Application of deep learning to computer vision: A comprehensive study

Islam, Shafiqul; Rahman, Sabita Rezwana; Rahman, Md. Mostafijur; Dey, Emon Kumar; Shoyaib, Mohammad

doi:10.1109/iciev.2016.7760071

Cited by 44 publications

(40 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The explored networks [62] are trained with stochastic gradient distributed machine learning system using 50 replicas on a NVidia Kepler GPU. Deep learning or Convolutional Neural Network (CNN) has become popular in the fields of machine learning and computer vision, because of it's high performance in object detection [33]. Using only GPP, a complex CNN may require more than one month to train [19].…”

Section: Graphic Processing Unit (Gpu)mentioning

confidence: 99%

“…Using only GPP, a complex CNN may require more than one month to train [19]. GPUs offer approximately ten fold speed-up compared to GPP, which is demonstrated in [33] for faster training and testing. A number of other computer vision and image processing algorithms [57] [34] [9] have been implemented on GPU mainly to accelerate them for real time needs.…”

Section: Graphic Processing Unit (Gpu)mentioning

confidence: 99%

See 1 more Smart Citation

Embedded Vision Systems: A Review of the Literature

Bhowmik

Appiah

2018

Applied Reconfigurable Computing. Architectures, Tools, and Applications

View full text Add to dashboard Cite

Over the past two decades, the use of low power Field Programmable Gate Arrays (FPGA) for the acceleration of various vision systems mainly on embedded devices have become widespread. The reconfigurable and parallel nature of the FPGA opens up new opportunities to speed-up computationally intensive vision and neural algorithms on embedded and portable devices. This paper presents a comprehensive review of embedded vision algorithms and applications over the past decade. The review will discuss vision based systems and approaches, and how they have been implemented on embedded devices. Topics covered include image acquisition, preprocessing, object detection and tracking, recognition as well as high-level classification. This is followed by an outline of the advantages and disadvantages of the various embedded implementations. Finally, an overview of the challenges in the field and future research trends are presented. This review is expected to serve as a tutorial and reference source for embedded computer vision systems.

show abstract

Section: Graphic Processing Unit (Gpu)mentioning

confidence: 99%

Section: Graphic Processing Unit (Gpu)mentioning

confidence: 99%

Embedded Vision Systems: A Review of the Literature

Bhowmik

Appiah

2018

Applied Reconfigurable Computing. Architectures, Tools, and Applications

View full text Add to dashboard Cite

show abstract

“…Recently, deep neural networks (including recurrent networks) have been successfully applied in several areas [1]: modeling and control of complex systems, fault detection, text understanding [2,3], speech recognition [4,5] and computer vision [6,7]. The advantage of deep learning is reflected on the modeling of high-level abstractions from the data by its architecture which is consisted of several non-linear learning layers.…”

Section: Introductionmentioning

confidence: 99%

Systems Modeling Using Deep Elman Neural Network

Salah

Fourati

2019

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

In this paper, the modeling of complex systems using deep Elman neural network architecture is improved. The emphasis is to retrieve better deep Elman structure that emulates perfectly such dynamic systems. To achieve this goal, sigmoid activation functions in the hidden and output layers nodes are chosen and data files on considered systems for modeling and validation steps are given. Simulation results prove the ability and the efficiency of a deep Elman neural network with two hidden layers in this task.

show abstract

“…A study on different applications of deep learning was conducted by S.M. Sofiqul Islam et al [15]. The review evaluates two models of CNN, AlexNet, and Visual Geometric Group (VGG-S) in nine different benchmark datasets.…”

Section: Introductionmentioning

confidence: 99%

Malayalam Handwritten Character Recognition Using AlexNet Based Architecture

James¹,

Manjusha²,

Chandran

2018

IJEEI

View full text Add to dashboard Cite

This research article proposes a new handwritten Malayalam character recognition model based on AlexNet based architecture. The Malayalam language consists of a variety of characters having similar features, thus, differentiating characters is a challenging task. A lot of handcrafted feature extraction methods have been used for the classification of Malayalam characters. Convolutional Neural Networks (CNN) is one of the popular methods used in image and language recognition. AlexNet based CNN is proposed for feature extraction of basic and compound Malayalam characters. Furthermore, Support Vector Machine (SVM) is used for classification of the Malayalam characters. The 44 primary and 36 compound Malayalam characters are recognised with better accuracy and achieved minimal time consumption using this model. A dataset consisting of about 180,000 characters is used for training and testing purposes. This proposed model produces an efficiency of 98% with the dataset. Further, a dataset for Malayalam characters is developed in this research work and shared on Internet.

show abstract

Application of deep learning to computer vision: A comprehensive study

Cited by 44 publications

References 21 publications

Embedded Vision Systems: A Review of the Literature

Embedded Vision Systems: A Review of the Literature

Systems Modeling Using Deep Elman Neural Network

Malayalam Handwritten Character Recognition Using AlexNet Based Architecture

Contact Info

Product

Resources

About