Irregular Shape Encoding via Hierarchical Angle-Based Descriptors
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Solution Overview
Problem
Conventional contour descriptors, such as angle-based and vertex-based descriptors, are limited in representing irregular shapes, leading to errors in shape reconstruction and difficulties in training learning-based algorithms for image segmentation due to noise sensitivity and correspondence interlacing issues.
Innovation Solution
A hierarchical encoding procedure using multiple local angle-based contour descriptors is employed, where the shape is subdivided into partitions until they are sufficiently regular, allowing for accurate representation of irregular shapes by adaptively generating contour descriptors with increased density at regions of rapidly varying curvatures, and a low-rank robust subspace is computed to approximate distance vectors, reducing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a conventional angle-based contour descriptor is used to represent a boundary of an object, then the descriptor is compact and consumes small memory, but it is limited to representing substantially regular shapes and results in errors when the shape is reconstructed
Solution Approach 1:
The shape boundary is divided into multiple contours, each represented by a separate angle-based contour descriptor. This segmentation allows the system to handle irregular shapes by breaking them down into regular sub-regions, maintaining compact representation while improving adaptability to various shape types.
2Adaptability or versatility
If a vertex-based contour descriptor is used to represent a boundary of a shape, then the descriptor can fit nearly any shape including highly irregular shapes, but it is more sensitive to noise due to 2-dimensional sampling sequences
Solution Approach 1:
The boundary is segmented into multiple contours, each with its own angle-based descriptor. This segmentation reduces noise sensitivity by localizing the representation to regular sub-regions, avoiding the global 2D sampling that causes noise amplification in vertex-based descriptors.
Solution Approach 2:
Different local regions of the shape boundary are represented with angle-based descriptors tailored to their specific geometric characteristics. This local approach maintains reliability by adapting the representation method to the local regularity of each contour segment.
3Adaptability or versatility
If a vertex-based contour descriptor is used for training learning-based algorithms, then the descriptor can represent irregular shapes, but problems associated with correspondence interlacing arise making learning difficult
Solution Approach 1:
The shape is segmented into multiple contours, each represented by an angle-based descriptor with a consistent 1D parameterization. This segmentation eliminates correspondence interlacing problems by providing a uniform representation framework across all contours, simplifying the learning process while maintaining the ability to represent irregular shapes.
4Measurement precision
If multiple local angle-based contour descriptors are generated with increased density at regions of rapidly varying curvatures, then accurate representation of irregular shapes is achieved, but the memory usage increases
Solution Approach 1:
The system applies higher descriptor density locally at regions of rapidly varying curvatures while using lower density in regions of less variance. This local quality approach maintains high measurement precision where needed while controlling overall memory consumption by adapting the representation resolution to the local geometric complexity.
Solution Approach 2:
The descriptor density is dynamically adjusted based on the local curvature characteristics of the shape boundary. This dynamic adaptation allows the system to optimize the balance between representation accuracy and memory usage by concentrating computational resources on geometrically complex regions.
Data Source
AI summary
A method performed by a processor of a computing system is described herein, where the method includes obtaining an image that includes an object having a shape, where a boundary of the shape of the object in the digital image is labeled in the digital image. The method also includes computing an encoding for the shape, where computing the encoding for the shape includes partitioning the shape into multiple partitions. Computing the encoding for the shape further includes, for the multiple partitions, computing angle-based contour descriptors that represent boundaries of the partitions, where the encoding for the shape of the object is based upon the angle-based contour descriptors.