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Automatic movement recognition is often used to support various fields such as clinical, sports, and security. To date, there is a lack of a classification feature that is both interpretable and not movement-specific. Pr...

Background: Climate change increasingly shapes development outcomes, necessitating its integration into evaluation processes. In Africa, recurrent droughts, floods and temperature extremes disrupt progress across sector...

Plastic waste is a significant environmental concern, constituting a major portion of the global waste stream. Improper disposal and accumulation have led to severe environmental challenges, including pollution, harm to ...

Materias / palabras clave: Computer science; Artificial intelligence; Pattern recognition (psychology)

The Journal of Electronic Imaging (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging s...

Materias / palabras clave: Computer science; Artificial intelligence; Computer graphics (images); Computer vision

Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book...

From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques fo...

Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper ...

Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prot...

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with...

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a sh...

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational appro...

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning alg...

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We...

BACKGROUND: Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insuff...

Materias / palabras clave: Benchmark (surveying); Artificial intelligence; Computer science; Categorical variable; Cognitive neuroscience of visual object recognition; Scale (ratio); Object (grammar); Object detection; Field (mathematics); Pattern...

Materias / palabras clave: Computer science; Artificial intelligence; Convolutional neural network; Image (mathematics); Pixel; Perception; Transformation (genetics); Ground truth; Transfer (computing); Computer vision; Pattern recognition (psycho...

We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in...

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrar...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers...

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the dif...

An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and a...

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among...