Video Stream Binarization via Adaptive Blur and Thresholding
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Solution Overview
Problem
Existing binarization techniques for video streams face challenges in real-time processing due to high processor intensity, poor performance under uneven or poor lighting conditions, and difficulty in handling content differing from the predefined content type used in model training.
Innovation Solution
A binarization system that converts video stream frames into single-channel images, generates approximation images through blurring, and applies thresholding to produce binary images, enabling real-time processing and object detection even in poor lighting conditions, with the ability to identify and track objects across a video stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional binarization methods with machine learning models are used, then binarization accuracy is improved, but processor intensity and computational complexity increase significantly
Solution Approach 1:
The patent replaces expensive, computationally intensive machine learning models with simpler, lightweight binarization algorithms that require minimal processing power. This allows real-time binarization on mobile devices without sacrificing excessive performance, effectively using 'cheap' computational methods instead of 'expensive' deep learning models.
Solution Approach 2:
The patent transforms the complex binarization problem by changing key parameters: instead of using full-color images with machine learning, it converts to grayscale, applies adaptive thresholding with optimized parameters, and uses morphological operations with carefully selected kernels. These parameter changes dramatically reduce computational complexity while maintaining acceptable binarization quality.
2Productivity
If real-time processing is implemented, then processing speed is improved, but binarization quality deteriorates under poor lighting conditions
Solution Approach 1:
The patent performs preliminary image enhancement operations before binarization, including adaptive histogram equalization and noise filtering. These preliminary actions prepare the image data to be more amenable to rapid binarization algorithms, ensuring both real-time processing capability and maintained quality even in poor lighting conditions.
Solution Approach 2:
The patent implements dynamic parameter adjustment based on lighting conditions. The binarization threshold and processing parameters are adapted in real-time according to the detected lighting environment, allowing the system to maintain quality across varying conditions while preserving real-time processing speed.
3Measurement precision
If adaptive binarization for different content types is implemented, then binarization accuracy is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent segments the binarization process into distinct stages: preprocessing, threshold determination, actual binarization, and post-processing. Each stage handles specific aspects of the problem independently, allowing optimized algorithms for each segment rather than a single complex unified approach, thereby reducing overall processing overhead.
Solution Approach 2:
The patent develops a universal binarization framework that handles multiple content types (text, objects, scenes) through a single adaptive system. Rather than implementing separate specialized algorithms for each content type, the system uses content-agnostic preprocessing and adaptive thresholding that automatically adjusts to different input types, reducing complexity while maintaining accuracy.
Data Source
AI summary
Systems, devices, media, and methods are presented for receiving a set of images in a video stream, receiving input that selects an image capture mode icon from a plurality of image capture mode icons; selecting a blur operation from a plurality of blur operations based on the image capture mode icon selected by the input; and modifying the video stream based on the selected blur operation.