4D Volumetric Underwater Scene Segmentation via Recursive Sonar Processing
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Solution Overview
Problem
Existing sonar imaging technologies face challenges in segmenting real-time four-dimensional volumetric underwater data due to environmental conditions like sediment, water column clutter, and acoustic interference, making it difficult to accurately identify moving objects in complex underwater scenes.
Innovation Solution
A method involving a computing device that applies multiple segmentation modes, such as shape-based techniques, recursively over spatial and temporal dimensions, to process 3D volumetric data from sonar transducers, enabling clear identification of moving objects by classifying them based on combinations of processed subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple segmentation modes are applied recursively over spatial and temporal dimensions, then object identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the complex segmentation task into multiple distinct segmentation modes (e.g., shape-based segmentation, intensity-based segmentation, temporal-based segmentation). Each mode processes the volumetric data independently to generate processed subsets, which are then combined to achieve accurate object identification. This segmentation of the segmentation process reduces the complexity of any single processing step while maintaining high overall accuracy.
Solution Approach 2:
The patent extends segmentation from traditional 2D or 3D processing to 4D volumetric data by adding the temporal dimension. Multiple segmentation modes are applied recursively across spatial dimensions (x, y, z) and temporal dimension (time), allowing the system to track objects through time and distinguish moving objects from static background. This multi-dimensional approach improves object identification accuracy by utilizing temporal consistency and motion patterns.
2Reliability
If large amounts of data are processed for reliable segmentation, then segmentation reliability is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent segments the volumetric data processing into multiple independent segmentation modes that can be applied in parallel or in a streamlined sequence. Each mode processes specific aspects of the data (shape, intensity, temporal changes) independently, allowing for efficient computation while maintaining comprehensive analysis. This segmented approach enables reliable segmentation of large datasets in real-time by avoiding the need for exhaustive single-step processing.
Solution Approach 2:
The patent applies segmentation modes recursively, where each mode generates processed subsets that are then used as input for subsequent modes. This recursive application allows the system to progressively refine segmentation results without processing the entire dataset at maximum complexity in a single step. The recursive nature enables incremental improvement of segmentation reliability while maintaining real-time processing capability through efficient resource utilization.
3Measurement precision
If co-segmentation techniques are applied to high resolution sonar images, then seabed texture segmentation is improved, but user interaction requirements increase
Solution Approach 1:
The patent implements unsupervised classification algorithms that automatically perform segmentation without requiring significant user interaction during training. The system self-adjusts parameters and learns from the data patterns automatically, reducing the need for manual training and user intervention. This self-service approach maintains high segmentation precision for seabed textures while significantly improving ease of operation compared to supervised classification systems.
Data Source
AI summary
Technologies for processing a three-dimensional (3D) underwater scene is disclosed. A computing device receives a data set representative of an underwater environment corresponding to sonar data generated by a sonar transducer assembly and receiving parameters. The data set includes 3D volumetric points representative of at least one 3D volumetric data set at one or more time points. The computing device applies segmentation modes on the data set, in which each segmentation mode generates a processed subset of the 3D volumetric points. Each processed subset includes one or more 3D volumetric objects. The computing device classifies one or more 3D volumetric objects based on a combination of the plurality of processed subsets.