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The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the ...

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression o...

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessaril...

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image...

MOTIVATION: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. RESULTS: UBLAST and USEARCH are new algorithms enabling sensitive ...

Materias / palabras clave: Computer science; Visualization; Functional magnetic resonance imaging; Software; Workstation; Magnetic resonance imaging; Transformation (genetics); Overlay; Artificial intelligence; Computer vision; Unix; Pattern recog...

Materias / palabras clave: Independent component analysis; Principal component analysis; Representation (politics); Computer science; Algorithm; Gaussian; Artificial intelligence; Projection (relational algebra); Pattern recognition (psychology); ...

Materias / palabras clave: Silhouette; Cluster analysis; Mathematics; Cluster (spacecraft); Graphical display; Interpretation (philosophy); Artificial intelligence; Pattern recognition (psychology); Plot (graphics); Computer science; Statistics; C...

Materias / palabras clave: Softmax function; Computer science; Benchmark (surveying); Classifier (UML); Artificial intelligence; Convolutional neural network; Machine learning; Visualization; Pattern recognition (psychology); Geodesy; Geography

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture de...

Materias / palabras clave: Intraclass correlation; Reliability (semiconductor); Region of interest; Cerebral cortex; Computer science; Magnetic resonance imaging; Artificial intelligence; Pattern recognition (psychology); Cortex (anatomy); Human b...

Materias / palabras clave: Convolutional neural network; Skin cancer; Artificial intelligence; Deep learning; Medicine; Computer science; Biopsy; Skin biopsy; Deep neural networks; Cancer; Pattern recognition (psychology); Dermatology

Materias / palabras clave: Artificial intelligence; Computer vision; Active contour model; Computer science; Constraint (computer-aided design); Tracking (education); Spline (mechanical); Feature matching; Matching (statistics); Feature (linguisti...

Materias / palabras clave: Artificial intelligence; Robustness (evolution); Computer science; Detector; Hessian matrix; Computer vision; Pattern recognition (psychology); Feature matching; Mathematics; Image (mathematics); Applied mathematics; Gen...

Materias / palabras clave: Pascal (unit); Benchmark (surveying); Artificial intelligence; Computer science; Object detection; Cognitive neuroscience of visual object recognition; Machine learning; Visual inspection; Context (archaeology); Object (...

Materias / palabras clave: Neuroscience; Segmentation; Artificial intelligence; Psychology; Pattern recognition (psychology); Computer science; Communication

An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners ...

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, p...

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern ...

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up ...

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In...

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional netwo...

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVR...

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally t...