Search

Your search keyword '"Hidaka, Akinori"' showing total 33 results
Start Over Author "Hidaka, Akinori"
33 results on '"Hidaka, Akinori"'

5. Nonlinear Discriminant Analysis Based on Probability Estimation by Gaussian Mixture Model

Author

Hidaka, Akinori, Kurita, Takio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fränti, Pasi, editor, Brown, Gavin, editor, Loog, Marco, editor, Escolano, Francisco, editor, and Pelillo, Marcello, editor

Published
2014
Full Text
View/download PDF

6. Sparse Discriminant Analysis Based on the Bayesian Posterior Probability Obtained by L1 Regression

Author

Hidaka, Akinori, Kurita, Takio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gimel’farb, Georgy, editor, Hancock, Edwin, editor, Imiya, Atsushi, editor, Kuijper, Arjan, editor, Kudo, Mineichi, editor, Omachi, Shinichiro, editor, Windeatt, Terry, editor, and Yamada, Keiji, editor

Published
2012
Full Text
View/download PDF

7. Co-occurrence of Intensity and Gradient Features for Object Detection

Author

Hidaka, Akinori, Kurita, Takio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Leung, Chi Sing, editor, Lee, Minho, editor, and Chan, Jonathan H., editor

Published
2009
Full Text
View/download PDF

8. Selection of Histograms of Oriented Gradients Features for Pedestrian Detection

Author

Kobayashi, Takuya, Hidaka, Akinori, Kurita, Takio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ishikawa, Masumi, editor, Doya, Kenji, editor, Miyamoto, Hiroyuki, editor, and Yamakawa, Takeshi, editor

Published
2008
Full Text
View/download PDF

9. Analysis based on neural representation of natural object surfaces to elucidate the mechanisms of a trained AlexNet model

Author

Wagatsuma, Nobuhiko, Hidaka, Akinori, and Tamura, Hiroshi

Subjects
Cellular and Molecular Neuroscience, Neuroscience (miscellaneous)
Abstract

Analysis and understanding of trained deep neural networks (DNNs) can deepen our understanding of the visual mechanisms involved in primate visual perception. However, due to the limited availability of neural activity data recorded from various cortical areas, the correspondence between the characteristics of artificial and biological neural responses for visually recognizing objects remains unclear at the layer level of DNNs. In the current study, we investigated the relationships between the artificial representations in each layer of a trained AlexNet model (based on a DNN) for object classification and the neural representations in various levels of visual cortices such as the primary visual (V1), intermediate visual (V4), and inferior temporal cortices. Furthermore, we analyzed the profiles of the artificial representations at a single channel level for each layer of the AlexNet model. We found that the artificial representations in the lower-level layers of the trained AlexNet model were strongly correlated with the neural representation in V1, whereas the responses of model neurons in layers at the intermediate and higher-intermediate levels of the trained object classification model exhibited characteristics similar to those of neural activity in V4 neurons. These results suggest that the trained AlexNet model may gradually establish artificial representations for object classification through the hierarchy of its network, in a similar manner to the neural mechanisms by which afferent transmission beginning in the low-level features gradually establishes object recognition as signals progress through the hierarchy of the ventral visual pathway.

Published
2022
Full Text
View/download PDF

25. Automatic Analysis of Composite Physical Signals Using Non-Negative Factorization and Information Criterion

Author

Watanabe, Kenji, Hidaka, Akinori, Otsu, Nobuyuki, Kurita, Takio, Watanabe, Kenji, Hidaka, Akinori, Otsu, Nobuyuki, and Kurita, Takio

Abstract

type:text, In time-resolved spectroscopy, composite signal sequences representing energy transfer in fluorescence materials are measured, and the physical characteristics of the materials are analyzed. Each signal sequence is represented by a sum of non-negative signal components, which are expressed by model functions. For analyzing the physical characteristics of a measured signal sequence, the parameters of the model functions are estimated. Furthermore, in order to quantitatively analyze real measurement data and to reduce the risk of improper decisions, it is necessary to obtain the statistical characteristics from several sequences rather than just a single sequence. In the present paper, we propose an automatic method by which to analyze composite signals using non-negative factorization and an information criterion. The proposed method decomposes the composite signal sequences using non-negative factorization subjected to parametric base functions. The number of components (i.e., rank) is also estimated using Akaike's information criterion. Experiments using simulated and real data reveal that the proposed method automatically estimates the acceptable ranks and parameters.

Published
2012

32. Automatic Analysis of Composite Physical Signals Using Non-Negative Factorization and Information Criterion

Author

Watanabe, Kenji, Hidaka, Akinori, Otsu, Nobuyuki, Kurita, Takio, Watanabe, Kenji, Hidaka, Akinori, Otsu, Nobuyuki, and Kurita, Takio

Abstract

In time-resolved spectroscopy, composite signal sequences representing energy transfer in fluorescence materials are measured, and the physical characteristics of the materials are analyzed. Each signal sequence is represented by a sum of non-negative signal components, which are expressed by model functions. For analyzing the physical characteristics of a measured signal sequence, the parameters of the model functions are estimated. Furthermore, in order to quantitatively analyze real measurement data and to reduce the risk of improper decisions, it is necessary to obtain the statistical characteristics from several sequences rather than just a single sequence. In the present paper, we propose an automatic method by which to analyze composite signals using non-negative factorization and an information criterion. The proposed method decomposes the composite signal sequences using non-negative factorization subjected to parametric base functions. The number of components (i.e., rank) is also estimated using Akaike's information criterion. Experiments using simulated and real data reveal that the proposed method automatically estimates the acceptable ranks and parameters.

33. Correspondence between Monkey Visual Cortices and Layers of a Saliency Map Model Based on a Deep Convolutional Neural Network for Representations of Natural Images.

Author

Wagatsuma N, Hidaka A, and Tamura H

Subjects
Animals, Attention, Haplorhini, Neural Networks, Computer, Models, Neurological, Visual Cortex
Abstract

Attentional selection is a function that allocates the brain's computational resources to the most important part of a visual scene at a specific moment. Saliency map models have been proposed as computational models to predict attentional selection within a spatial location. Recent saliency map models based on deep convolutional neural networks (DCNNs) exhibit the highest performance for predicting the location of attentional selection and human gaze, which reflect overt attention. Trained DCNNs potentially provide insight into the perceptual mechanisms of biological visual systems. However, the relationship between artificial and neural representations used for determining attentional selection and gaze location remains unknown. To understand the mechanism underlying saliency map models based on DCNNs and the neural system of attentional selection, we investigated the correspondence between layers of a DCNN saliency map model and monkey visual areas for natural image representations. We compared the characteristics of the responses in each layer of the model with those of the neural representation in the primary visual (V1), intermediate visual (V4), and inferior temporal (IT) cortices. Regardless of the DCNN layer level, the characteristics of the responses were consistent with that of the neural representation in V1. We found marked peaks of correspondence between V1 and the early level and higher-intermediate-level layers of the model. These results provide insight into the mechanism of the trained DCNN saliency map model and suggest that the neural representations in V1 play an important role in computing the saliency that mediates attentional selection, which supports the V1 saliency hypothesis., (Copyright © 2021 Wagatsuma et al.)

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results