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In this paper we introduce a novel real-time method to track weakly textured planar objects and to simultaneously estimate their 3D pose. The basic idea is to adapt the classic tracking-by-detection approach, which seeks for the object to be tracked independently in each frame, for tracking non-textured objects. In order to robustly estimate the 3D pose of such objects in each frame, we have to tackle...
To accelerate the processing for integration, registration, representation and recognition of point clouds, it is of growing necessity to simplify the surface of 3-D models. This paper proposes a Retinex theory based points sampling method and the effectiveness of the sampling results are demonstrated by the registration. The points sampling considers both the local details and the overall shape of...
In this work we present a new method for compacting data from 3D shapes by extracting a 3D characteristic curve that we call a 3D signature. The 3D signature obtained preserves almost all the morphological shape information but drastically reduces the number of points required for representing the shape. Furthermore, based on the 3D signature, we present a 2D signature that draws a closed contour...
In this paper, a new method for the estimation of 3D human body poses from monocular images is proposed. Histograms of oriented gradients are used as the features for modeling human body poses. Human body poses are represented as 3D limb angles, which can remove the structure information from pose vector. Relevance Vector Machine is used to infer the mapping from image features to body poses. Experiments...
In this paper, a novel topological structured pattern, dubbed a spiral facet, is proposed and applied for the encoding and alignment of 3D facial triangular mesh surfaces. After describing the foundation of this representation, we present two direct face alignment methods, that exploit the ordered structure of the spiral facet in addressing the correspondence problem. We study the behavior and the...
A 3D face recognition method is proposed. For feature extraction, 3D point set is divided into number of slices to be projected on the central planes parallel to the slices. Feature vectors are formed by approximating best fitting polynomials for data on the planes. A multimodal approach in the score level and corresponding distance metrics for both unimodal and multimodal cases are presented.
This work addresses the localization of 11 prominent facial landmarks in 3D by fitting state of the art shape models to 2D data. Quantitative results are provided for 34 scan sat high resolution (texture maps of 10 M-pixels) in terms of accuracy (with respect to manual measurements) and precision(repeatability on different images from the same individual). We obtain an average accuracy of approximately...
Recognizing and localizing queried objects in range images plays an important role for robotic manipulation and navigation. Even though it has been steadily studied, it is still a challenging task for scenes with occlusion and clutter
This paper proposes a novel prediction scheme for depth map coding. We utilize the fact that depth values are largely dependent on objects and one small block consists of only a small number of objects at most. The proposed method approximates each block with one palette and one object shape map. The palette consists of two representative depth values for foreground object and background object in...
Effective robotic interaction with household objects requires the ability to recognize both object instances and object categories. The former are often characterized by locally discriminative texture cues (e.g., instances with prominent brand names and logos), and the latter by salient global shape properties (plates, bowls, pots). We describe experiments with both types of cues, combining a template-and-deformable-parts...
Object recognition and especially object class recognition is and will be a key capability in home robotics when robots have to tackle manipulation tasks and grasp new objects or just have to search for objects. The goal is to have a robot classify 'never before seen objects' at first occurrence in a single view in a fast and robust manner. The classification task can be seen as a matching problem,...
This paper addresses the problem of recognizing and reconstructing real-world objects in cluttered environments to enable service robot to grasp the objects and manipulate them. A novel approach to combine disparity segmentation method with the closed-loop color region based segmentation is presented. Disparity map segmentation leads to definition of object region of interest (ROI) enabling autonomous...
To discriminate more accurately between dyslexic and normal brains, we detect the brain cortex variability through a spherical harmonic analysis that represents a 3D surface supported by the unit sphere, having a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D brain cortex segmentation, with a deformable...
We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. It produces more compact and discriminative descriptors than shape context. It robust...
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum A Posteriori (MAP)...
Accurate image segmentation is important for many medical imaging applications, whereas it remains challenging due to the complexity in medical images, such as the complex shapes and varied neighbor structures. This paper proposes a new hierarchical 3D image segmentation method based on patient-specific shape prior and surface patch shape statistics (SURPASS) model. In the segmentation process, a...
We present a system for virtual mirror experience that performs attentive facial geometric alterations in augmented reality. The virtual mirror is simulated using commonly available PC with webcam that capture, process and display video in real-time. High realism is obtained by considerate 3D-aware warping of the 2D captured video. A Kalman-based real-time face tracker is used for 3D head pose estimation...
We propose a method to perform automatic segmentation of 3D scenes based on a standard classifier, whose learning model is continuously improved by means of new samples, and a grouping stage, that enforces local consistency among classified labels. The new samples are automatically delivered to the system by a feedback loop based on a feature selection approach that exploits the outcome of the grouping...
This paper describes a human shape reconstruction method from multiple cameras in daily living environment, which leads to robust markerless motion capture. Due to continual illumination changes in daily space, it had been difficult to get human shape by background subtraction methods. Recent statistical foreground segmentation techniques based on graph-cuts, which combine background subtraction information...
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