Automatic Perspective Transformation via ML Object Detection
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Solution Overview
Problem
Converting the perspective of an image is a computationally expensive and labor-intensive task, particularly in applications like self-driving vehicles and augmented reality, where accurate true distance measurements are crucial but current methods require manual input and are not efficient enough for emerging technologies.
Innovation Solution
A method using machine learning to detect objects with known shapes in an image, automatically predicting corresponding points in a different perspective, and constructing a transformation matrix to transform the image perspective without user input, allowing for efficient and accurate perspective transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to convert image perspective, then transformation accuracy can be achieved, but the process becomes labor-intensive and computationally expensive
Solution Approach 1:
The system automatically detects objects with known shapes, identifies their vertices, predicts corresponding points in the target perspective, and constructs the transformation matrix without requiring manual user input. The machine learning model performs self-service by autonomously completing the entire perspective transformation pipeline.
Solution Approach 2:
The patent replaces manual mechanical operations (user clicking and dragging to define vertices) with an automated machine learning-based system that uses object detection, vertex identification, and neural network prediction to achieve the same transformation goal more efficiently.
2Measurement precision
If traditional perspective transformation methods are used, then accurate distance measurements can be obtained, but computing resources are excessively consumed
Solution Approach 1:
The system performs preliminary object detection and vertex identification using machine learning before the actual perspective transformation. By pre-identifying key geometric features and using the known shape constraints to predict target points, the system prepares the necessary data structures in advance, reducing the computational burden during the transformation phase.
Solution Approach 2:
The patent leverages the known shape parameters of objects (such as rectangular dimensions or circular radius) as additional constraints in the transformation process. By incorporating these predefined geometric parameters, the system reduces the degrees of freedom in the transformation problem, leading to more efficient computation while maintaining accuracy.
3Extent of automation
If automated machine learning methods are used for perspective transformation, then computing efficiency improves, but user input is completely eliminated requiring robust automatic detection
Solution Approach 1:
The system uses a universal machine learning object detection model that can identify various types of objects with known shapes (rectangles, circles, polygons) across different images and perspectives. This multi-functional detection capability enables the automated system to handle diverse scenarios without requiring task-specific customization.
Solution Approach 2:
The system employs feedback mechanisms where the detected object vertices and predicted target points are used to evaluate and refine the transformation matrix. The known shape constraints provide feedback validation, ensuring that the automated detection and transformation results are geometrically consistent and accurate.
Data Source
AI summary
A method may include obtaining an image of a scene from a first perspective, the image including an object, and detecting the object in the image using a machine learning process, where the object may be representative of a known shape with at least four vertices at a first set of points. The method may also include automatically predicting a second set of points corresponding to the at least four vertices of the object in a second perspective of the scene based on the known shape of the object. The method may additionally include constructing, without user input, a transformation matrix to transform a given image from the first perspective to the second perspective based on the first set of points and the second set of points.