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The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional...
This paper provides a voice transformation model that uses pitch data and Feed-forward Neural Networks on Line Spectral Frequency. The aim of this work is to achieve the transformation of a speech signal produced by a source speaker by modifying voice individuality parameters such that it appears to be spoken by a chosen target speaker, without modifying the message contents. Most of the previous...
This paper proposes a low-cost video-based Real-Time Pupil-Tracking embedded system which will allow people with reduced mobility to control a wheelchair through their eyes. The main aspect of the method is its capacity to be implemented in a portable computing system, reduced both in computing power and in RAM memory. The Pupil-Tracking system is based on Feedforward Neural Networks-using offline...
Pedestrian detection is one of the key technologies in automotive safety, robotic and intelligent video surveillance. Recently, deep convolutional neural networks have achieved significant effect in image classification and retrieval tasks. In this paper, we propose a novel deep convolutional neural networks model for pedestrian detection to simultaneously extract and classify pedestrian features...
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures. An interactive training scheme reduces the extremely tedious manual annotation task that is typically required for deep networks to perform well on image segmentation problems. Our proposed method employs a feedback loop that captures sparse annotations using a graphical...
This paper presents the development process of the SUST-Bangla Handwritten Numeral Database (SUST-BHND). We extracted handwritten Bengali digits from twenty-one hundred pre-designed form filled by different people. After data retrieval, cleaning, processing and error analysis we have created a database consisting of 101065 sample images. It provides a basic database for Bangla OCR and script identification...
In order to realize the real-time collection of network information, to achieve energy saving and fault monitoring, it puts forward a method of monitoring the power consumption information based on the network data analysis. The method can use SNMP and MIB to collect the power information of the power equipment in different networks, including the CPU occupancy rate, physical and virtual memory usage...
This paper presents a new methodology for detecting deterioration in performance of deep neural networks when applied to on line visual analysis problems and enabling fine-tuning, or retraining, of the network to the current data characteristics. Pre-trained deep neural networks which have a satisfactory performance on the problem under study constitute the basis of the approach, with efficient transfer...
In this paper we propose a publicly available static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS database and existing databases is given. Baseline results for both of the protocols are presented.
We compare the performance of multilayer perceptrons (MLPs) obtained using back propagation (BP), decision boundary making (DBM) algorithm and extreme learning machine (ELM), and investigate better method for developing aware agents (A-agent) that are suitable for implementation in portable/wearable computing devices (P/WCD). The DBM has been proposed by us for inducing compact and high performance...
The finite impulse response multilayer perceptron (FIRMLP), a class of temporal processing neural networks, is a multilayer perceptron where the static weights (synapses) have been replaced by finite impulse response filters. Thus FIRMLPs are a type of convolutional neural network and different synapse types can be considered. We compare the performance of different network configurations for the...
In this paper, we investigate the use of neural networks (NN) to detect weed plants in rice fields based on aerial images. For this purpose, images are taken at 50 meters high with 16.1 megapixels CMOS digital camera mount-ted on an autonomous electrical fixed wind plane. Then, an ortho-mosaic map of the field is created by stitching 250 pictures, as the image is ortho-corrected, the pixel information...
Aiming at the shorting of the existing atrial fibrillation (AF) detection algorithms and improve the ability of intelligent recognition and extraction of AF signals. Recently, deep learning theory with massive data has been used on image, voice and other filed widely. In this paper, a method based on the stack sparse autoencoder neural network, a instance of deep learning strategy, was proposed for...
In this paper, we describe how neural networks can be used for high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects...
The field of artificial neural networks has a long history of several decades, where the theoretical contributions have progressed with advances in terms of power and memory in present day computers. Some old methods are now rebranded or represented, taking advantage of the power of present day computers. More particularly, we consider the current trend of Random Vector Functional Link Networks, which...
Retinal vessel segmentation has been widely used for screening, diagnosis and treatment of cardiovascular and ophthalmologic diseases. In this paper, we propose an automated approach for vessel segmentation in digital retinal images based on de-noising auto-encoders layer-wise initialized neural networks. The proposed method utilized a deep neural network, which is layer-wise initialized by de-noising...
Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem in computer vision. Despite efforts made in developing various methods for FER, existing approaches lack generalizability when applied to unseen images or those that are captured in wild setting (i.e. the results are not significant). Most of the existing approaches are based on engineered features (e...
Here, we propose a method for recognition of handwritten English digit utilizing discrete cosine space-frequency transform known as the Discrete Cosine S-Transform (DCST). Experiments have been conducted on the publicly availabe standard MNIST handwritten digit database. The DCST features along with an Artificial Neural Network (ANN) classifier is utilized for solving the classification issues of...
With the continuous development of network and database technology, share and conversion on heterogeneous data are still a complex problem currently facing. Current methods are mostly based on the similarity between the field to complete the matching process, although this method can finish partial matches on information, its time complexity is very high due to the large processing data sets and the...
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn. In this work, not only do we show that CNNs can achieve strong performance, but we also introduce an approach to...
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