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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
Multi-block LBP: the image is divided into many blocks, a LBP histogram is calculated for every block and concatenated as the final histogram. Volume Local Binary Pattern(VLBP): [ 11 ] VLBP looks at dynamic texture as a set of volumes in the (X,Y,T) space where X and Y denote the spatial coordinates and T denotes the frame index.
The network is trained by minimizing the euclidean distance between the image and the output of a CNN that reconstructs the input from the output of the terminal capsules. [1] The network is discriminatively trained, using iterative routing-by-agreement. [1] The activity vectors of all but the correct parent are masked. [1]
As the image illustrated below, if only a small portion of the image is shown, it is very difficult to tell what the image is about. Mouth. Even try another portion of the image, it is still difficult to classify the image. Left eye. However, if we increase the contextual of the image, then it makes more sense to recognize. Increased field of ...
Image acquisition – A digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence.
In the original Chua-Yang CNN (CY-CNN) processor, the state of the cell was a weighted sum of the inputs and the output was a piecewise linear function.However, like the original perceptron-based neural networks, the functions it could perform were limited: specifically, it was incapable of modeling non-linear functions, such as XOR.
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5] The U-Net architecture has also been employed in diffusion models for iterative image denoising. [6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.