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What is the main purpose of capturing the features of diagonal lines and Crosses in the context described?
What is the main purpose of capturing the features of diagonal lines and Crosses in the context described?
- Enhancing the brightness of the image
- Recognizing the spatial structure between pixels (correct)
- Identifying the background color
- Highlighting the image borders
Which components capture all the features related to diagonal lines and Crosses as mentioned in the text?
Which components capture all the features related to diagonal lines and Crosses as mentioned in the text?
- Arms, legs, and body (correct)
- Arms, legs, and head
- Hands, feet, and torso
- Eyes, nose, and mouth
What do the smaller matrices represent in this context?
What do the smaller matrices represent in this context?
- Padding added to the image
- Filters of weights (correct)
- Biases of the model
- Training data labels
What operation is performed when a patch is slid over an image in the described convolution process?
What operation is performed when a patch is slid over an image in the described convolution process?
What is the primary function of convolution in preserving spatial structure between pixels?
What is the primary function of convolution in preserving spatial structure between pixels?
What follows element wise multiplication when a patch overlaps with an image during convolution?
What follows element wise multiplication when a patch overlaps with an image during convolution?
What is the purpose of comparing images piece by piece or patch by patch in image classification?
What is the purpose of comparing images piece by piece or patch by patch in image classification?
Why is it important for a model to find rough feature matches across images in image classification?
Why is it important for a model to find rough feature matches across images in image classification?
What are features in image classification described as mini versions of?
What are features in image classification described as mini versions of?
How are black and white represented in the context of image classification from the text?
How are black and white represented in the context of image classification from the text?
What allows models in image classification to be invariant to deformations like scale, shift, and rotation?
What allows models in image classification to be invariant to deformations like scale, shift, and rotation?
What helps pick up on common features in images according to the text?
What helps pick up on common features in images according to the text?
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