For example, DeepPose [3] is the first CNNs proposed to solve human pose estimation problem with cascade
convolution network regressed by Toshev et al.
Finally, the last layer performs a transposed
convolution that increases the dimensions to (H, W, CO), whose height and width are the same as the input image, with the depth in each pixel being the likelihood that the pixel belongs to each of the CO classes.
The VSoC concept of column-parallel, charge-based signal processing and flexible ASIP-based control enables 1D analogue
convolution operations and software-defined digitisation.
Moreover, a useful mathematical
convolution was discovered for a study of international behaviors regarding beverage consumption (Lanier, 2013).
The deep CNN is a feedforward NN that comprises a stack of alternating
convolution layers and pooling layers and then fully connected layers and softmax layers [13] (Figure 2).
Although amounts of pooling operations enlarge the receptive fields of the
convolution kernel of FCN, they lose the detailed location information, resulting in coarse segmentation result, which hinders its further application.
[8], and a method of discrete
convolution and FFT (DCFFT) was advanced, which completely avoids any additional error except for the discretization error.
In the current work, the SEM image of cemented backfill captured at the microscopic scale is regarded as the input sample in the deep
convolution neural network.
In order to solve these problems, we propose a multiscale deep
convolution detection network to detect small objects.
The Hadamard product or
convolution of two power series [h.sub.1](z) = [[summation].sup.[infinity].sub.n=1] [a.sub.n][z.sup.n] and [h.sub.2](z) = [[summation].sup.[infinity].sub.n=1] [c.sub.n][z.sup.n] is given by [h.sub.1](z) * [h.sub.2](z) = ([h.sub.1] * [h.sub.2])(z) = [[summation].sup.[infinity].sub.n=1] [a.sub.n][c.sub.n][z.sup.n].
As the
convolution layer is the most complicated part of CNN, we first optimize the single
convolution layer and then, on this basis, we accelerate complete CNN and explore different optimization strategies.
