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This paper presents a novel Robust Deep Appearance Models (RDAMs) approach to learn the non-linear correlation between shape and texture of face images. In this approach, two crucial components of face images, i.e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively. The RDBM, an alternative form of Robust Boltzmann Machines, can separate...
This paper proposes a novel ball tracking approach for coping with difficult situations as occlusion and fast object movement, in the context of collective sports. In particular, in the context of soccer, the ball cannot be represented by the features which are commonly utilised in the state of the art, because of the high distortion of the ball in case of fast movement, and considering the small...
Object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure reliability, a global object descriptor must be computed based on a unique and repeatable object reference frame. Moreover, the descriptor should contain enough information enabling...
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,...
Shape outliers can seriously affect the statistical analysis of the shape variations usually performed by the Principal Component Analysis PCA. This paper presents an algorithm for outliers detection and shape restoration as a new strategy for robust statistical shape analysis. The proposed framework is founded on an elastic metric in the shape space to cope with the nonlinear shape variability. The...
Detection and tracking of moving objects in video sequences are essential for many computer vision applications & it is considered as a challenging research issue due to dynamic changes in objects or background appearance, illumination, shape and occlusions. In this article, we proposed a robust algorithm to detect moving objects in a video, based on the combination of discrete cosine transform...
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...
Statistical shape models (SSMs) are widely used for introducing shape priors in medical image analysis. However, building a SSM usually requires careful data acquisitions to gather training datasets with both sufficient quality and enough shape variations. We present a robust framework to build reliable SSMs from a dataset with outliers and incomplete data. Our method is based on Point Distribution...
We present a robust framework for learning and fusing different modalities for rigid object tracking. Our method fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depths cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically,...
Humans usually describe objects along a certain direction, called intuitive direction, in other words place them in a way that they are commonly seen in their surroundings. In computer vision, the intuitive alignment may be very useful for object interpretation and semantic classification. For example, it may facilitate the extraction of characteristic points such as the base and apex of plant leaves,...
Finding correspondences between two 3D shapes is common both in computer vision and computer graphics. In this paper, we propose a general framework that shows how to build correspondences by utilizing the isometric property. We show that the problem of finding such correspondences can be reduced to the problem of spectral assignment, which can be solved by finding the principal eigenvector of the...
Active shape model statistically represents a shape by a set of well-defined landmark points and can model object variations using principal component analysis. However, the shape generated by standard active shape model is unsmooth when the test sample has a large variation compared with the training images. In this paper, we introduce a robust active shape model for facial feature location. First,...
In this paper an efficient method for 3D objects indexing and retrieval is presented. It is based on a set of six 2D views extracting using the projection box after a scale and pose normalization of the 3D models using PCA. Afterwards, we binarize each 2D view associated with the 3D object and we extract its external contour that represents its associated 2D shape. The Similarity among the 2D shapes...
This paper compares several approaches to extract facial landmarks and studies their influence on face recognition problems. In order to obtain fair comparisons, we use the same number of facial landmarks and the same type of descriptors (HOG descriptors) for each approach. The comparative results are obtained using FERET and FRGC datasets and show that better recognition rates are obtained when landmarks...
Lip segmentation is an important problem which is necessary to be solved in many applications, especially in audio-visual speech recognition. In this paper, a level-set based method that utilizes adaptive color distributions and shape priors for lip segmentation is introduced. More precisely, an implicit curve representation which learns the color information of lip and non-lip points and shape information...
We propose a robust registration method for range images under a rough estimate of illumination. Because reflectance properties are invariant to changes in illumination, they are promising to range image registration of objects lacking in discriminative geometric features under variable illumination. In our method, we use adaptive regions to model the local distribution of reflectance, which enables...
This paper deals with the problem of Constant False Alarm Rate (CFAR) detection of thermal anomalies in multispectral satellite data. The goal is to provide robustness to the algorithm proposed in [1], with respect to the presence of outliers in the analysis window. In [1], data from 4 ??m and 11 ??m MODIS bands, that are statistically correlated, are re-projected through a Principal Component Analysis...
This paper addresses issues on moving object tracking from videos. We propose a novel tracking scheme that jointly exploits local object features using consensus point correspondences, and global object appearance and shape models using adaptive particle filter-based eigen-tracking. The paper include the following main novelties: (a) employ consensus feature point correspondences to estimate the motion...
Real-time face alignment in video is very critical in many applications such as facial expression analysis, driver fatigue monitoring, etc. This paper presents a real time algorithm for face alignment in video that combines Active Shape Model (ASM) based face alignment and spatial-temporal continuity based tracking strategy. To guarantee the correctness of the tracked shape in each frame, a verification...
This paper proposes a new robust 3-D object blind watermarking method using constraints in the spectral domain. Mesh watermarking in spectral domain has the property of spreading the information in unpredictable ways, thus increasing the security of the watermark. In the proposed method, firstly, the Laplacian matrix of the graphical object mesh is eigen-decomposed. The coefficients corresponding...
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