Image Processing via Salient Instance Segmentation
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Solution Overview
Problem
Existing image processing models for object recognition are often computationally expensive and lack accuracy, limiting their widespread adoption in applications such as robotics and AI-driven image analysis.
Innovation Solution
A computer-implemented method and system for image processing that identifies objects by receiving input images, identifying salient instances, determining key points (center and peripheral points), segmenting instances using these key points, and predicting objects based on segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image processing models are used for object recognition, then object detection can be performed, but the computational cost is too high and accuracy is insufficient
Solution Approach 1:
The image processing is segmented into distinct stages: saliency detection to identify potential objects, key point identification to locate center and peripheral points, and segmentation to define object boundaries. This multi-stage segmentation approach reduces computational burden by focusing processing only on salient regions rather than entire images, while improving accuracy through progressive refinement at each stage.
Solution Approach 2:
The saliency detection step performs preliminary action by pre-identifying potential object regions before detailed processing. By detecting salient instances first and generating saliency scores, the system filters out non-relevant areas, reducing the computational scope for subsequent key point identification and segmentation operations.
2Measurement precision
If existing image processing models are used for object recognition, then object detection can be performed, but the accuracy is not sufficient for widespread usage
Solution Approach 1:
The system employs dynamic filtering where segmentation filters are adaptively applied based on saliency scores. The filtering process is dynamic rather than static, allowing the system to adjust processing intensity and methods based on the salience of detected instances, thereby improving accuracy while maintaining productivity through intelligent resource allocation.
Solution Approach 2:
The invention changes key parameters in the processing pipeline: using saliency scores as a parameter to weight processing priority, using key point coordinates as parameters to guide segmentation, and using segmentation masks as parameters for final object identification. These parameter changes enable more accurate object recognition while controlling computational complexity.
3Manufacturing precision
If key point identification and segmentation are performed, then segmentation accuracy is improved, but processing complexity increases
Solution Approach 1:
The processing complexity is managed by segmenting the task into modular components: saliency detection module, key point identification module, and segmentation module. Each module handles a specific aspect of the problem, making the overall complex process more manageable and implementable through distributed or sequential processing.
Solution Approach 2:
Saliency scores act as an intermediary between initial object detection and final segmentation. The scores mediate the processing by providing a confidence metric that guides subsequent key point identification and segmentation operations, reducing the need for complex processing in low-confidence regions.
Data Source
AI summary
A computer-implemented method of image processing to identify one or more objects in an image including receiving one or more input images, wherein each input image includes one or more salient instances, wherein each salient instance is indicative of an object, identifying a plurality of key points associated with each salient instance within each input image, segmentation of salient instances in each image by utilizing the plurality of key points, wherein the key points include a centre point and peripheral points of each salient instance, and predicting one or more objects within each image based on the segmentation of each salient instance.