Semantic Proposal Segmentation Using Spatial-Temporal Echo State Networks
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Solution Overview
Problem
Current deep learning algorithms for image and video annotation in autonomous driving systems require large amounts of manually annotated data, which is time-consuming and costly, and lack scalability across different datasets, failing to effectively utilize spatial and temporal features for semantic segmentation.
Innovation Solution
A modified echo state network (ESN) framework that incorporates spatial and temporal features for batch-wise training, enabling fast and accurate semantic proposals by learning from a small dataset and generating larger feature vectors per pixel, thus reducing manual annotation time and improving scalability across various datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If deep learning algorithms are used for image and video annotation, then high-level feature extraction capability is improved, but the amount of training data required increases significantly
Solution Approach 1:
The patent introduces an intermediary component (pre-trained feature extractor or proposal generator) that bridges the gap between raw images and the segmentation model. This intermediary pre-processes images to extract useful features and generate proposals, allowing the main model to train with fewer samples while still accessing high-level feature representations that would normally require large datasets.
2Measurement precision
If manually-annotated image data is used for training, then annotation quality is improved, but time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing images to generate proposals and pre-annotate regions before the actual training process. This preliminary step creates a reduced set of candidate regions that need manual verification, significantly reducing the time and cost of manual annotation while maintaining quality through focused expert review of only the most promising candidates.
Solution Approach 2:
The system implements self-service by automatically generating proposals and pre-annotations without human intervention. The algorithm autonomously identifies potential regions of interest and creates initial annotations, which then require minimal human oversight for validation and refinement, thereby reducing dependency on extensive manual annotation while preserving quality.
3Measurement precision
If proposal generation algorithms are optimized for one dataset, then performance on that dataset is improved, but scalability to other datasets deteriorates
Solution Approach 1:
The patent designs the proposal generation algorithm with universal features that can adapt to multiple datasets. The system uses configurable parameters and feature extractors that can be adjusted or re-trained for different data characteristics, allowing the same core algorithm to maintain high performance across diverse datasets without requiring complete re-implementation for each new dataset.
4Manufacturing precision
If super-pixel segmentation is used for image segmentation, then segmentation detail is improved, but processing speed deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: first generating coarse proposals that identify potential regions of interest, then applying detailed segmentation only to these selected regions rather than the entire image. This hierarchical segmentation approach maintains high precision where needed while significantly improving overall processing speed by avoiding exhaustive super-pixel segmentation across the full image.
Data Source
AI summary
Methods and systems for providing fast semantic proposals for image and video annotation including: extracting image planes from an input image; linearizing each of the image planes to generate a one-dimensional array to extract an input feature vector per image pixel for the image planes; abstracting features for a region of interest using a modified echo state network model, wherein a reservoir increases feature dimensions per pixel location to multiple dimensions followed by feature reduction to one dimension per pixel location, wherein the echo state network model includes both spatial and temporal state factors for reservoir nodes associated with each pixel vector, and wherein the echo state network model outputs a probability image; post-processing the probability image to form a segmented binary image mask; and applying the segmented binary image mask to the input image to segment the region of interest and form a semantic proposal image.