Constant-Intensity Region Detection for Low-Latency Image Segmentation
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Solution Overview
Problem
Existing image processing techniques are resource-intensive and latency-constrained, making them unsuitable for real-time applications on resource-constrained platforms, particularly in tasks like segmenting background content from foreground content in images.
Innovation Solution
A method for identifying contiguous regions of constant pixel intensity in images using a resource-efficient and latency-efficient approach, involving a two-phase process to determine and analyze pixel intensity distributions and neighboring pixels, without relying heavily on machine-trained components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine-learning models are used to identify background content, then measurement precision is improved, but use of energy and device complexity increase
Solution Approach 1:
The patent segments the image processing task into distinct phases: histogram generation (first phase) and contiguous region search (second phase). This segmentation allows the system to process images in manageable steps, reducing overall energy consumption while maintaining identification accuracy through systematic analysis of pixel intensity distributions and spatial relationships.
Solution Approach 2:
The patent extracts only the necessary information from images by focusing on histogram data and pixel intensity distributions rather than processing every pixel in detail. This extraction approach identifies background content characteristics without requiring computationally intensive machine-learning models, thereby reducing energy consumption while preserving measurement precision.
2Measurement precision
If complex machine-learning models are used to identify background content, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex machine-learning models with simpler, more lightweight algorithms that can be easily implemented and discarded. The histogram-based approach and contiguous region search use basic computational operations rather than heavy neural network architectures, reducing device complexity while maintaining sufficient accuracy for background content identification.
Solution Approach 2:
The patent substitutes complex machine-learning mechanisms with traditional image processing techniques based on histogram analysis and spatial pixel examination. This substitution eliminates the need for trained neural networks and complex computational graphs, simplifying the processing system while achieving the same functional goals through more straightforward algorithms.
3Measurement precision
If techniques requiring processing of plural successive frames are used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary analysis by generating histograms of pixel intensity distributions and identifying contiguous regions within individual frames before any temporal processing occurs. This preliminary action allows the system to make accurate background content determinations from single images without needing to process multiple successive frames, thereby eliminating time loss while maintaining precision.
Solution Approach 2:
The patent creates a simplified representation of the image through histogram data and contiguous region maps, which serves as a copy that captures essential background content characteristics. This copying approach allows accurate identification without processing the full original image data or requiring temporal sequences, reducing processing time while preserving measurement precision.
4Measurement precision
If resource-intensive techniques are used for image processing, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent changes the parameters of image analysis by focusing on histogram distributions and pixel intensity ranges rather than processing individual pixel values in detail. This parameter transformation allows the system to maintain accurate contiguous region identification while using significantly less computational energy, as histograms can be computed efficiently and provide sufficient statistical information for analysis.
Data Source
AI summary
A technique identifies regions of an image characterized by constant pixel intensity in a resource-efficient, latency-efficient, and scalable manner. The technique involves: obtaining a candidate image; determining whether the candidate image contains a contiguous region of pixels having intensity values within a specified range of intensity values; assessing whether the contiguous region satisfies a prescribed test; and selecting or excluding the candidate image for further processing based on a result of the assessing. The operation of determining involve two phases. First, the technique determines a distribution of intensity values within the candidate image. Second, the technique leverages the distribution to search the candidate image for neighboring pixels having intensity values within the specified range of intensity values, beginning from a selected starting pixel in a qualifying subset of pixels. In some examples, the technique is applied to the task of combining supplemental content with the candidate image.