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Active shape model is widely used for facial feature localization. Regarding the traditional ASM algorithm can't describe the object shape precisely, an improved ASM algorithm is proposed. At first, we establish shape model and use PCA (Principle Component Analysis) to transform high-dimensional data to lower dimensions. Another work is to establish local texture model giving sample points with different...
Parametrisation of the shape of deformable objects is of paramount importance in many computer vision applications. Many state-of-the-art statistical deformable models perform landmark localisation via optimising an objective function over a certain parametrisation of the object's shape. Arguably, the most popular way is by employing statistical techniques. The points of shape samples of an object...
We address the problem of transferring motion between captured 4D models. We particularly focus on human subjects for which the ability to automatically augment 4D datasets, by propagating movements between subjects, is of interest in a great deal of recent vision applications that builds on human visual corpus. Given 4D training sets for two subjects for which a sparse set of corresponding keyposes...
This paper tackles the problem of reconstructing 3D human poses from 2D landmarks, which is still an ill-posed problem. A widely-used approach is active shape model (ASM) which considers an unknown 3D shape as a linear combination of predefined basis shapes. The existing methods often resolve an optimization problem to reckon the weights and viewpoints of basis shapes, but they could fall into a locally-optimal...
Densely sampled dynamic geophysical data are often modeled using principal components analysis (PCA, a.k.a. empirical orthogonal function or EOF analysis) to provide constraints for their inversion with remote sensing techniques. We show that overcomplete sparsifying dictionaries, generated using dictionary learning, provide a more informative basis for geophysical signal representation. Relative...
Hand shape recognition is a challenging task because hands are deformable objects. Some techniques for hand shape recognition using Computer Vision have been proposed. The key problem is how to make hand gestures understood by computers/mobile devices. In this paper we present a study about Principal Component Analysis (PCA) used to reduce the dimensionality and extract features of images of the human...
Recently, the synthesis of 3D dynamic expressions has become an important concern in computer graphics, facial recognition, etc. In this study, we propose a regression based joint subspace learning method for the automatic synthesis of 3D dynamic expression images. This method synthesizes 3D dynamic expression images from a single 2D facial image. We use two subspaces (the view subspace and the frame...
We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised...
Images are usually represented by different groups of features, such as color, shape and texture attributes. In this paper, we propose a classification approach that integrates multiple features, such as spectral and spatial information. We refer this approach to multiple feature learning via rotation (MFL-R) strategy, which adopt a rotation-based ensemble method by using a data transformation approach...
In this paper we are interesting in knowing which features provide useful information for detecting a fall and how the set of selected characteristics impact the performance of detection. Then we define a large set of possible features, which are extracted from a cloud of points of a person by the kinect device, some of features were used in previous work, and we propose to add and evaluate the effect...
In this paper, we develop a spatio-temporal cascade shape regression (STCSR) model for robust facial shape tracking. It is different from previous works in three aspects. Firstly, a multi-view cascade shape regression (MCSR) model is employed to decrease the shape variance in shape regression model construction, which is able to make the learned regression model more robust to shape variances. Secondly,...
In image segmentation, the shape knowledge of the object may be used to guide the segmentation process. From a training set of representative shapes, a statistical model can be constructed and used to constrain the segmentation results. The shape space is usually constructed with tools such such as principal component analysis (PCA). However the main assumption of PCA that shapes lie a linear space...
Lung cancer is one of the malignant tumors with the fastest increasing speed of incidence and death rate. It appears in the form of spherical nodules in a conventional radiograph. However, some lung nodules are not be able to be detected due to their overlap with normal anatomic structures such as ribs and clavicles. In this paper, a rib suppression method based on principle component analysis (PCA)...
Statistical shape models generally characterize shape variations linearly by principal component analysis (PCA), which assumes that the non-rigid shape parameters are drawn from a Gaussian distribution. This practical assumption is often not valid. Instead, we propose a constrained local model based on independent component analysis (ICA) and use kernel density estimation (KDE) for non-parametrically...
In this paper, we propose a formulation of graph-cut segmentation that relies on a generative image model by incorporating both local and global shape priors. With surface estimation, rather than pixel classification, we cast the segmentation problem as a maximum a posteriori estimation from the image intensities via a cut through a multi-layer three-dimensional mesh model that preserves the topology...
When segmenting images of low quality or with missing data, statistical prior information about the shapes of the objects to be segmented can significantly aid the segmentation process. However, defining probability densities in the space of shapes is an open and challenging problem. In this paper, we propose a nonparametric shape prior model for image segmentation problems. In particular, given example...
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
Gender recognition has important applications in apparel design, social security, and human-computer interaction systems. In this paper, we investigate gender-recognition technologies using 3-D human body shape. The front and side silhouettes from 459 female subjects and 107 male subjects were extracted and then modeled using normalized Elliptic Fourier descriptors. Principal Component Analysis (PCA)...
To improve the robustness against variation in shooting angles, we previously proposed using an asymptotic expansion of the Gabor transform of ear images to compute the Gabor features of other poses and using these estimates in multiple linear discriminant analysis to enhance feature discriminability. Extending this study, the accuracies are compared with other standard methods that can be used to...
Technological advancement had replaced humans with machines in almost every field. Banking automation have reduced human workload by introducing machines. Tedious task like currency handling that require more care are simplified by banking automation. When machines are handling currency they should recognize it. In this paper a method for currency recognition using principal component analysis is...
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