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Neuroscience study shows mammalian brain only use millisecond scale time window to process complicated real-life recognition scenarios. However, such speed cannot be achieved by traditional rate-based spiking neural network (SNN). Compared with spiking rate, the specific spiking timing (also called spiking pattern) may convey much more information. In this paper, by using modified rank order coding...
The selection of parameters is one of the most important tasks in the training of a neural network. The choice of activation and loss functions is particularly relevant as the formulation of training procedures strongly depends on the pairing of these functions. However, the very few works on the effect of different combinations of these functions do not present a comprehensive experimental study...
Many studies of material property estimation and material recognition have been conducted. Previous approaches evaluate the validity or usefulness of hand-designed image features. Thus, we propose a method to directly and naturally acquire image features for material perception using convolutional neural networks. Using a fine-tuned network, we achieved approximately the same recognition accuracy...
Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance...
Reservoir Computing Network (RCN) is a special type of the single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters...
The problems of estimating missing values in visual data appear ubiquitously in computer vision applications including image inpainting, video inpainting, hyperspectral data recovery, and magnetic resonance imaging (MRI) data recovery. Recently, it is shown that tensor completion, which generalizes matrix completion to multiway data of higher order, could accurately estimate the overall data structure...
Visual recognition and vision based retrieval of objects from large databases are tasks with a wide spectrum of potential applications. In this paper we propose a novel recognition method from video sequences suitable for retrieval from databases acquired in highly unconstrained conditions e.g. using a mobile consumer-level device such as a phone. On the lowest level, we represent each sequence as...
Due to the rapid increase of different digitized documents, the development of a system to automatically retrieve document images from a large collection of structured and unstructured document images is in high demand. Many techniques have been developed to provide an efficient and effective way for retrieving and organizing these document images in the literature. This paper provides an overview...
In the recognition of osteosarcoma magnetic resonance images (MRI), the probability of a pixel belonging to a class is not only related to its own features, but also closely correlated with the information distribution of the surrounding pixels. However, it is currently unable to recognize the osteosarcoma lesions and surrounding issues simultaneously. In order to solve the problem, we propose a fully...
In order to reduce data dimensions, autoencoders with neural networks have been proposed by Hinton et al. Autoencoders are composed of input, one hidden, and output layers, which tune weights and biases by a back propagation to minimize an error between inputs and outputs. The learned weights have input features, and can be applied to pretrainings of deep neural networks. However, these autoencoders...
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