Horizon Identification in Images via Pixel Row Analysis
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Solution Overview
Problem
Current methods lack an efficient way to identify the horizon in an image, which is crucial for applying image effects such as text and transition effects, as they rely on manual or imperfect automated processes.
Innovation Solution
A system comprising physical processors configured by machine-readable instructions to analyze image data, determine pixel parameters, and use machine learning to identify pixel rows that represent the horizon, enabling accurate detection and application of image effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify the horizon in an image, then the accuracy of horizon identification can be maintained, but the productivity and automation level deteriorate
Solution Approach 1:
The system enables automatic horizon identification through self-service mechanisms by analyzing image data autonomously. The processor automatically determines pixel parameters, calculates average values for pixel rows, and identifies the horizon without requiring manual intervention, thereby resolving the contradiction between maintaining accuracy and improving productivity
Solution Approach 2:
The patent replaces manual mechanical horizon identification with an automated computational system. The system uses digital image analysis, pixel parameter calculation, and average value determination to substitute human manual processes, achieving both high accuracy and improved productivity through automation
2Productivity
If imperfect automated processes are used to identify the horizon, then the productivity improves, but the measurement precision and reliability of horizon identification deteriorate
Solution Approach 1:
The system segments the image into individual pixel rows and analyzes each row separately to determine average parameter values. This segmentation approach allows the automated system to systematically process each row independently, improving both productivity through automation and measurement precision through detailed individual analysis
Solution Approach 2:
The system implements feedback mechanisms by calculating and comparing average parameter values across different pixel rows. The processor uses this feedback information to iteratively refine horizon identification, ensuring high measurement precision while maintaining automated productivity
3Ease of operation
If a simple automated method is used for horizon identification, then the ease of operation improves, but the measurement precision and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by first determining pixel parameters for all pixels and calculating average values for each pixel row before identifying the horizon. This preliminary processing establishes a reliable foundation for accurate horizon detection while maintaining ease of operation through systematic automation
Solution Approach 2:
The system creates a digital representation (copy) of the image data in the form of pixel parameter values and average calculations. This copying process allows the automated system to analyze and identify the horizon reliably without direct manual manipulation, improving both ease of operation and measurement precision
Data Source
AI summary
Systems and method of identifying a horizon depicted in an image are presented herein. Information defining an image may be obtained. The image may include visual content comprising an array of pixels. The array may include pixel rows. Parameter values for a set of pixel parameters of individual pixels of the image may be determined. Individual average parameter values of the individual pixel parameters of the pixels in the individual pixel rows may be determined. Based on the average parameter values a pixel row may be identified as depicting a horizon in the image.