Modelling Objects Using Kernel Principal Component Analysis

Object detection is a technologically challenging and practically useful field of computer vision.The success of object detection relies on modelling of an object class. Statistical shape modelling is one of the popular method. Object modelling starts with asset of examples shapes (the training set), and learn from this the pattern of variability of the shape of the class of objects for which the training set can be considered a representative sample. Modelling can considered as the process of modelling the distribution of the training points in shape space. In this paper we present Kernel principal component analysis (KPCA) based active shape models (ASM) for learning the intra –class deformation modes of an object. KPCA is the non-linear dimensionality reduction method. The comparison on performance and space of KPCA and principal component analysis (PCA) are shownKeywords: Object model, KPCA, PCA, ASM.Cite as: Rajkumari Bidyalakshmi Devi, Romesh Laishram, Y.J. Singh, “Modelling Objects Using KernelPrincipal Component Analysis†ADBU J.Engg.Tech.,2(1)(2015) 0021102(5pp

Face alignment using a three layer predictor

Face alignment is an important feature for most facial images related algorithms such as expression analysis, face recognition or detection etc. Also, some images lose information due to factors such as occlusion and lighting and it is important to obtain those lost features. This paper proposes an innovative method for automatic face alignment by utilizing deep learning. First, we use second order gaussian derivatives along with RBF-SVM and Adaboost to classify a first layer of landmark points. Next, we use branching based cascaded regression to obtain a second layer of points which is further used as input to a parallel and multi-scale CNN that gives us the complete output. Results showed the algorithm gave excellent results in comparison to state-of-the-art algorithms

Light Field Morphable Models

Statistical shape and texture appearance models are powerful image representations, but previously had been restricted to 2D or simple 3D shapes. In this paper we present a novel 3D morphable model based on image-based rendering techniques, which can represent complex lighting conditions, structures, and surfaces. We describe how to construct a manifold of the multi-view appearance of an object class using light fields and show how to match a 2D image of an object to a point on this manifold. In turn we use the reconstructed light field to render novel views of the object. Our technique overcomes the limitations of polygon based appearance models and uses light fields that are acquired in real-time

Sign-correlation partition based on global supervised descent method for face alignment

Face alignment is an essential task for facial performance capture and expression analysis. As a complex nonlinear problem in computer vision, face alignment across poses is still not studied well. Although the state-of-the-art Supervised Descent Method (SDM) has shown good performance, it learns conflict descent direction in the whole complex space due to various poses and expressions. Global SDM has been presented to deal with this case by domain partition in feature and shape PCA spaces for face tracking and pose estimation. However, it is not suitable for the face alignment problem due to unknown ground truth shapes. In this paper we propose a sign-correlation subspace method for the domain partition of global SDM. In our method only one reduced low dimensional subspace is enough for domain partition, thus adjusting the global SDM efficiently for face alignment. Unlike previous methods, we analyze the sign correlation between features and shapes, and project both of them into a mutual sign-correlation subspace. Each pair of projected shape and feature keep sign consistent in each dimension of the subspace, so that each hyperoctant holds the condition that one general descent exists. Then a set of general descent directions are learned from the samples in different hyperoctants. Our sign-correlation partition method is validated in the public face datasets, which includes a range of poses. It indicates that our methods can reveal their latent relationships to poses. The comparison with state-of-the-art methods for face alignment demonstrates that our method outperforms them especially in uncontrolled conditions with various poses, while keeping comparable speed

Sparse Modeling of Landmark and Texture Variability using the Orthomax Criterion

In the past decade, statistical shape modeling has been widely popularized in the medical image analysis community. Predominantly, principal component analysis (PCA) has been employed to model biological shape variability. Here, a reparameterization with orthogonal basis vectors is obtained such that the variance of the input data is maximized. This property drives models toward global shape deformations and has been highly successful in fitting shape models to new images. However, recent literature has indicated that this uncorrelated basis may be suboptimal for exploratory analyses and disease characterization. This paper explores the orthomax class of statistical methods for transforming variable loadings into a simple structure which is more easily interpreted by favoring sparsity. Further, we introduce these transformations into a particular framework traditionally based on PCA; the Active Appearance Models (AAMs). We note that the orthomax transformations are independent of domain dimensionality (2D/3D etc.) and spatial structure. Decompositions of both shape and texture models are carried out. Further, the issue of component ordering is treated by establishing a set of relevant criteria. Experimental results are given on chest radiographs, magnetic resonance images of the brain, and face images. Since pathologies are typically spatially localized, either with respect to shape or texture, we anticipate many medical applications where sparse parameterizations are preferable to the conventional global PCA approach

Adapted active appearance models

Active Appearance Models (AAM) are able to align known faces in an efficient manner when face pose and illumination are controlled. The AAM exploit a set of face examples in order to extract a statistical model. There is no difficulty to align a face with the same type (same morphology, illumination and pose) which constitute the example data set. Unfortunately, the AAM are less outstanding from the moment when the illumination, pose and face type changes. AAM robustness is link to the variability introduced in the learning base. The more the AAM will contain variability, the more it will be able to adapt itself to variable faces with the following drawback : the data represented in the reduced parameters space will then form different classes letting appear holes, regions without any data (see Fig. 1). It is therefore very difficult to make the AAM converge in this scattered space. We propose in this paper a robust Active Appearance Models allowing a real-time implementation. To increase the AAM robustness to illumination changes, we propose Oriented Map AAM (OM-AAM). Adapted AAM will be presented after to increase the AAM robustness to any other types of variability (in identity, pose, expression etc.)...Les Modèles Actifs d’Apparence (MAA) sont efficaces lorsqu’il s’agit d’aligner (détecter les contours des yeux, du nez et de la bouche) des visages connus dans des espaces contraints (illumination et pose contrôlées). Nous proposons des Modèles Actifs d’Apparence Adaptés afin d’aligner des visages inconnus dans des poses et illuminations quelconques. Notre proposition repose d’une part sur une transformation des textures du modèle actif en carte d’orientation, ce qui impacte l’opération de normalisation des MAA ; et d’autre part sur une recherche dans une banque de modèles pré-calculés du MAA le plus adapté au visage inconnu. Des tests sur des bases publiques et privées (BioId, CMU-PIE) montrent l’intérêt de notre approche: il devient possible d’aligner en temps réel des visages inconnus dans des situations où la lumière et la pose sont non contrôlées