Self-Spatial Adaptive Normalization for Image Segmentation
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Solution Overview
Problem
Current image region segmentation technologies using deep learning do not incorporate spatial information normalization, leading to degraded performance in segmenting image regions.
Innovation Solution
The implementation of a self-spatial adaptive normalization (SSAN) method within an image region segmentation system, which includes an encoder and decoder with convolution, normalization, and down-sampling blocks, uses spatial weight parameters to perform adaptive normalization, enhancing segmentation performance by considering spatial characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If spatial adaptive normalization is not performed in deep learning networks for image region segmentation, then the network structure remains simple, but region segmentation performance degrades
Solution Approach 1:
The patent introduces spatial adaptive normalization that dynamically changes normalization parameters (gamma and beta) based on spatial location in the image. This allows different regions to be normalized differently, improving segmentation performance by adapting to spatial variations in the image data.
Solution Approach 2:
The normalization process is segmented into different spatial regions with independent normalization parameters. Each region can have its own gamma and beta values, allowing localized optimization of segmentation performance without uniformly complicating the entire network.
2Loss of information
If spatial information is not utilized in normalization, then the processing is simpler, but important spatial characteristics of the image are lost
Solution Approach 1:
The patent applies local quality by allowing different normalization parameters (gamma and beta) for different spatial locations. This ensures that local spatial characteristics are preserved and enhanced, with each region normalized according to its specific properties rather than a global average.
Solution Approach 2:
The normalization block uses the input image data itself to generate the spatial normalization parameters through convolution operations. The system is self-contained, extracting spatial information directly from the input without requiring external spatial information sources.
Data Source
AI summary
An image region segmentation method and system suing self-spatial adaptive normalization is provided. The image region segmentation system includes: an encoder configured to encode an image for segmenting a region by using a plurality of encoding blocks; and a decoder configured to decode the image encoded by the encoder and to generate a region-segmented image by using a plurality of decoding blocks, wherein each of the encoding blocks processes an inputted image into a convolution layer, performs spatial adaptive normalization, and then reduces the image and delivers the image to the next encoding block. Accordingly, spatial characteristics of the image are considered in an encoding process and a decoding process, so that region segmentation can be exactly performed with respect to various images.