2,973 research outputs found
Recognising facial expressions in video sequences
We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base
Continuous Wavelet Transform and Hidden Markov Model Based Target Detection
Standard tracking filters perform target detection process by comparing the sensor output signal with a predefined threshold. However, selecting the detection threshold is of great importance and a wrongly selected threshold causes two major problems. The first problem occurs when the selected threshold is too low which results in increased false alarm rate. The second problem arises when the selected threshold is too high resulting in missed detection. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. Although TBD techniques eliminate the need for detection threshold at sensor’s signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Continuous Wavelet Transform (CWT) and Hidden Markov Model (HMM) based target detection method for employing with TBD techniques which does not employ any thresholding
Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
We present a method for automated segmentation of the vasculature in retinal
images. The method produces segmentations by classifying each image pixel as
vessel or non-vessel, based on the pixel's feature vector. Feature vectors are
composed of the pixel's intensity and continuous two-dimensional Morlet wavelet
transform responses taken at multiple scales. The Morlet wavelet is capable of
tuning to specific frequencies, thus allowing noise filtering and vessel
enhancement in a single step. We use a Bayesian classifier with
class-conditional probability density functions (likelihoods) described as
Gaussian mixtures, yielding a fast classification, while being able to model
complex decision surfaces and compare its performance with the linear minimum
squared error classifier. The probability distributions are estimated based on
a training set of labeled pixels obtained from manual segmentations. The
method's performance is evaluated on publicly available DRIVE and STARE
databases of manually labeled non-mydriatic images. On the DRIVE database, it
achieves an area under the receiver operating characteristic (ROC) curve of
0.9598, being slightly superior than that presented by the method of Staal et
al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE
Trans Med Imag; added copyright notic
A graphical model based solution to the facial feature point tracking problem
In this paper a facial feature point tracker that is motivated by applications
such as human-computer interfaces and facial expression analysis systems is
proposed. The proposed tracker is based on a graphical model framework. The
facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated
on real video data under various conditions including occluded facial gestures
and head movements. It is also compared to two popular methods, one based
on Kalman filtering exploiting temporal relations, and the other based on active
appearance models (AAM). Improvements provided by the proposed approach
are demonstrated through both visual displays and quantitative analysis
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
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