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Keyword: CNN
Keyword Driven Image Description Generation System
Image description generation is an important area in Computer Vision and Natural Language Processing. This paper introduces a novel architecture for an image description generation system using keywords. The proposed architecture uses a high-level feature such as keywords for generating captions. The important component of caption generation is the deep Bidirectional LSTM network. The space…
Read More Deep Learning Model for A Driver Assistance System to Increase Visibility on A Foggy Road
For many years, a lot of researches have been made to develop Advanced Driver Assistance Systems (ADAS) that are based on integrated systems. The main objective is to help drivers. Hence, keeping them safe under different driving conditions. Visibility for drivers remains the biggest problem faced on the road in an atmosphere of fog. In…
Read More A Survey on 3D Hand Skeleton and Pose Estimation by Convolutional Neural Network
Restoring, estimating the fully 3D hand skeleton and pose from the image data of the captured sensors/cameras applied in many applications of computer vision and robotics: human-computer interaction; gesture recognition, interactive games, Computer-Aided Design (CAD), sign languages, action recognition, etc. These are applications that flourish in Virtual Reality and Augmented Reality (VR/AR) technologies. Previous survey…
Read More Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document
As many digital documents are required in various environments, paper documents are digitized by scanner, fax, digital camera and specific software. In the case of a scanned document, we need to check whether the document is right sided or upside down because the orientation of the scanned document is determined by the orientation in which…
Read More Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks
Blurred small objects produced by cropping, warping, or intrinsically so, are challenging to detect and classify. Therefore, much recent research is focused on feature extraction built on Faster R-CNN and follow-up systems. In particular, RPN, SPP, FPN, SSD, and DSSD are the layered feature extraction methods for multiple object detections and small objects. However, super-resolution…
Read More Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion
With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security.…
Read More Sentiment Analysis of Transjakarta Based on Twitter using Convolutional Neural Network
TransJakarta is one of the methods to reduce congestion in Jakarta. However, the number of TransJakarta users compared to number of private vehicle users is very small, only 24% of the total population in Jakarta. The purpose of this research is to know public opinions about TransJakarta whether positive or negative by doing sentiment analysis…
Read More Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks
The major problem in electric utility is Electrical Theft, which is harmful to electric power suppliers and causes economic loss. Detecting and controlling electrical theft is a challenging task that involves several aspects like economic, social, regional, managerial, political, infrastructural, literacy rate, etc. Numerous methods were proposed formerly for detecting electricity theft. However, the previous…
Read More Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model
Low contrast images degrade the performance of image processing system. To solve the issue, plenty of image enhancement methods have been proposed. But the methods work properly on the fixed environment or specific images. The methods dependent on fixed image conditions cannot perform image enhancement properly and perspective of smart device users, algorithms including iterative…
Read More Software and Hardware Enhancement of Convolutional Neural Networks on GPGPUs
Convolutional Neural Networks (CNNs) have gained attention in recent years for their ability to perform complex machine learning tasks with high accuracy and resilient to noise of inputs. The time-consuming convolution operations of CNNs pose great challenges to both software as well as hardware designers. To achieve superior performance, a design involves careful concerns between…
Read More Retrieving Dialogue History in Deep Neural Networks for Spoken Language Understanding
In this paper, we propose a revised version of the semantic decoder for multi-label classification task in the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5). Our model concatenates two deep neural networks – a Convolutional Neural Network (CNN) and a Recurrent Neural Networks (RNN) – for detecting semantic…
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