Max-pooling convolutional neural networks for vision-based hand gesture recognition

Nagi, Jawad; Ducatelle, Frederick; Di, Gianni A.; Cireşan, Dan; Meier, Ueli; Giusti, Alessandro; Nagi, Farrukh; Schmidhuber, Jürgen; Gambardella, Luca Maria

doi:10.1109/icsipa.2011.6144164

Cited by 531 publications

(264 citation statements)

References 35 publications

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“…The translation invariant property leads to the question: why to create a full connected neural network? There is no need to have full connections because we always work with finite images .The farther the connection, the less importance to the computation [18].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Automated Edge Detection Using Convolutional Neural Network

El-Sayed¹,

Estaitia²,

Khafagy³

2013

IJACSA

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Abstract-The edge detection on the images is so important for image processing. It is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictness, but require excessive postprocessing. Proposed CNN technique is used to realize edge detection task it takes the advantage of momentum features extraction, it can process any input image of any size with no more training required, the results are very promising when compared to both classical methods and other ANN based methods.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Automated Edge Detection Using Convolutional Neural Network

El-Sayed¹,

Estaitia²,

Khafagy³

2013

IJACSA

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“…These include three well-known ConvNets for computer vision applications with different computational loads, namely the Convolutional Face Finder (CFF) [12], LeNet-5 [13] and MPCNN [14]. We also implemented two ConvNets from existing FPGA works [4][7] for scene labelling and sign recognition which we denote CNP and Sign Recognition respectively, and one ConvNet for scene labelling from an embedded GPU work [15].…”

Section: A Benchmarksmentioning

confidence: 99%

fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs

Venieris

Bouganis

2016

2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)

212

103

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Abstract-Convolutional Neural Networks (ConvNets) are a powerful Deep Learning model, providing state-of-the-art accuracy to many emerging classification problems. However, ConvNet classification is a computationally heavy task, suffering from rapid complexity scaling. This paper presents fpgaConvNet, a novel domain-specific modelling framework together with an automated design methodology for the mapping of ConvNets onto reconfigurable FPGA-based platforms. By interpreting ConvNet classification as a streaming application, the proposed framework employs the Synchronous Dataflow (SDF) model of computation as its basis and proposes a set of transformations on the SDF graph that explore the performance-resource design space, while taking into account platform-specific resource constraints. A comparison with existing ConvNet FPGA works shows that the proposed fully-automated methodology yields hardware designs that improve the performance density by up to 1.62× and reach up to 90.75% of the raw performance of architectures that are hand-tuned for particular ConvNets.

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“…The receptive field is the size of the kernel used. Each feature map represents distinct extracted features from the input, e.g., one may represent vertical edges and another may represent points [8].…”

Section: Kernels Convolution and Subsamplingmentioning

confidence: 99%

Iris nevus diagnosis: convolutional neural network and deep belief network

Oyedotun¹,

Khashman²

2017

Turk J Elec Eng & Comp Sci

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This work presents the diagnosis of iris nevus using a convolutional neural network (CNN) and deep belief network (DBN). Iris nevus is a pigmented growth (tumor) found in the front of the eye or around the pupil. It is seen that racial and environmental factors affect the iris color (e.g., blue, hazel, brown) of patients; hence, pigmented growths may be masked in the eye background or iris. In this work, some image processing techniques are applied to images to reinforce areas of interests in them, after which the considered classifiers are trained. We describe the automated diagnosis of iris nevus using neural network-based systems for the classification of eye images as "nevus affected" and "unaffected". Recognition rates of 93.35% and 93.67% were achieved for the CNN and DBN, respectively. Hence, the systems described in this work can be used satisfactorily for diagnosis or to reinforce the confidence in manual-visual diagnosis by medical experts.

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Max-pooling convolutional neural networks for vision-based hand gesture recognition

Cited by 531 publications

References 35 publications

Automated Edge Detection Using Convolutional Neural Network

Automated Edge Detection Using Convolutional Neural Network

fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs

Iris nevus diagnosis: convolutional neural network and deep belief network

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