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Deep stacking networks (DSNs) have been successfully applied in classification tasks. Its architecture builds upon blocks of simplified neural network modules (SNNM). The hidden units are assumed to be independent in the SNNM module. However, this assumption prevents SNNM from learning the local dependencies between hidden units to better capture the information in the input data for the classification...
This paper describes the use of convolutional neural network(CNN) method to classify various image and photo of Indonesia ancient temple. The method itself implements Deep Learning technique designed for Computer Vision task. The idea behind CNN is image pre-processing through a stack of convolution layers to create many patterns that can be easily recognized. The result shows that the learning model...
Tongue coating nature inspection is an essential part in the tongue diagnosis of Traditional Chinese Medicine (TCM). However, it has been depending on doctors' visual judgment. Although many researches have been done in this field, the issue remains challenging. The approaches are limited to image processing or shallow neural networks. In this paper, we propose to computerize tongue coating nature...
In recent years, deep learning has been used in image classification, object tracking, pose estimation, text detection and recognition, visual saliency detection, action recognition and scene labeling. Auto Encoder, sparse coding, Restricted Boltzmann Machine, Deep Belief Networks and Convolutional neural networks is commonly used models in deep learning. Among different type of models, Convolutional...
Environmental sound classification (ESC) is usually conducted based on handcrafted features such as the log-mel feature. Meanwhile, end-to-end classification systems perform feature extraction jointly with classification and have achieved success particularly in image classification. In the same manner, if environmental sounds could be directly learned from the raw waveforms, we would be able to extract...
In this paper, General Purpose Graphical Processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing...
This paper presents the impact of automatic feature extraction used in a deep learning architecture such as Convolutional Neural Network (CNN). Recently CNN has become a very popular tool for image classification which can automatically extract features, learn and classify them. It is a common belief that CNN can always perform better than other well-known classifiers. However, there is no systematic...
Artificial neural networks (ANN) with deep learning using convolutional neural networks have recently achieved good results in various challenging problems. The HMAX is yet another deep architecture that could offer similar performance but with less training cycles required. In this paper, we extended the performance of HMAX by aggregating several HMAX networks together to achieve state-of-the-art...
Sparse representations for signals and images have been used extensively in various image processing tasks. In this work, we use the curvelet transform as a sparsity inducing tool in neural networks. Nowadays, there is much interest in research and development of efficient algorithms that reduce the computational demands of training neural networks. We demonstrate that the compact directional representation...
We show that holistic image features, specifically GIST, can be used for semantic scene categorization. In our study, the problem of indooroutdoor scene classification is addressed. We first propose a simple yet efficient pipeline in which the GIST vector of an image is initially computed. For the classification task, a feedforward neural network is trained with a comprehensive training dataset. The...
This paper introduces a regularization method called Correlative Filter (CF) for Convolutional Neural Network (CNN), which takes advantage of the relevance between the convolutional kernels belonging to the same convolutional layer. During the process of training with the proposed CF method, several pairs of filters are designed in a manner of randomness to contain opposite weights in low-level layers...
The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix...
The feasibility of automating the evaluation of stroke chronic patients' motor functions has been explored while analyzing their corresponding fMRI studies with statistical parametric analysis, statistical inference analysis and a nonlinear multivoxel pattern-analysis classifier based on a feed-forward backward-propagation neural network. After doing principal component analysis and independent component...
In this paper, Probabilistic Neural Network with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance...
Magnetic resonance image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, MRI makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. MRI will complicate the judgment due to strong noise...
Urdu compound character recognition is a scarcely developed area and requires robust techniques to develop as Urdu being a family of Arabic script is cursive, right to left in nature and characters change their shapes and sizes when they are placed at initial, middle or at the end of a word. The developed system consists of two main modules segmentation and classification. In the segmentation phase...
In this paper, we used ICA based artificial neural network (ANN) and propose a robust technique for efficient 2D echocardiography image analyzing. This project can be divided in three parts. The first part is cardiac motion estimation from sequence of echocardiography. The second part is feature extraction from motion patterns using sources, these sources is derived from independent component analyzing...
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