101 research outputs found
Reliability measure for shape-from-focus
This is the author’s version of a work that was accepted for publication in Journal Image and Vision Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Image and Vision Computing , 31, 10 (2013) DOI: 10.1016/j.imavis.2013.07.005Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is
attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically
found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when
the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth
estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF
without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the
depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyses the shape of the focus measure function
and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The
proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard
them. Experiments with both synthetic and real scenes are presented
Measuring (in)variances in Convolutional Networks
Convolutional neural networks (CNN) offer state-of-the-art performance in various computer vision tasks such as activity recognition, face detection, medical image analysis, among others. Many of those tasks need invariance to image transformations (i.e.. rotations, translations or scaling).
This work proposes a versatile, straightforward and interpretable measure to quantify the (in)variance of CNN activations with respect to transformations of the input. Intermediate output values of feature maps and fully connected layers are also analyzed with respect to different input transformations. The technique is applicable to any type of neural network and/or transformation. Our technique is validated on rotation transformations and compared with the relative (in)variance of several networks. More specifically, ResNet, AllConvolutional and VGG architectures were trained on CIFAR10 and MNIST databases with and without rotational data augmentation. Experiments reveal that rotation (in)variance of CNN outputs is class conditional. A distribution analysis also shows that lower layers are the most invariant, which seems to go against previous guidelines that recommend placing invariances near the network output and equivariances near the input.Instituto de Investigación en Informátic
Measuring (in)variances in Convolutional Networks
Convolutional neural networks (CNN) offer state-of-the-art performance in various computer vision tasks such as activity recognition, face detection, medical image analysis, among others. Many of those tasks need invariance to image transformations (i.e.. rotations, translations or scaling).
This work proposes a versatile, straightforward and interpretable measure to quantify the (in)variance of CNN activations with respect to transformations of the input. Intermediate output values of feature maps and fully connected layers are also analyzed with respect to different input transformations. The technique is applicable to any type of neural network and/or transformation. Our technique is validated on rotation transformations and compared with the relative (in)variance of several networks. More specifically, ResNet, AllConvolutional and VGG architectures were trained on CIFAR10 and MNIST databases with and without rotational data augmentation. Experiments reveal that rotation (in)variance of CNN outputs is class conditional. A distribution analysis also shows that lower layers are the most invariant, which seems to go against previous guidelines that recommend placing invariances near the network output and equivariances near the input.Instituto de Investigación en Informátic
Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics
In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method
Invariance Measures for Neural Networks
Invariances in neural networks are useful and necessary for many tasks.
However, the representation of the invariance of most neural network models has
not been characterized. We propose measures to quantify the invariance of
neural networks in terms of their internal representation. The measures are
efficient and interpretable, and can be applied to any neural network model.
They are also more sensitive to invariance than previously defined measures. We
validate the measures and their properties in the domain of affine
transformations and the CIFAR10 and MNIST datasets, including their stability
and interpretability. Using the measures, we perform a first analysis of CNN
models and show that their internal invariance is remarkably stable to random
weight initializations, but not to changes in dataset or transformation. We
believe the measures will enable new avenues of research in invariance
representation
Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs
Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models
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