Shared Map Convolutional Neural Networks for Real-Time Mobile Image Recognition

Raveane, William; Arrieta, María Angélica González

doi:10.1007/978-3-319-07593-8_56

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking into account that a single CNN execution, due to its complex nature, can require several million floating point operations, it can be seen that a dense window stride value can increase exponentially the computing toll on the system. This process can be greatly optimized by executing the network as Shared Maps, a detailed explanation of which is given in [30]. This allows executing the network for the entire image frame in parallel, thus requiring a single execution.…”

Section: Convolutional Neural Network and Shared Mapsmentioning

confidence: 99%

Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos

2019

Self Cite

View full text Add to dashboard Cite

The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear's projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method matches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions.

show abstract

Section: Convolutional Neural Network and Shared Mapsmentioning

confidence: 99%

Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, the deep neural network can extract more representative features from the raw data in a pre-training way for obtaining more accurate prediction results. Due to the superiority in feature extraction and model fitting, deep learning has attracted a great amount of attention around the world, and has been widely applied in various fields, such as green buildings [27,28], image processing [29][30][31][32], speech recognition [33,34], and intelligent traffic management systems [35][36][37]. As a novel deep learning method, the long-short-term memory network (LSTM) can make full use of the historical information due to its special structure [38].…”

Section: Introductionmentioning

confidence: 99%

Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

Xie

et al. 2018

Information

View full text Add to dashboard Cite

Short-term electrical load forecasting is of great significance to the safe operation, efficient management, and reasonable scheduling of the power grid. However, the electrical load can be affected by different kinds of external disturbances, thus, there exist high levels of uncertainties in the electrical load time series data. As a result, it is a challenging task to obtain accurate forecasting of the short-term electrical load. In order to further improve the forecasting accuracy, this study combines the data-driven long-short-term memory network (LSTM) and extreme learning machine (ELM) to present a hybrid model-based forecasting method for the prediction of short-term electrical loads. In this hybrid model, the LSTM is adopted to extract the deep features of the electrical load while the ELM is used to model the shallow patterns. In order to generate the final forecasting result, the predicted results of the LSTM and ELM are ensembled by the linear regression method. Finally, the proposed method is applied to two real-world electrical load forecasting problems, and detailed experiments are conducted. In order to verify the superiority and advantages of the proposed hybrid model, it is compared with the LSTM model, the ELM model, and the support vector regression (SVR). Experimental and comparison results demonstrate that the proposed hybrid model can give satisfactory performance and can achieve much better performance than the comparative methods in this short-term electrical load forecasting application.

show abstract

Shared Map Convolutional Neural Networks for Real-Time Mobile Image Recognition

Cited by 2 publications

References 7 publications

Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos

Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos

Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

Contact Info

Product

Resources

About