Object Segmentation Model Iterative Training for Image Analysis
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Solution Overview
Problem
Existing technologies struggle to accurately identify and classify objects of interest in images, particularly when unknown objects are present, as they fail to separate necessary foregrounds from background regions effectively.
Innovation Solution
An apparatus is developed that includes an input unit for receiving images, an object segmentation unit for separating object regions of interest from the background, and a learning unit that trains models to enhance object segmentation and classification. The learning unit iteratively learns and updates models using training data and existing model outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing object detection techniques are used, then unknown objects can be found, but the objects cannot be classified into specific types
Solution Approach 1:
The patent segments the object detection task into two distinct stages: first, detecting unknown objects using clustering algorithms based on saliency scores and statistical models; second, classifying these detected objects using a separate neural network model. This segmentation allows each stage to optimize for its specific function without interfering with the other, thereby achieving both unknown object detection and accurate classification.
Solution Approach 2:
The patent introduces an intermediary classification model that acts as a bridge between unknown object detection and detailed quantitative analysis. This intermediary neural network takes the detected unknown objects as input and classifies them into specific categories, enabling the system to handle both novel and known objects uniformly while providing the classification information needed for further analysis.
2Adaptability or versatility
If multiple object types are detected in images, then comprehensive object identification is achieved, but the complexity of separating foreground objects from background increases
Solution Approach 1:
The patent performs preliminary segmentation of the image into foreground and background regions before applying object detection and classification. By pre-separating the image content and identifying potential object regions using saliency detection and clustering, the system reduces the complexity of subsequent processing steps, as the neural network only needs to classify already-isolated object candidates rather than processing the entire image.
Solution Approach 2:
The patent transforms the object classification problem from analyzing raw pixel data to analyzing extracted feature vectors. By converting image data into dimensional feature spaces through clustering and saliency computation, the system simplifies the classification task, allowing the neural network to operate on compressed, meaningful representations rather than high-dimensional raw images.
3Measurement precision
If detailed quantitative analysis is performed on all objects, then comprehensive analysis is achieved, but processing time increases due to unnecessary objects
Solution Approach 1:
The patent extracts and isolates only the relevant foreground objects from the image before performing detailed quantitative analysis. By using saliency detection and clustering to identify and separate objects of interest from the background and unnecessary elements, the system applies the computationally intensive neural network classification and analysis only to the extracted object regions, significantly reducing processing time while maintaining analysis completeness for relevant objects.
Data Source
AI summary
An apparatus for generating models for identifying objects of interest from images is disclosed. The apparatus includes an input unit that receives input images, an object segmentation unit that receives the input images from the input unit and separates regions of objects of interest from a background region, and a learning unit that learns models used by the object segmentation unit. The learning unit trains new models using training data and outputs of existing models in relation to the training data, adds the trained new models to the existing model, and repeats the training of the new models and the adding of the trained new models to the existing models.