CNN Image Tensor Segmentation for Context-Aware Foreground Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing technologies face challenges in adapting to different context information and suffer from high computational costs, making it difficult to efficiently segment foreground objects across varying environments, especially in resource-constrained devices like portable cameras.
Innovation Solution
A method and system that utilize a Convolutional Neural Network (CNN) to analyze images by determining an image tensor and context tensor, combining them to form a combined tensor for estimating object attributes, which improves adaptability and reduces computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional CNN is used for foreground segmentation, then segmentation capability is achieved, but computational cost is high and adaptability to different contexts is poor
Solution Approach 1:
The patent segments the input image into multiple patches and processes them separately through the CNN network. This allows the model to handle different context information in each patch independently, improving adaptability while reducing the computational burden on the entire image at once. The segmentation approach enables parallel processing and reduces memory requirements.
Solution Approach 2:
The patent introduces context information as an additional dimension to the input data, combining spatial information from image patches with contextual information. This multi-dimensional approach allows the network to adapt to different contexts without requiring a completely different model architecture, thereby improving versatility while maintaining computational efficiency.
2Measurement precision
If CNN processes entire images for foreground segmentation, then comprehensive analysis is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent divides the image into smaller patches for processing. Each patch is processed independently through the CNN, which reduces the computational complexity from O(n^2) for the entire image to O(k*n^2) where k is the number of patches. This segmentation approach maintains segmentation accuracy while significantly reducing processing time and memory requirements.
Solution Approach 2:
The patent processes only relevant patches containing potential foreground objects rather than analyzing every pixel in the entire image. By identifying and processing only the necessary portions of the image, the system achieves accurate foreground segmentation while minimizing processing time and computational resource consumption.
Data Source
AI summary
The present disclosure provides a system of analysing an object in an image. The system comprises a processor and a memory in communication with the processor. The memory has application programs that are executable by the processor, wherein the processor executes the application programs to perform a method of analysing the object in the image. The method comprises the steps of determining an image tensor of the image; determining context information of the image; determining a context tensor of the determined context information; combining the image tensor and the context tensor to form a combined tensor; and estimating an attribute of the object based on the combined tensor.