4,904 research outputs found
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR
2015 workshop
Traditional meditation, mindfulness and psychodynamic approach: An integrative perspective
The purpose of this article is to consolidate the inter-theoretical bridge between psychodynamic approach and TM, beyond the apparent incompatibility. Our impression is that even if some authors have already worked in order to fill the gap between TM and psychodynamic psychotherapy at theoretical level, this integration could be underrated and these efforts remain isolated. This could be due mainly to ambiguities in the translation of those terms with respect to the fundaments of core concepts of both perspectives, and a lack of empirical research on psychodynamic and meditation. Psychodynamic approach could embrace those aspects of TM that have been less developed in MBIs\u2019 theory and practice. Moreover, an integration of modern mindfulness practices into a psychodynamic framework should be explored. Further empirical studies and theoretical considerations are needed to corroborate testable hypotheses and comparing classical and combine
A fluctuating environment as a source of periodic modulation
We study the intermittent fluorescence of a single molecule, jumping from the
"light on" to the "light off" state, as a Poisson process modulated by a
fluctuating environment. We show that the quasi-periodic and
quasi-deterministic environmental fluctuations make the distribution of the
times of sojourn in the "light off" state depart from the exponential form, and
that their succession in time mirrors environmental dynamics. As an
illustration, we discuss some recent experimental results, where the
environmental fluctuations depend on enzymatic activity.Comment: 13 pages, 4 figures. Accepted for publication on Chem. Phys. Let
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural
networks (DNNs) proposed in the state of the art for image recognition. For
each DNN multiple performance indices are observed, such as recognition
accuracy, model complexity, computational complexity, memory usage, and
inference time. The behavior of such performance indices and some combinations
of them are analyzed and discussed. To measure the indices we experiment the
use of DNNs on two different computer architectures, a workstation equipped
with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson
TX1 board. This experimentation allows a direct comparison between DNNs running
on machines with very different computational capacity. This study is useful
for researchers to have a complete view of what solutions have been explored so
far and in which research directions are worth exploring in the future; and for
practitioners to select the DNN architecture(s) that better fit the resource
constraints of practical deployments and applications. To complete this work,
all the DNNs, as well as the software used for the analysis, are available
online.Comment: Will appear in IEEE Acces
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