Parametric Curve Object Detector Network
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Solution Overview
Problem
Current object detection techniques using axis-aligned bounding boxes (AABBs) fail to accurately capture the shape of objects, and image segmentation models struggle with ambiguous pixel-level classifications and object importance, leading to suboptimal boundary predictions and object detection performance.
Innovation Solution
The use of parametric curves defined by multiple control points for object detection, which allows for tighter boundary predictions and correct representation of object importance, by predicting K curve control points instead of 4 AABB values, and incorporating object shape regularization to prevent overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If axis-aligned bounding boxes (AABBs) are used for object detection, then the detection process is simple and fast, but the boundary predictions do not accurately capture the shape of objects
Solution Approach 1:
The patent applies curved parametric boundaries instead of straight AABB edges to better fit object shapes. The detector network predicts parametric curve parameters that define smooth curved boundaries, allowing the detection regions to conform to the actual contours of objects while maintaining computational efficiency through parameterized representations.
Solution Approach 2:
The patent transforms the detection output from simple AABB parameters (x, y, width, height) to parametric curve parameters. This parameter transformation enables the boundaries to represent complex shapes through mathematical curves defined by control points and parameters, significantly improving boundary accuracy while keeping the number of parameters manageable.
2Measurement precision
If pixel-level segmentation is used to capture object shapes, then boundary accuracy is improved, but object importance cannot be properly expressed and computational complexity increases
Solution Approach 1:
The patent segments the boundary representation into discrete parametric control points and curve segments rather than using continuous pixel-level segmentation. This segmentation approach captures essential boundary shapes through a small number of parametric elements, avoiding the computational burden of pixel-level processing while maintaining accurate shape representation.
Solution Approach 2:
The patent transitions from 2D pixel-level segmentation to a parametric representation that uses control points and curve parameters. This dimensional transformation compresses the boundary information into a lower-dimensional parametric space, reducing computational complexity while preserving shape accuracy through the mathematical properties of parametric curves.
Data Source
AI summary
Embodiments may provide improved techniques for object detection so as to improve the finding of objects and the accuracy of the boundary predictions using parametric curves defined by multiple control points. For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise receiving an image, extracting from the image a plurality of features related to objects shown in the image, generating, from the extracted features, at least one plurality of points representing a parametric curve bounding an object shown in the image; and outputting the plurality of points representing the parametric curve.