Which of the following Neural Networks is used in Machine Vision system?

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Question restated: Which of the following Neural Networks is used in Machine Vision system?
Option A: 'A) Convolutional Neural Networks (CNN)'. This option points to CNNs, which are specifically designed to process grid-like data such as images. The convolutional layers help detect local patterns (edges, textures) and build hierarchical representations useful for vision tasks.
Option B: 'B) Feed Forward Neural Networks (FNN)'. While FNNs can process inputs, they lack the spatial structure awareness that convolutional operations provide, making them less effective for raw image data without substantial feature engineering.
Option C: 'C) Generative Adversarial Networks (GAN)'. GANs are a framework involving a generator and a discriminator, often used for data generation and realism assessments. They are powerful in vision-related contexts but are not the standard core architecture for general machine vision pipelines.
Option D: 'D) Recurrent Neural Networks (RNN)'. RNNs excel at sequential or time-series data, such as language or video frames where temporal dependencies are key. However, for static image analysis and feature extraction, RNNs are not the primary choice compared to convolutional approaches.
Further thoughts: In practical machine vision systems, the ability to capture spatial locality and translate invariances across an image is central, which aligns with the strengths of CNNs. GANs can augment vision tasks but are not the foundational architecture for typical object recognition, segmentation, or detection pipelines. FNNs, while universal function approximators, generally require flattening image data and lose spatial structure, leading to poorer performance compared to CNNs. RNNs, while valuable for sequence processing, do not inherently model 2D spatial structure as effectively as CNNs.
In summary, the reasoning across options emphasizes how convolutional structure aligns with the core needs of machine vision, while the other architectures serve different roles or lack key properties for image-centric tasks.

Which of the following Neural Networks is used in Machine Vision system?

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