach 1:
The patent changes the colorspace parameters by converting from the environment's dominant colorspace to an inverse colorspace where the object's colors become most distinguishable. This parameter transformation enables accurate edge detection despite environmental color variations by fundamentally altering the colorspace representation.
Solution Approach 2:
The patent applies inversion by determining the inverse colorspace of the environment rather than working directly with the environment's colorspace. This inversion strategy allows the detection system to identify objects by their color contrast against the inverted environmental background, significantly improving edge detection accuracy.
2Reliability
If standard colorspace detection is used, then the verification process is straightforward, but security vulnerabilities increase
Solution Approach 1:
The patent transforms the verification process by changing from standard colorspace detection to inverse colorspace detection. This parameter change creates a more secure verification mechanism because it requires both the scanner and the encoded content to understand and apply the same colorspace conversion, adding a layer of cryptographic-like security to the verification process.
Solution Approach 2:
The inverse colorspace acts as an intermediary layer between the detection system and the object colors. This intermediary transformation creates a secure bridge that requires specific knowledge (the colorspace conversion parameters) to traverse, thereby enhancing verification security while maintaining a relatively simple overall process.
3Loss of information
If conventional colorspace models are used, then information storage capacity is limited, but increasing capacity increases complexity
Solution Approach 1:
The patent maximizes information storage capacity by changing the colorspace parameters to the inverse colorspace, which optimizes the representation of object colors. This parameter optimization allows for more efficient encoding of color information, increasing the effective information storage capacity of the detected data without adding physical storage elements.
Data Source
AI summary
Techniques, devices, and systems to improve detection and security of a displayed image on a screen of a computer device, such as a device, including detection of an object in an environment, where the object has an inverse-colorspace relationship in relation to the colorspace of the environment. A system includes: a first camera device configured to determine a first underlying colorspace corresponding to an environment, a second camera device configured to determine a second underlying colorspace corresponding to either i) an actual change in the environment or ii) a predicted change in the environment, and a computer device configured to determine an inverse colorspace of at least one of i) the first underlying colorspace or ii) the second underlying colorspace and configured to detect an object in the environment based on the inverse colorspace determination.