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Results 1 - 10
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22
Supervised learning of image restoration with convolutional networks
- in ICCV
, 2007
"... Convolutional networks have achieved a great deal of success in high-level vision problems such as object recognition. Here we show that they can also be used as a general method for low-level image processing. As an example of our approach, convolutional networks are trained using gradient learning ..."
Abstract
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Cited by 44 (11 self)
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Convolutional networks have achieved a great deal of success in high-level vision problems such as object recognition. Here we show that they can also be used as a general method for low-level image processing. As an example of our approach, convolutional networks are trained using gradient
Discriminative unsupervised feature learning with convolutional neural networks
- arXiv:1406.6909
"... Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate c ..."
Abstract
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Cited by 6 (1 self)
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Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate
Max-Pooling Convolutional Neural Networks for Vision-based Hand Gesture Recognition
- IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (ICSIPA2011)
, 2011
"... Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural networ ..."
Abstract
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Cited by 7 (3 self)
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network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates
Convolutional neural support vector machines: Hybrid visual . . .
"... We introduce Convolutional Neural Support Vector Machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). CNSVMs are trained using a Stochastic Gradient Descent approach, that provides the com ..."
Abstract
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Cited by 1 (0 self)
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We introduce Convolutional Neural Support Vector Machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). CNSVMs are trained using a Stochastic Gradient Descent approach, that provides
Descriptor matching with convolutional neural networks: a comparison to SIFT
"... Latest results indicate that features learned via convolutional neural networks out-perform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trai ..."
Abstract
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Cited by 11 (2 self)
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Latest results indicate that features learned via convolutional neural networks out-perform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were
Neural Network Based Face Recognition Using Matlab
"... In this paper, we propose to label a Self-Organizing Map (SOM) to measure image similarity. To manage this goal, we feed Facial images associated to the regions of interest into the neural network. At the end of the learning step, each neural unit is tuned to a particular Facial image prototype. Fac ..."
Abstract
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self-organizing map (SOM) based retrieval system. SOM has good feature extracting property due to its topological ordering. The Facial Analytics results for the 400 images of AT&T database reflects that the face recognition rate using one of the neural network algorithm SOM is 92.40 % for 40
Understanding Convolutional Neural Networks in Terms of Category-level Attributes
"... Abstract. It has been recently reported that convolutional neural net-works (CNNs) show good performances in many image recognition tasks. They significantly outperform the previous approaches that are not based on neural networks particularly for object category recognition. These performances are ..."
Abstract
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Abstract. It has been recently reported that convolutional neural net-works (CNNs) show good performances in many image recognition tasks. They significantly outperform the previous approaches that are not based on neural networks particularly for object category recognition. These performances
NEURAL NETWORKS FOR FACE RECOGNITION USING SOM
"... In today's networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. Face recognition is one of the few biometric methods, which is very complicated system since the human faces change depending on ..."
Abstract
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Cited by 1 (1 self)
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with different facial expressions with noisy input and optimize the recognition as possible. Among the architectures and algorithms suggested for artificial neural network, the SOM has special property of effectively creating spatially organized “internal representation ’ of various features of input signals
Disentangling factors of variation for facial expression recognition. ECCV
, 2012
"... Abstract. We propose a semi-supervised approach to solve the task of emotion recognition in 2D face images using recent ideas in deep learning for handling the factors of variation present in data. An emotion classifi-cation algorithm should be both robust to (1) remaining variations due to the pose ..."
Abstract
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Cited by 7 (0 self)
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Abstract. We propose a semi-supervised approach to solve the task of emotion recognition in 2D face images using recent ideas in deep learning for handling the factors of variation present in data. An emotion classifi-cation algorithm should be both robust to (1) remaining variations due
Analysis and Simulation on Recognition Algorithm for Dynamic Facial Images
"... Abstract—In order to realize the recognition for dynamic facial images, the paper builds a dynamic matching model. First, this paper introduces a dynamic feature extraction algorithm of feature constraint optimization which can effectively extract the 2D dynamic facial features. Then, the paper anal ..."
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analyzes the neurons mathematical model comprehensive expressing neurons operational mechanism and applies the model to dynamic facial feature expression. Finally, we study the learning rules how BP algorithm directs neural network, establish a corresponding mathematical model, and then use facial feature
Results 1 - 10
of
22
