Cycle GAN Image Translation Using Time Series Channels
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Solution Overview
Problem
Existing cycle GAN methods struggle to accurately translate images of two objects with different movement tendencies, particularly in scenarios where a single image is insufficient to differentiate between the objects, leading to difficulties in generating models that effectively remove one object from images containing both.
Innovation Solution
The use of cycle GAN with image data defined by multiple channels of time series images, where one object moves significantly and the other minimally, allows for the generation of models that can accurately translate images to exclude the second object, enhancing the discrimination and removal process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cycle GAN is applied to translate images containing two objects with different movement tendencies, then the model can learn to separate the objects, but a single image is insufficient to differentiate between the objects leading to poor translation accuracy
Solution Approach 1:
The patent transitions from using single images to using time series image data across multiple channels. By adding the temporal dimension and utilizing multiple channels (e.g., different wavelengths or sensor types), the system gains additional information to differentiate between objects with different movement tendencies, thereby improving translation accuracy in the cycle GAN model.
2Measurement precision
If multiple channels of time series images are used to improve object differentiation, then translation accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent utilizes the dynamic movement patterns of objects across time series images. By analyzing how objects move differently over time in multiple channels, the system can automatically differentiate and separate objects based on their movement characteristics, reducing the need for complex manual processing while improving separation accuracy.
Data Source
AI summary
There is provided an information processing apparatus including a generator building section configured to build a generator for generating image data of a first domain from image data of a second domain and for generating the image data of the second domain from the image data of the first domain by use of cycle GAN (generative adversarial networks). The first domain is defined by image data of a plurality of channels including at least two time series images each including a first object and a second object, each of the objects having a tendency to move differently. The second domain is defined by the image data of a plurality of the channels including at least the two time series images each including the first object and excluding the second object.