An Estimation Method of Human Impression Factors for Objects from their 3D Shapes Using a Deep Neural Network

2018 ◽  
Vol 2018 (13) ◽  
pp. 194-1-194-6
Author(s):  
Koichi Taguchi ◽  
Manabu Hashimoto ◽  
Kensuke Tobitani ◽  
Noriko Nagata
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Estimation Method ◽  
3D Shapes

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Removing gloss using Deep Neural Network for 3D Reconstruction

2019 ◽  
Vol 2019 (1) ◽  
pp. 143-148
Author(s):  
Futa Matsushita ◽  
Ryo Takahasshi ◽  
Mari Tsunomura ◽  
Norimichi Tsumura
Keyword(s):  
Neural Network ◽  
3D Reconstruction ◽  
Conventional Method ◽  
Deep Neural Network ◽  
High Accuracy ◽  
Simple Shape ◽  
3D Shape ◽  
Illumination Direction ◽  
Rgb Images ◽  
3D Shapes

3D reconstruction is used for inspection of industrial products. The demand for measuring 3D shapes is increased. There are many methods for 3D reconstruction using RGB images. However, it is difficult to reconstruct 3D shape using RGB images with gloss. In this paper, we use the deep neural network to remove the gloss from the image group captured by the RGB camera, and reconstruct the 3D shape with high accuracy than conventional method. In order to do the evaluation experiment, we use CG of simple shape and create images which changed geometry such as illumination direction. We removed gloss on these images and corrected defect parts after gloss removal for accurately estimating 3D shape. Finally, we compared 3D estimation using proposed method and conventional method by photo metric stereo. As a result, we show that the proposed method can estimate 3D shape more accurately than the conventional method.

scholarly journals Personalized HRTF Modeling Based on Deep Neural Network Using Anthropometric Measurements and Images of the Ear

2018 ◽  
Vol 8 (11) ◽  
pp. 2180 ◽  
Author(s):  
Geon Lee ◽  
Hong Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Subjective Evaluation ◽  
Estimation Method ◽  
Objective Evaluation ◽  
Anthropometric Measurements ◽  
Sound Sources ◽  
Subjective Evaluations ◽  
Localization Test

This paper proposes a personalized head-related transfer function (HRTF) estimation method based on deep neural networks by using anthropometric measurements and ear images. The proposed method consists of three sub-networks for representing personalized features and estimating the HRTF. As input features for neural networks, the anthropometric measurements regarding the head and torso are used for a feedforward deep neural network (DNN), and the ear images are used for a convolutional neural network (CNN). After that, the outputs of these two sub-networks are merged into another DNN for estimation of the personalized HRTF. To evaluate the performance of the proposed method, objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated one are measured. Consequently, the proposed method provides the RMSE of −18.40 dB and LSD of 4.47 dB, which are lower by 0.02 dB and higher by 0.85 dB than the DNN-based method using anthropometric data without pinna measurements, respectively. Next, a sound localization test is performed for the subjective evaluation. As a result, it is shown that the proposed method can localize sound sources with higher accuracy of around 11% and 6% than the average HRTF method and DNN-based method, respectively. In addition, the reductions of the front/back confusion rate by 12.5% and 2.5% are achieved by the proposed method, compared to the average HRTF method and DNN-based method, respectively.

scholarly journals Reconstructing 3D Model from Single-View Sketch with Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Wang ◽  
Yu Yang ◽  
Baoquan Zhao ◽  
Dazhi Jiang ◽  
Siwei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Shape Reconstruction ◽  
Reconstruction Method ◽  
Single View ◽  
3D Shape ◽  
Proposed Model ◽  
Reconstruction Performance ◽  
3D Shape Reconstruction ◽  
3D Shapes

In this paper, we introduce a novel 3D shape reconstruction method from a single-view sketch image based on a deep neural network. The proposed pipeline is mainly composed of three modules. The first module is sketch component segmentation based on multimodal DNN fusion and is used to segment a given sketch into a series of basic units and build a transformation template by the knots between them. The second module is a nonlinear transformation network for multifarious sketch generation with the obtained transformation template. It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples. The third module is deep 3D shape reconstruction using multifarious sketches, which takes the obtained sketches as input to reconstruct 3D shapes with a generative model. It fuses and optimizes features of multiple views and thus is more likely to generate high-quality 3D shapes. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on a public 3D reconstruction dataset. The results demonstrate that our model can achieve better reconstruction performance than peer methods. Specifically, compared to the state-of-the-art method, the proposed model achieves a performance gain in terms of the five evaluation metrics by an average of 25.5% on the man-made model dataset and 23.4% on the character object dataset using synthetic sketches and by an average of 31.8% and 29.5% on the two datasets, respectively, using human drawing sketches.

scholarly journals An Adaptive Offloading Method for an IoT-Cloud Converged Virtual Machine System Using a Hybrid Deep Neural Network

2018 ◽  
Vol 10 (11) ◽  
pp. 3955 ◽  
Author(s):  
Yunsik Son ◽  
Junho Jeong ◽  
YangSun Lee
Keyword(s):  
Neural Network ◽  
Virtual Machine ◽  
Deep Neural Network ◽  
Estimation Method ◽  
Prediction Method ◽  
Context Information ◽  
Local Environments ◽  
Low Performance ◽  
Iot Devices ◽  
Synchronization Structure

A virtual machine with a conventional offloading scheme transmits and receives all context information to maintain program consistency during communication between local environments and the cloud server environment. Most overhead costs incurred during offloading are proportional to the size of the context information transmitted over the network. Therefore, the existing context information synchronization structure transmits context information that is not required for job execution when offloading, which increases the overhead costs of transmitting context information in low-performance Internet-of-Things (IoT) devices. In addition, the optimal offloading point should be determined by checking the server’s CPU usage and network quality. In this study, we propose a context management method and estimation method for CPU load using a hybrid deep neural network on a cloud-based offloading service that extracts contexts that require synchronization through static profiling and estimation. The proposed adaptive offloading method reduces network communication overheads and determines the optimal offloading time for low-computing-powered IoT devices and variable server performance. Using experiments, we verify that the proposed learning-based prediction method effectively estimates the CPU load model for IoT devices and can adaptively apply offloading according to the load of the server.

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