Image Segmentation via Optical Flow and Feature Tracking
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Solution Overview
Problem
Current image identification and machine vision recognition methods are inefficient and require human intervention, leading to low accuracy and poor user experience, especially when dealing with complex or irregular objects.
Innovation Solution
An image segmentation method that uses optical flow analysis to determine the foreground region of a target object, extracts feature points, and performs visual tracking and stationary judgment to segment and track the object, allowing for accurate identification through dynamic movement without manual delimitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual delimitation is used for image identification, then the process can be controlled by users, but the operation becomes inconvenient and user experience deteriorates
Solution Approach 1:
The system automatically performs image segmentation and target object identification without requiring manual user intervention. The computing device autonomously analyzes video images, extracts feature points, and identifies target objects, eliminating the need for users to manually delimit regions while improving operational convenience.
Solution Approach 2:
The patent replaces manual mechanical delimitation operations with automated computer vision algorithms. Optical flow analysis, feature point extraction, and machine learning-based object identification algorithms substitute the manual clicking and dragging operations, transforming a mechanical interaction process into an automated computational process.
2Measurement precision
If traditional picture-based identification is used, then the method is simple to implement, but identification accuracy decreases and learning cost increases
Solution Approach 1:
The system changes the input parameters from static pictures to dynamic video sequences with optical flow information. By utilizing temporal information from multiple frames and motion vectors, the system enhances identification accuracy while the automated processing reduces the learning curve for users.
Solution Approach 2:
The system performs preliminary processing of video images by pre-extracting optical flow fields and feature points before actual identification is needed. This preliminary analysis prepares the data structure in advance, enabling faster and more accurate identification while reducing the computational burden during user interaction.
3Adaptability or versatility
If static picture input is used for machine vision, then the process is straightforward, but the system cannot accurately identify irregular objects or handle complex shot content
Solution Approach 1:
The system transitions from static picture analysis to dynamic video sequence processing. By analyzing temporal changes and motion patterns across multiple frames, the system can accurately identify irregular objects and handle complex scenes that static images cannot capture, enhancing adaptability while managing system complexity through efficient optical flow computation.
Data Source
AI summary
An image segmentation method and an image segmentation device are provided. The method comprises receiving a video image of a dynamic movement of a target object, acquiring a full-image optical flow of the video image to estimate a first displacement of each pixel therein, acquiring a background optical flow of the video image to estimate a second displacement of a background pixel therein; comparing the first displacement with the second displacement to obtain a foreground region of the target object; extracting feature points in the video image in the foreground region, calculating a probability density of the feature points to determine a number of the target objects; performing visual tracking and movement trajectory analysis on the target object to track the same; performing stationary judgment and image segmentation on the target object according to an interframe displacement of the feature points, an interframe cutting window similarity and tracking box scaling.