Search

The analysis of skin color with makeup is needed to examine the effect of makeup to human skin. Foundation is cosmetics to cover undesirable color on skin and gives basic color to skin. It is important evaluate a change of skin color by the foundation. The present paper modeled the optics of skin with foundation makeup by two layers. Then, the surface spectrum of skin with foundation makeup is estimated by the Kubelka-Munk theory for radiation transfer in the turbid medium. The proposed algorithm can predict the spectral shape of skin surface with different types of foundation by appropriately determining model parameters. As an application of the skin reflectance estimation, we have developed a color simulator for human skin with foundation makeup. Experimental results showed a good accuracy of our estimation results. [ABSTRACT FROM AUTHOR]

This paper considers scale invariance of statistical image models. We study statistical scale invariance of the covariance structure of jet space under scale space blurring and derive the necessary structure and conditions of the jet covariance matrix in order for it to be scale invariant. As part of the derivation, we introduce a blurring operator At that acts on jet space contrary to doing spatial filtering and a scaling operator Ss. The stochastic Brownian image model is an example of a class of functions which are scale invariant with respect to the operators At and Ss. This paper also includes empirical results where we estimate the scale invariant jet covariance of natural images and show that it resembles that of Brownian images. [ABSTRACT FROM AUTHOR]

A general algorithm framework for 3D mesh parametrization is proposed in this paper. Under this framework, a parametrization algorithm is divided into three steps. In the first step, the linearly reconstructing weights of each vertex with respect to its neighbours are computed. These weights are then used to computed a initial parametrization mesh, and in the third step, this initial mesh is rotated and scaled to obtain a parametrization mesh with high isometric precision. Four parametrization algorithms are proposed based on this framework. Examples show the effectiveness and applicability of the parametrization algorithms proposed in the paper. [ABSTRACT FROM AUTHOR]

This paper presents the fusion of monogenic signal processing and differential geometry to enable monogenic analyzing of local intrinsic 2D features of low level image data. New rotational invariant features such as structure and geometry (angle of intersection) of two superimposed intrinsic 1D signals will be extracted without the need of any steerable filters. These features are important for all kinds of low level image matching tasks in robot vision because they are invariant against local and global illumination changes and result from one unique framework within the monogenic scale-space. [ABSTRACT FROM AUTHOR]

Person tracking is a key requirement for modern service robots. But methods for robot vision have to fulfill several constraints: they have to be robust to errors evoked by noisy sensor data, they have to be able to work under real-world conditions, and they have to be fast and computationally inexpensive. In this paper we present an approach for tracking the position of a person in 3D based on a particle filter. In our framework, each particle represents a hypothesis for the 3D position, velocity and size of the person's head being tracked. Two cameras are used for the evaluation of the particles. The particles are weighted by projecting them onto the camera image and applying a color-based perception model. This model uses skin color cues for face tracking and color histograms for body tracking. In contrast to feature-based approaches, our system even works when the person is temporary or partially occluded. [ABSTRACT FROM AUTHOR]

The watchman route problem (WRP) was first introduced in 1988 and is defined as follows: How to calculate a shortest route completely contained inside a simple polygon such that any point inside this polygon is visible from at least one point on the route? So far the best known result for the WRP is an ${\cal O}(n^3 \log n)$ runtime algorithm (with inherent numerical problems of its implementation). This paper gives an $\kappa(\varepsilon)\times {\cal O}(kn)$ approximate algorithm for WRP by using a rubberband algorithm, where n is the number of vertices of the simple polygon, k the number of essential cuts, ε the chosen accuracy constant for the minimization of the calculated route, and κ(ε) equals the length of the initial route minus the length of the calculated route, divided by ε. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm for computing multi-resolution optical flow, and compares this new hierarchical method with the traditional combination of the Lucas-Kanade method with a pyramid transform. The paper shows that the new method promises convergent optical flow computation. Aiming at accurate and stable computation of optical flow, the new method propagates results of computations from low resolution images to those of higher resolution. The resolution of images increases this way for the sequence of images used in those calculations. The given input sequence of images defines the maximum of possible resolution. [ABSTRACT FROM AUTHOR]

This paper proposes an novel approach for automatic gridding of cDNA microarray images based on histogram representation. The approach constructs histogram representation to characterize microarray images and use it, instead of raw signal intensities used in previous gridding methods, to identify spots. The histogram representation can efficiently reduce noise and the influence from low raw signal intensities and local contaminations. The proposed approach is successfully tested on different types of microarray images and is compared with several previous algorithms. [ABSTRACT FROM AUTHOR]

In single photon emission computed tomography (SPECT), pinhole and cone-beam collimators are used for small object imaging. It is accepted that a pinhole collimator should be used if the object is very small, and the cone-beam collimator should be used if the object is larger. This paper presents criteria to determine which collimation geometry is more advantageous in terms of spatial resolution and detection sensitivity. [ABSTRACT FROM AUTHOR]

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%. [ABSTRACT FROM AUTHOR]

The home remote monitoring systems aim to make a protective contribution to the well being of individuals (patients, elderly persons) requiring moderate amounts of support for independent living spaces, and improving their everyday life. Existing researches of these systems suffer from lack of experimental data and a standard medical database intended for their validation and improvement. This paper presents a multi-sensors environment for acquiring and recording a multimodal medical database, which includes physiological data (cardiac frequency, activity or agitation, posture, fall), environment sounds and localization data. It provides graphical interface functions to manage, process and index these data. The paper focuses on the system implementation, its usage and it points out possibilities for future work. [ABSTRACT FROM AUTHOR]

How to wean patients efficiently off mechanical ventilation continues to be a challenge for medical professionals. In this paper we have described a novel approach to the study of a ventilator weaning prediction system (VWPS). Firstly, we have developed and written three Artificial Neural Network (ANN) algorithms to predict a weaning successful rate based on the clinical data. Secondly, we have implemented two user-friendly weaning success rate prediction systems; the VWPS system and the BWAP system. Both systems could be used to help doctors objectively and effectively predict whether weaning is appropriate for patients based on the patients' clinical data. Our system utilizes the powerful processing abilities of MatLab. Thirdly, we have calculated the performance through measures such as sensitivity and accuracy for these three algorithms. The results show a very high sensitivity (around 80%) and accuracy (around 70%). To our knowledge, this is the first design approach of its kind to be used in the study of ventilator weaning success rate prediction. [ABSTRACT FROM AUTHOR]

Tongueprints are fissile texture on tongue and one of observational contents of tongue diagnosis, which is an important diagnostic method in Traditional Chinese Medicine (TCM). With deep researches on tongueprints, three basic problems emerge: (1) TCM has always held that healthy individuals do not exhibit tongueprints, but in the recent years some medical researchers have found some healthy individuals in their small sample (< 1000 cases) tongue image database do exhibit tongueprints. How about do tongueprints in a large amount of healthy individuals (> 2000 cases)? (2) If about a third of a large amount of healthy individuals have tongueprints, mainstream diagnosis by inspecting tongueprints should lead to an over-diagnosis problem and some healthy individuals are diagnosed mistakenly as unhealthy individuals because it holds tongueprints themselves declare publicly diseases. Thus it is necessary to diagnose definitely a tongueprint image belong to an unhealthy individual based on tongueprint features. This is a basic problem and is of theoretical and practical importance for diagnosis by inspecting tongueprints. And which features of tongueprints can be used to diagnose definitely a tongueprint image belong to an unhealthy individual? (3) Actually, the second problem is recognition and classification of tongueprints in healthy and unhealthy individuals. To further promote the modernization process of the traditional tongue diagnosis Chow are the researches done on automatic classification of tongueprints in healthy and unhealthy individuals? After further making sure there appear tongueprints in healthy individuals and finding which features of tongueprints can be used to diagnose definitely a tongueprint image belong to an unhealthy individual by statistic analysis on a large database of tongue images (more than 3000 cases), this paper do the researches about automatic classification of tongueprints in healthy and unhealthy individuals based on the large database. Firstly tongeprint regions are extracted by multi-direction synthetic tongueprint extraction. Then a SVM classifier is used to recognize a tongueprint image belong to a healthy or unhealthy individual based on tongueprint features. Recognition accurate rate of tongueprints in healthy and unhealthy individuals respectively are 75.81% and 74.61% based on the large database. [ABSTRACT FROM AUTHOR]

The Sublingual Vein Diagnosis, one part of Tongue Diagnosis, plays an important role in deciding the healthy condition of humans. This paper focuses on establishing a feature quantization framework for the inspection of sublingual veins of healthy humans, composed of two parts: the segmentation of sublingual veins of healthy humans and the feature quantization of them. Firstly, a novel technique of sublingual vein segmentation is proposed here. Sublingual Vein Color Model, which combines the Bayesian Decision with CIEYxy color space, is established based on a large number of labeled sublingual images. Experiments prove that the proposed method performs well on the segmentation of images from healthy humans with weak color contrast between sublingual vein and tongue proper. And then, a chromatic system in conformity with diagnostic standard of Traditional Chinese Medicine doctors is established to describe the chromatic feature of sublingual veins. Experimental results show that the geometrical and chromatic features quantized by the proposed framework are properly consistent with the diagnostic standard summarized by TCM doctors for healthy humans. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive feature extraction method for pattern recognition of hand gesture action sEMG to enhance the reusability of myoelectric control. The feature extractor is based on wavelet packet transform and Local Discriminant Basis (LDB) algorithms to select several optimized decomposition subspaces of origin SEMG waveforms caused by hand gesture motions. Then the square roots of mean energy of signal in those subspaces are calculated to form the feature vector. In data acquisition experiments, five healthy subjects implement six kinds of hand motions every day for a week. The recognition results of hand gesture on the basis of the measured SEMG signals from different use sessions demonstrate that the feature extractor is effective. Our work is valuable for the realization of myoelectric control system in rehabilitation and other medical applications. [ABSTRACT FROM AUTHOR]

This paper attempts to extract tongue cracks, one of pathological features in tongue diagnosis. A detection scheme is proposed to extract tongue cracks. This scheme is based on the wide line detector which extracts the whole of the line by employing an isotropic nonlinear filter. The wide line detector is improved based on the properties of tongue cracks. The proposed scheme has been tested on a total of 286 cracked tongue images and our experimental results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a novel feature selection technique for SELDI-TOF spectrum data. The new technique, called RISC (Relevance Index by Sample Counting) , measures the relevance of features based on each sample's discriminating power to partition the samples in the opposite class. We also proposes a heuristic searching method to obtain the optimal feature set, which makes use of the relevance parameters. Our technique is fast even for extremely high-dimensional datasets such as SELDI spectrum, since it has low computational complexity and consists of simple counting operations. The new technique also shows good performance comparable to the conventional feature selection techniques from the experiment on three clinical datasets from NCI/CCR and FDA/CBER Clinical Proteomics Program Databank: Ovarian 4-3-02, Ovarian 7-8-02, Prostate. [ABSTRACT FROM AUTHOR]

We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute. [ABSTRACT FROM AUTHOR]

Recent developments in freehand 3D ultrasound (US) have shown how image registration and speckle decorrelation methods can be used for 3D reconstruction instead of relying on a tracking device. Estimating elevational separation between untracked US images using speckle decorrelation is error prone due to the uncertainty that plagues the correlation measurements. In this paper, using maximum entropy estimation methods, the uncertainty is directly modeled from the calibration data normally used to estimate an average decorrelation curve. Multiple correlation measurements can then be fused within a maximum likelihood estimation framework in order to reduce the drift in elevational pose estimation over large image sequences. The approach is shown to be effective through empirical results on simulated and phantom US data. [ABSTRACT FROM AUTHOR]

This paper proposes to use the Laplace-Beltrami spectrum (LBS) as a global shape descriptor for medical shape analysis, allowing for shape comparisons using minimal shape preprocessing: no registration, mapping, or remeshing is necessary. The discriminatory power of the method is tested on a population of female caudate shapes of normal control subjects and of subjects with schizotypal personality disorder. [ABSTRACT FROM AUTHOR]

Characterization and quantification of diffuse parenchymal lung disease (DPLD) severity using MDCT, mainly in interstitial lung diseases and emphysema, is an important issue in clinical research for the evaluation of new therapies. This paper develops a 3D automated approach for detection and diagnosis of DPLDs (emphysema, fibrosis, honeycombing, ground glass).The proposed methodology combines multi-resolution image decomposition based on 3D morphological filtering, and graph-based classification for a full characterization of the parenchymal tissue. The very promising results obtained on a small patient database are good premises for a near implementation and validation of the proposed approach in clinical routine. [ABSTRACT FROM AUTHOR]

Consistency of MR scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. Accurate manual scan planning is tedious and requires skillful operators. On the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. This paper presents a novel method for automated planning of MRI scans of the spine. Lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. The automated scan planning (ASP) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. A validation study demonstrates the robustness of the proposed method and its feasibility for clinical use. [ABSTRACT FROM AUTHOR]

Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium and the attenuated AIF based on a Hierarchical Bayesian Model (HBM). With the proposed method, both the AIF and the response function are modeled as smoothed functions by using Bayesian penalty splines (P-Splines). The derived AIF is then used to estimate the impulse response of the myocardium based on deconvolution analysis. The proposed technique is validated both with simulated data using the MMID4 model and ten in vivo data sets for estimating myocardial perfusion reserve rates. The results demonstrate the ability of the proposed technique in accurately reconstructing the desired AIF for myocardial perfusion quantification. The method does not involve any MRI pulse sequence modification, and thus is expected to have wider clinical impact. [ABSTRACT FROM AUTHOR]

This paper presents a fast image synthesis procedure for elastic volumes under deformation. Given the node displacements of a mesh and the 3D image voxel data of an undeformed volume, the method maps the image plane pixels to be synthesized from the deformed configuration back to the nominal pre-deformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the voxel volume. For smooth interpolation, this mapping requires the identification of the mesh element enclosing each image pixel. To accelerate this point location procedure, a fast method of marking the image pixels is employed by finding the intersection of the mesh and the image, and marking this intersection on the image pixels using Bresenham's line drawing algorithm. A deformable tissue phantom was constructed, it was modeled using the finite element method, and its 3D ultrasound volume was acquired in its undeformed state. Actual B-mode images of the phantom under deformation by the ultrasound probe were then compared with the corresponding synthesized images simulated for the same deformations. Results show that realistic images can be synthesized in real-time using the proposed technique. [ABSTRACT FROM AUTHOR]

In this paper we present a new methodology for cardiac motion tracking in tagged MRI using nonrigid image registration based on subdivision surfaces and subdivision lattices. We use two sets of registrations to do the motion tracking. First, a set of surface registrations is used to create and initially align the subdivision model of the left ventricle with short-axis and long-axis MR images. Second, a series of volumetric registrations are used to perform the motion tracking and to reconstruct the 4D cardiac motion field from the tagged MR images. The motion of a point in the myocardium over time is calculated by registering the images taken during systole to the set of reference images taken at end-diastole. Registration is achieved by optimizing the positions of the vertices in the base lattice so that the mutual information of the images being registered is maximized. The presented method is validated using a cardiac motion simulator and we also present strain measurements obtained from a group of normal volunteers. [ABSTRACT FROM AUTHOR]

In this paper, an original approach for the segmentation of tensor fields is proposed. Based on the modeling of the data by means of Gaussian mixtures directly in the tensor domain, this technique presents a wide range of applications in medical image processing, particularly for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). The performance of the segmentation method proposed is shown through the segmentation of the corpus callosum from a dataset of 32 DT-MRI volumes. Comparison with a recent and related segmentation approach is favorable to our method, showing its capability for the automatic extraction of anatomical structures in the white matter. [ABSTRACT FROM AUTHOR]

This paper presents a novel segmentation-based approach for fiber-tract extraction in diffusion-tensor (DT) images. Typical tractography methods, incorporating thresholds on fractional anisotropy and fiber curvature to terminate tracking, can face serious problems arising from partial voluming and noise. For these reasons, tractography often fails to extract thin tracts with sharp changes in orientation, e.g. the cingulum. Unlike tractography—which disregards the information in the tensors that were previously tracked—the proposed method extracts the cingulum by exploiting the statistical coherence of tensors in the entire structure. Moreover, the proposed segmentation-based method allows fuzzy class memberships to optimally extract information within partial-volumed voxels. Unlike typical fuzzy-segmentation schemes employing Gaussian models that are biased towards ellipsoidal clusters, the proposed method models the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. Furthermore, it exploits the nonparametric model to capture the spatial continuity and structure of the fiber bundle. The results on real DT images demonstrate that the proposed method extracts the cingulum bundle significantly more accurately as compared to tractography. [ABSTRACT FROM AUTHOR]

In this paper we propose a new approach for false positive reduction in the field of mammographic mass detection. The goal is to distinguish between the true recognized masses and the ones which actually are normal parenchyma. Our proposal is based on Local Binary Patterns (LBP) for representing salient micro-patterns and preserving at the same time the spatial structure of the masses. Once the descriptors are extracted, Support Vector Machines (SVM) are used for classifying the detected masses. We test our proposal using a set of 1792 suspicious regions of interest extracted from the DDSM database. Exhaustive experiments illustrate that LBP features are effective and efficient for false positive reduction even at different mass sizes, a critical aspect in mass detection systems. Moreover, we compare our proposal with current methods showing that LBP obtains better performance. [ABSTRACT FROM AUTHOR]

The presence of Rician noise in magnetic resonance imaging (MRI) introduces systematic errors in diffusion tensor imaging (DTI) measurements. This paper evaluates gradient direction schemes and tensor estimation routines to determine how to achieve the maximum accuracy and precision of tensor derived measures for a fixed amount of scan time. We present Monte Carlo simulations that quantify the effect of noise on diffusion measurements and validate these simulation results against appropriate in-vivo images. The predicted values of the systematic and random error caused by imaging noise are essential both for interpreting the results of statistical analysis and for selecting optimal imaging protocols given scan time limitations. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for the segmentation of white matter tracts based on Finsler active contours. This technique provides an optimal measure of connectivity, explicitly segments the connecting fiber bundle, and is equipped with a metric which is able to utilize the directional information of high angular resolution data. We demonstrate the effectiveness of the algorithm for segmenting the cingulum bundle. [ABSTRACT FROM AUTHOR]

Magnetic resonance diffusion imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet. [ABSTRACT FROM AUTHOR]

Pose variance remains a challenging problem for face recognition. In this paper, a scheme including image synthesis and recognition is proposed to improve the performance of automatic face recognition system. In the image synthesis part, a series of pose-variant images are produced based on three images respectively with front, left-profile, right-profile poses, and are added into the gallery in order to overcome the pose inconsistence between probes and images in the database. In the recognition part, a multi-level fusion method based on Gabor-combined features and gray-intensity features (GCGIF) is presented. Both amplitude features and phase features extracted through Gabor filters are utilized. Fusion is introduced in both the face representation level and the confidence level. Experiment results show that the integrated scheme achieve superior recognition performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified scheme of subspace and distance metric learning under the Bayesian framework for face recognition. According to the local distribution of data, we divide the k-nearest neighbors of each sample into the intra-person set and the inter-person set, and we aim to learn a distance metric in the embedding subspace, which can make the distances between the sample and its intra-person set smaller than the distances between it and its inter-person set. To reach this goal, we define two variables, that is, the intra-person distance and the inter-person distance, which are from two different probabilistic distributions, and we model the goal with minimizing the overlap between two distributions. Inspired by the Bayesian classification error estimation, we formulate it by minimizing the Bhattachyrra coefficient between two distributions. The power of the proposed approach are demonstrated by a series of experiments on the CMU-PIE face database and the extended YALE face database. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel boosting based classification algorithm, SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). Unlike the conventional AdaBoost based algorithms widely applied in computer vision, SODA-Boosting does not involve time consuming procedures to search a huge feature pool in every iteration during the training stage. Instead, in each iteration SODA-Boosting efficiently computes discriminative weak classifiers in closed-form, based on reasonable hypotheses on the distribution of the weighted training samples. As an application, SODA-Boosting is employed for image based gender recognition. Experimental results on publicly available FERET database are reported. The proposed algorithm achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance to relevant boosting based algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel appearance-based representation, called Structured Ordinal Feature (SOF). SOF is a binary string encoded by combining eight ordinal blocks in a circle symmetrically. SOF is invariant to linear transformations on images and is flexible enough to represent different local structures of different complexity. We further extend SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position. In this way, MSOF encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation. We also present an efficient algorithm for computing MSOF using integral images. Based on MSOF, statistical analysis and learning are performed to select most effective features and construct classifiers. The proposed method is evaluated with face recognition experiments, in which we achieve a high rank-1 recognition rate of 98.24% on FERET database. [ABSTRACT FROM AUTHOR]

This paper describes a model-assisted system for reconstruction of 3D faces from a single consumer quality camera using a structure from motion approach. Typical multi-view stereo approaches use the motion of a sparse set of features to compute camera pose followed by a dense matching step to compute the final object structure. Accurate pose estimation depends upon precise identification and matching of feature points between images, but due to lack of texture on large areas of the face, matching is prone to errors. To deal with outliers in both the sparse and dense matching stages, previous work either relies on a strong prior model for face geometry or imposes restrictions on the camera motion. Strong prior models result in a serious compromise in final reconstruction quality and typically bear a signature resemblance to a generic or mean face. Model-based techniques, while giving the appearance of face detail, in fact carry this detail over from the model prior. Face features such as beards, moles, and other characteristic geometry are lost. Motion restrictions such as allowing only pure rotation are nearly impossible to satisfy by the end user, especially with a handheld camera. We significantly improve the robustness and flexibility of existing monocular face reconstruction techniques by introducing a deformable generic face model only at the pose estimation, face segmentation, and preprocessing stages. To preserve data fidelity in the final reconstruction, this generic model is discarded completely and dense matching outliers are removed using tensor voting: a purely data-driven technique. Results are shown from a complete end to end system. [ABSTRACT FROM AUTHOR]

Recently skeletal motion data is obtained from the motion capture and is used for movie and sports. The movie production does not need the skeletal motion data but the body surface data. It is difficult to generate body surface data from only skeletal motion data because muscle deforms according to the skeletal motion. Muscle deformation occurs with arm and leg joint rotation. In this paper, we visualize body surface deformation based on the deformation mechanism that is applicable to human motion according to anatomy based modeling. We propose the method generating body surface by covering the skeletal muscles using a thin film based on the level set method. We demonstrate the effectiveness of the system through the generation of the movement of a body builder by using the proposed system. [ABSTRACT FROM AUTHOR]

Over the last two decades automatic facial expression recognition has become an active research area. Facial expressions are an important channel of non-verbal communication, and can provide cues to emotions and intentions. This paper introduces a novel method for facial expression recognition, by assembling contour fragments as discriminatory classifiers and boosting them to form a strong accurate classifier. Detection is fast as features are evaluated using an efficient lookup to a chamfer image, which weights the response of the feature. An Ensemble classification technique is presented using a voting scheme based on classifiers responses. The results of this research are a 6-class classifier (6 basic expressions of anger, joy, sadness, surprise, disgust and fear) which demonstrate competitive results achieving rates as high as 96% for some expressions. As classifiers are extremely fast to compute the approach operates at well above frame rate. We also demonstrate how a dedicated classifier can be consrtucted to give optimal automatic parameter selection of the detector, allowing real time operation on unconstrained video. [ABSTRACT FROM AUTHOR]

We propose a novel method for 3D head reconstruction and view-invariant recognition from single 2D images. We employ a deterministic Shape From Shading (SFS) method with initial conditions estimated by Hybrid Principal Component Analysis (HPCA) and multi-level global optimization with error-dependent smoothness and integrability constraints. Our HPCA algorithm provides initial estimates of 3D range mapping for the SFS optimization, which is quite accurate and yields much improved 3D head reconstruction. The paper also includes significant contributions in novel approaches to global optimization and in SFS handling of variable and unknown surface albedo, a problem with unsatisfactory solutions by prevalent SFS methods. In the experiments, we reconstruct 3D head range images from 2D single images in different views. The 3D reconstructions are then used to recognize stored model persons. Empirical results show that our HPCA based SFS method provides 3D head reconstructions that notably improve the accuracy compared to other approaches. 3D reconstructions derived from side view (profile) images of 40 persons are tested against 80 3D head models and a recognition rate of over 90% is achieved. Such a capability was not demonstrated by any other method we are aware of. [ABSTRACT FROM AUTHOR]

This paper addresses the recovery of face models from stereo pairs of images in the presence of foreign-body occlusions. In the proposed approach, a 3D morphable model (3DMM) for faces is augmented by an occlusion map defined on the model shape, and occlusion is detected with minimal computational overhead by incorporating robust estimators in the fitting process. Additionally, the method uses an explicit model for texture (or reflectance) in addition to shape, which is in contrast to most existing multi-view methods that use a shape model alone. We argue that both model components are required to handle certain classes of occluders, and we present empirical results to support this claim. In fact, the empirical results in this paper suggest that even in the absence of occlusions, stereo reconstruction using existing shape-only face models can perform poorly by some measures, and that the inclusion of an explicit texture model may be worth its computational expense. [ABSTRACT FROM AUTHOR]

The vision of Ambient Intelligence (AmI) presumes a plethora of embedded services and devices that all endeavor to support humans in their daily activities as unobtrusively as possible. Hardware gets distributed throughout the environment, occupying even the fabric of our clothing. The environment is equipped with a diversity of sensors, the information of which can be accessed from all over the AmI network. Individual services are distributed over hardware, share sensors with other services and are generally detached from the traditional singleaccess- point computer (see also the paper of Pantic et al. in this volume [51]). [ABSTRACT FROM AUTHOR]

Automatic multimodal recognition of spontaneous emotional expressions is a largely unexplored and challenging problem. In this paper, we explore audio-visual emotion recognition in a realistic human conversation setting—the Adult Attachment Interview (AAI). Based on the assumption that facial expression and vocal expression are at the same coarse affective states, positive and negative emotion sequences are labeled according to Facial Action Coding System. Facial texture in visual channel and prosody in audio channel are integrated in the framework of Adaboost multi-stream hidden Markov model (AdaMHMM) in which the Adaboost learning scheme is used to build component HMM fusion. Our approach is evaluated in AAI spontaneous emotion recognition experiments. [ABSTRACT FROM AUTHOR]

Instinctive computing is a computational simulation of biological and cognitive instincts. It is a meta-program of life, just like universal gravity in nature. It profoundly influences how we look, feel, think, and act. If we want a computer to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have primitive instincts. In this paper, we will review the recent work in this area, the building blocks for the instinctive operating system, and potential applications. The paper proposes a 'bottom-up' approach that is focused on human basic instincts: forage, vigilance, reproduction, intuition and learning. They are the machine codes in human operating systems, where high-level programs, such as social functions can override the low-level instinct. However, instinctive computing has been always a default operation. Instinctive computing is the foundation of Ambient Intelligence as well as Empathic Computing. It is an essential part of Human Computing. [ABSTRACT FROM AUTHOR]

This paper presents a system to monitor a space and detect intruders. Specifically, the system analyzes color video to determine if an intruder entered the space. The system compares any new items in a video frame to a collection of known items (e.g. pets) in order to allow known items to enter and leave the space. Simple trip-line systems using infrared sensors normally fail when a pet wanders into the path of a sensor. This paper details an adaptation of the mean shift algorithm (described by Comaniciu et al.) in RGB color space to discern between intruders and benign environment changes. A refinement to the histogram bin function used in the tracking algorithm is presented which increases the robustness of the algorithm. [ABSTRACT FROM AUTHOR]

Display systems with high quality and wide display screen can be used at fixed place due to big size and heavy weight. On the other hands, mobile systems have small display screen and thus decrease user-immersion because it is compact. In this paper, we resolve these drawbacks of established display systems by proposing a novel portable projection-based augmented reality (AR) system. The system uses a camera mounted on PDA and a small projector to measure characteristics of screen surface. We use geometric correction and radiometric compensation technique to project undistorted image in user viewpoint onto an arbitrary screen surface. Rather than float point operations, we use integer point operations to enhance system performance. Our proposed system not only supports mobility but also wide display screen. Usability of our system is verified through experimental results and user evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a technique that allows test engineers to visually analyze and explore within memory chip test data. We represent the test results from a generation of chips along a traditional 2D grid and a spiral. We also show correspondences in the test results across multiple generations of memory chips. We use simple geometric "glyphs" that vary their spatial placement, color, and texture properties to represent the critical attribute values of a test. When shown together, the glyphs form visual patterns that support exploration, facilitate discovery of data characteristics, relationships, and highlight trends and exceptions in the test data that are often difficult to identify with existing statistical tools. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for iris recognition by analyzing the iris patterns. We propose an iris classification method that divides the normalized iris image into several regions to avoid the iris image with several noise factors (eyelids and eyelashes) and reduce the error rates. In every region, effective features are extracted by the proposed method of local edge pattern (LEP) for edge and corner detection. Feature vectors are linearly combined into a two dimensional matrix that represents every iris image for further recognition. Then 2D linear discriminant analysis (2DLDA) is used to identify the person. We use two public and freely available iris image databases for evaluation, organized in training and test sets respectively. Experimental results show that the recognition rate of the two iris image databases have achieved similar performance more than 98% and the proposed method has an encouraging performance and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we systematically examine two fundamental questions in information visualization - how to define effective visualization and how to measure it. Through a literature review, we point out that the existing definitions of effectiveness are incomplete and often inconsistent - a problem that has deeply affected the design and evaluation of visualization. There is also a lack of standards for measuring the effectiveness of visualization as well as a lack of standardized procedures. We have identified a set of basic research issues that must be addressed. Finally, we provide a more comprehensive definition of effective visualization and discuss a set of quantitative and qualitative measures. The work presented in this paper contributes to the foundational research of information visualization. [ABSTRACT FROM AUTHOR]

This paper presents a robust segmentation method based on the integrated squared error or L2 estimation (L2E). Formulated under the Finite Gaussian Mixture (FGM) framework, the new model (FGML2E) has a strong discriminative ability in capturing the major parts of intensity distribution without being affected by outlier structures or heavy noise. Comparisons are made with two popular solutions, the EM and FCM algorithms, and the experimental results clearly show the improvement made by our model. [ABSTRACT FROM AUTHOR]

In this paper we present a novel approach for mimicking expressions in 3D from a monocular video sequence. To this end, first we construct a high resolution semantic mesh model through automatic global and local registration of a low resolution range data. Such a model is represented using predefined set of control points in a compact fashion, and animated using radial basis functions. In order to recover the 2D positions of the 3D control points in the observed sequence, we use cascade Adaboost-driven search. The search space is reduced through the use of predictive expression modeling. The optimal configuration of the Adaboost responses is determined using combinatorial linear programming which enforces the anthropometric nature of the model. Then the displacement can be reproduced on any version of the model, registered on another face. Our method doesn't require dense stereo estimation and can then produce realistic animations, using any 3D model. Promising experimental results demonstrate the potential of our approach. [ABSTRACT FROM AUTHOR]