Search results for "pattern"
showing 10 items of 4203 documents
The development of children's perception of hierarchical patterns : an investigation across tasks and populations
2011
The thesis investigated the development of children’s global/local processing hierarchical patterns introduced by Navon (1977). The objectives were to understand more comprehensively the developmental characteristics of children’s perception through their global and local processing of hierarchical patterns, by considering the effects of age, stimuli properties, duration of exposure to the stimuli and gender in a perceptual task and a drawing task. These effects were tested in 3 different populations: typically developing children, children with mental retardation and early blind children. The results revealed that typically developing children attended to both the local and global level of…
Semisupervised nonlinear feature extraction for image classification
2012
Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…
Kernel-Based Inference of Functions Over Graphs
2018
Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…
Model selection based product kernel learning for regression on graphs
2013
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the…
A structural cluster kernel for learning on graphs
2012
In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
2011
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…
Analyzing Protein-Protein Spatial-Temporal Dependencies from Image Sequences Using Fuzzy Temporal Random Sets
2008
Total Internal Reflection Fluorescence Microscopy (TIRFM) allows us to image fluorescenttagged proteins near the plasma membrane of living cells with high spatial-temporal resolution. Using TIRFM imaging of GFP-tagged clathrin endocytic proteins, areas of fluorescence are observed as overlapping spots of different sizes and durations. Standard procedures to measure protein-protein colocalization of dual labeled samples threshold the original graylevel images to segment areas covered by different proteins. This binary logic is not appropriate as it leaves a free tuning parameter which can influence the conclusions. Moreover, these procedures rely on simple statistical analysis based on corre…
A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.
2020
To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…
Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation
2018
Part 8: Pattern Recognition and Image Processing; International audience; Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugat…
New Error Measures to Evaluate Features on Three-Dimensional Scenes
2011
In this paper new error measures to evaluate image features in three-dimensional scenes are proposed and reviewed. The proposed error measures are designed to take into account feature shapes, and ground truth data can be easily estimated. As other approaches, they are not error-free and a quantitative evaluation is given according to the number of wrong matches and mismatches in order to assess their validity