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The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network (CNN) using a minimal model (Minimal CNN). The proposed minimal CNN is presented using a layering approach. This approach provides a concise and accessible understanding of the main mathematical operations of a CNN. Hence, it benefits...
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend...
Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion,...
In this paper, we study an unconventional but practically meaningful reversibility problem of commonly used image filters. We broadly define filters as operations to smooth images or to produce layers via global or local algorithms. And we raise the intriguingly problem if they are reservable to the status before filtering. To answer it, we present a novel strategy to understand general filter via...
This paper presents a winning solution to the AAIA'17 Data Mining Challenge. The challenge focused on creating an efficient prediction model for digital card game Hearthstone. Our final solution is an ensemble of various neural network models, including convolutional neural networks.
In recent years, 3-dimension convolutional neural networks (3D CNNs) have been widely used for video analysis, 3-dimension geometric data and medical image diagnosis. While conventional CNNs are computationally intensive, 3D CNNs push the computational requirements into another level, since each computation depends on multiple image frames. This paper describes a novel hardware architecture for a...
Convolutional Neural Networks (CNNs) can achieve high classification accuracy while they require complex computation. Binarized Neural Networks (BNNs) with binarized weights and activations can simplify computation but suffer from obvious accuracy loss. In this paper, low bit-width CNNs, BNNs and standard CNNs are compared to show that low bit-width CNNs is better suited for embedded systems. An architecture...
This paper presents our study on image reconstruction algorithms for THz near field scanning systems. Based on the principle of Physical Optics (PO) algorithm, we have proposed and investigated a novel transposed convolution image reconstruction algorithm (TC), in comparison with the back propagation algorithm (BP) in simulation and experiment.
Ultrasound medical diagnostics is a real-time modality based on a doctor's interpretation of images. So far, automated Computer-Aided Diagnostic tools were not widely applied to ultrasound imaging. The emerging methods in Artificial Intelligence, namely deep learning, gave rise to new applications in medical imaging modalities. The work's objective was to show the feasibility of implementing deep...
CNN involves large number of convolution of feature maps and kernels, necessary for extracting useful features for accurate classification. However, it requires significant amount of computationally intensive power and area hungry multiplications limiting its deployment on embedded devices under resource constrained scenario. To address this problem, we propose modified distributed arithmetic based...
Convolutional neural networks (CNNs), in which several convolutional layers extract feature patterns from an input image, are one of the most popular network architectures used for image classification. The convolutional computation, however, requires a high computational cost, resulting in an increased power consumption and processing time. In this paper, we propose a novel algorithm that substitutes...
In this paper, we propose an analog circuit for binary neural firing model that can extract various image features. Both computational and hardware models were designed for feature extraction algorithm that explores the dependency of firing rates on the pixel intensity in alignment with inhibition and excitation principles. The circuit for translating each pixel intensity into a series of pulses is...
In this paper we argue that the Wigner-Ville distribution (WVD), instead of the spectrogram, should be used as basic input into convolutional neural network (CNN) based classification schemes. The WVD has superior resolution and localization as compared to other time-frequency representations. We present a method where a large-size kernel may be learned from the data, to enhance features important...
In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. The proposed method is applied to a practical medical diagnosis problem of classifying different stages...
We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Specifically, our architecture combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages...
This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition...
Today, Convolutional Neural Network (CNN) is adopted in a lot of areas such as computer vision and natural language processing. By employing hardware accelerators such as graphic processing unit (GPU), a significant amount of speedup can be achieved in CNN and many studies have proposed such acceleration methods. However, it is not straightforward to parallelize the CNN on a hardware accelerator because...
Visual inspection process for weld defects still manually operated by human vision, so the result of the test still highly subjective. In this research, the visual inspection process will be done through image processing on the image sequence to make data accuracy more better. CNN as one of the image processing technique can determine the feature automatically which is suitable for this problem in...
A bi-dimensional filter for high accuracy image processing is implemented by using a novel partitioning method. The method is based on a number theory theorem, which permits to reduce the complexity of the operation to that of an adder chain and also the amount of the coefficients stored in memory, improving the memory organization. To show the advantage of such method, we implemented a Floating Point...
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