Fast Fourier Transform

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy

Hands may be tracked before, during, and after occlusion, and a gesture may be recognized. Movement of two occluded hands may be tracked as a unit during an occlusion period. A type of synchronization characterizing the two occluded hands during the occlusion period may be determined based on the tracked movement of the occluded hands. Based on the determined type of synchronization, it may be determined whether directions of travel for each of the two occluded hands change during the occlusion period. Implementations may determine that a first hand and a second hand are occluded during an occlusion period, the first hand having come from a first direction and the second hand having come from a second direction. The first hand may be distinguished from the second hand after the occlusion period based on a determined type of synchronization characterizing the two hands, and a behavior of the two hands.

Tracking bimanual movements

A system that could automatically analyze the facial actions in real time has applications in a wide range of different fields. However, developing such a system is always challenging due to the richness, ambiguity, and dynamic nature of facial actions. Although a number of research groups attempt to recognize facial action units (AUs) by improving either the facial feature extraction techniques or the AU classification techniques, these methods often recognize AUs or certain AU combinations individually and statically, ignoring the semantic relationships among AUs and the dynamics of AUs. Hence, these approaches cannot always recognize AUs reliably, robustly, and consistently. In this paper, we propose a novel approach that systematically accounts for the relationships among AUs and their temporal evolutions for AU recognition. Specifically, we use a dynamic Bayesian network (DBN) to model the relationships among different AUs. The DBN provides a coherent and unified hierarchical probabilistic framework to represent probabilistic relationships among various AUs and to account for the temporal changes in facial action development. Within our system, robust computer vision techniques are used to obtain AU measurements. Such AU measurements are then applied as evidence to the DBN for inferring various AUs. The experiments show that the integration of AU relationships and AU dynamics with AU measurements yields significant improvement of AU recognition, especially for spontaneous facial expressions and under more realistic environment including illumination variation, face pose variation, and occlusion.

/pdf/facial-action-unit-recognition-by-exploiting-their-dynamic-396ubkgruq.pdf

Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

The appropriate selection of feature extraction method plays an important role in designing a pattern recognition system. The proper representation of features contributes to a significant improvement of the classifier performance and also to the reduction of time processing. The purpose of this study is to present a description of hand's posture features based on boundary histograms. The use of histograms aims to deal with two problems: the chain small magnitude circular-shift problem caused by posture rotation and, to attenuate the non-linearity caused by shape differences when performing gesture postures. We also present a fast search start point algorithm for the boundary chain that gives a rotation invariance property to the system. The performance was evaluated using 26 postures of American Sign Language, and a comparison with other algorithms is presented. As result we obtained a robust method to be used in largescale applications using neural networks.

/pdf/a-rotation-invariant-approach-on-static-gesture-recognition-dpwtc8zlnx.pdf

A rotation invariant approach on static-gesture recognition using boundary histograms and neural networks

This work describes a new pre-processing step to classify facial expression. Previous works suggest that Gabor wavelets applied to recognize facial expression images subtracted from neutral face from the same subject could achieve good recognition rate under controlled condition as eye and month alignment. We propose a recognition system where the Gabor kernels are applied on facial expression subtracted from an averaged neutral face. A fast pre-processing technique that generates a small dimension output data is also proposed. A correct recognition rate of 86.6% is obtained in a 7 upper face actions and 81.6% in a 7 lower face actions detection problem using a neural network based classifier. The performance is evaluated in a heterogeneous subject database with head motion and lighting variations.

Recognizing facial actions using Gabor wavelets with neutral face average difference

The automatic sign language translation still is the most complex and challenging task for video recognition and processing This work presents the Brazilian Sign Language Automatic Translation project and specifically focuses on low complexity Artificial Neural Networks dedicated to real-time video processing A new approach for reducing the computational complexity of the activation function of the Multi-Layer Perceptron is proposed in this work, allowing complex processing of video signals be done in real-time The low complexity neural networks are used in two stages of the system In the color detection and hand posture classification blocks The obtained results indicate an increase of the frame rate from 86 fps to 281 fps using a personal microcomputer with a USB webcam, without reduction of the correct recognition rate

Real-time video based finger spelling recognition system using low computational complexity Artificial Neural Networks

Hand Gesture Recognition Using T-CombNET : A New Neural Network Model

Describes an application of neural networks in the development of a travel forecast model for transportation planning. The model intends to quantify trips within the urban area through the representation of the land use-transportation system interaction. The data to express such a complex interaction is mainly obtained from remote sensing images that are processed in a geographical information system. We present the model's basic formulation and the results of a case study conducted in the Boston metropolitan area.

/pdf/neural-network-for-travel-demand-forecast-using-gis-and-2ye6fuwut9.pdf

M.V. Lamar

Papers

A rotation invariant approach on static-gesture recognition using boundary histograms and neural networks

Recognizing facial actions using Gabor wavelets with neutral face average difference

Real-time video based finger spelling recognition system using low computational complexity Artificial Neural Networks

Hand Gesture Recognition Using T-CombNET : A New Neural Network Model

Neural network for travel demand forecast using GIS and remote sensing