454,633 research outputs found
Overview: Computer vision and machine learning for microstructural characterization and analysis
The characterization and analysis of microstructure is the foundation of
microstructural science, connecting the materials structure to its composition,
process history, and properties. Microstructural quantification traditionally
involves a human deciding a priori what to measure and then devising a
purpose-built method for doing so. However, recent advances in data science,
including computer vision (CV) and machine learning (ML) offer new approaches
to extracting information from microstructural images. This overview surveys CV
approaches to numerically encode the visual information contained in a
microstructural image, which then provides input to supervised or unsupervised
ML algorithms that find associations and trends in the high-dimensional image
representation. CV/ML systems for microstructural characterization and analysis
span the taxonomy of image analysis tasks, including image classification,
semantic segmentation, object detection, and instance segmentation. These tools
enable new approaches to microstructural analysis, including the development of
new, rich visual metrics and the discovery of
processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Neural-Image-QA, an end-to-end
formulation to this problem for which all parts are trained jointly. In
contrast to previous efforts, we are facing a multi-modal problem where the
language output (answer) is conditioned on visual and natural language input
(image and question). Our approach Neural-Image-QA doubles the performance of
the previous best approach on this problem. We provide additional insights into
the problem by analyzing how much information is contained only in the language
part for which we provide a new human baseline. To study human consensus, which
is related to the ambiguities inherent in this challenging task, we propose two
novel metrics and collect additional answers which extends the original DAQUAR
dataset to DAQUAR-Consensus.Comment: ICCV'15 (Oral
Focusing on optic tectum circuitry through the lens of genetics
The visual pathway is tasked with processing incoming signals from the retina and converting this information into adaptive behavior. Recent studies of the larval zebrafish tectum have begun to clarify how the 'micro-circuitry' of this highly organized midbrain structure filters visual input, which arrives in the superficial layers and directs motor output through efferent projections from its deep layers. The new emphasis has been on the specific function of neuronal cell types, which can now be reproducibly labeled, imaged and manipulated using genetic and optical techniques. Here, we discuss recent advances and emerging experimental approaches for studying tectal circuits as models for visual processing and sensorimotor transformation by the vertebrate brain
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio
Convolutional Kernel Networks
An important goal in visual recognition is to devise image representations
that are invariant to particular transformations. In this paper, we address
this goal with a new type of convolutional neural network (CNN) whose
invariance is encoded by a reproducing kernel. Unlike traditional approaches
where neural networks are learned either to represent data or for solving a
classification task, our network learns to approximate the kernel feature map
on training data. Such an approach enjoys several benefits over classical ones.
First, by teaching CNNs to be invariant, we obtain simple network architectures
that achieve a similar accuracy to more complex ones, while being easy to train
and robust to overfitting. Second, we bridge a gap between the neural network
literature and kernels, which are natural tools to model invariance. We
evaluate our methodology on visual recognition tasks where CNNs have proven to
perform well, e.g., digit recognition with the MNIST dataset, and the more
challenging CIFAR-10 and STL-10 datasets, where our accuracy is competitive
with the state of the art.Comment: appears in Advances in Neural Information Processing Systems (NIPS),
Dec 2014, Montreal, Canada, http://nips.c
- …