On orientation and anisotropy estimation for online fingerprint authentication

Analysis of the Approaches to 3-D Reconstruction of Porous Media

In this paper, we propose a new operator for texture orientation estimation. We focus on directional textures which can have more than a single orientation at the same point. Our operator consists in a steerable network of parallel lines along which a homogeneity feature is computed in the spatial domain. The analysis of the network response along each direction allows us to set up the presence of single or multiple orientations. In order to reduce the computing cost, we propose a recursive implementation of our operator, thanks to the rotations of the image instead of the rotation of the network. Our operator works on a small support, and thus provides a local estimation of the orientations. Results obtained both with synthetic textures and natural images are accurate and show the selectivity and the isotropic behaviour of our operator.

A unified differential geometric framework for estimation of local surface shape and orientation from projective distortion is proposed, based on a differential version of the stationarity assumption introduced by Malik and Rosenholtz. This framework allows the information content of the gradient of any descriptor defined in a local coordinate frame to be characterized in a very compact form. The analysis encompasses both full affine descriptors and the classical texture gradients. For estimation of local surface orientation and curvature from uncertain observations of affine distortion, the proposed framework allows the dimensionality of the search space to be reduced from five to one. >

Multifiber pathway reconstruction using bundle constrained streamline

This paper presents a novel orientation estimate approach named inertial guided visual sample consensus (IGVSAC). This method is intentionally designed for capturing the orientation of human body joints in free-living environments. Unlike the traditional visual-based orientation estimation methods, where outliers among putative image-pair correspondences are removed based on hypothesize-and-verify models such as the computationally costly RANSAC, our approach novelly exploits prior motion information (i.e., rotation and translation) deduced from the quick-response inertial measurement unit (IMU) as the initial body pose to assist camera in removing hidden outliers. In addition, our IGVSAC algorithm is able to ensure estimation accuracy even in the presence of a large quantity of outliers, thanks to its capability of rejecting apparent mismatches. The estimated orientation from the visual sensor is, in turn, able to correct long-term IMU drifts. We conducted extensive experiments to verify the effectiveness and robustness of our IGVSAC algorithm. Comparisons with highly accurate VICON and OptiTrack Motion Tracking Systems prove that our orientation estimate system is quite suitable for capturing human body joints.

White matter fiber tractography plays a key role in the in vivo understanding of brain circuitry. For tract-based comparison of a population of images, a common approach is to first generate an atlas by averaging, after spatial normalization, all images in the population, and then perform tractography using the constructed atlas. The reconstructed fiber trajectories form a common geometry onto which diffusion properties of each individual subject can be projected based on the corresponding locations in the subject native space. However, in the case of high angular resolution diffusion imaging (HARDI), where modeling fiber crossings is an important goal, the above-mentioned averaging method for generating an atlas results in significant error in the estimation of local fiber orientations and causes a major loss of fiber crossings. These limitatitons have significant impact on the accuracy of the reconstructed fiber trajectories and jeopardize subsequent tract-based analysis. As a remedy, we present in this paper a more effective means of performing tractography at a population level. Our method entails determining a bipolar Watson distribution at each voxel location based on information given by all images in the population, giving us not only the local principal orientations of the fiber pathways, but also confidence levels of how reliable these orientations are across subjects. The distribution field is then fed as an input to a probabilistic tractography framework for reconstructing a set of fiber trajectories that are consistent across all images in the population. We observe that the proposed method, called PopTract, results in significantly better preservation of fiber crossings, and hence yields better trajectory reconstruction in the atlas space.

Motivation: Traditional tractography methods rely on complex physical models and tracking algorithms, making them computationally intensive and sensitive to data quality. Machine learning-based methods have gained interest but they often fail to estimate local fiber orientations accurately. Goal(s): We propose a novel tractography method using a deep diffusion model to enhance fiber orientation estimation accuracy while improving computational efficiency. Approach: Our method contains convolutional networks to capture local spatial features, recurrent networks to model high-order dependencies along the streamline points, and a diffusion model to estimate the local fiber orientation. Results: We demonstrate our method's superior performance on phantom and in-vivo datasets. Impact: The proposed diffusion model-based method, integrating local neighborhood information and sequential dependencies, directly achieves local fiber orientation estimation and tractography. Tested on in silico and in vivo datasets, we demonstrate competitive performance against current methods.

Curvature consistency improves local shading analysis

Current denoising methods for diffusion weighted images can obtain high quality estimates of local fiber orientation in static structures. However, recovering reliable fiber orientation from in vivo data is considerably more difficult. To address this problem we use a geometric approach, with a spatio-temporal Cartan frame field to model spatial (within time-frame) and temporal (between time-frame) rotations within a single consistent mathematical framework. The key idea is to calculate the Cartan structural connection parameters, and then fit probability distributions to these volumetric scalar fields. Voxels with low log-likelihood with respect to these distributions signal geometrical “noise” or outliers. With experiments on both simulated (canine) moving fiber data and on an in vivo human heart sequence, we demonstrate the promise of this approach for outlier detection and denoising via inpainting.

This study investigates local orientation-based approaches to the complex problem of pattern segmentation in three-dimensional (3D) texture image. The current problem focuses on the extraction of so-called lamellar colonies in titanium alloy, which, from the materials science and engineering point of view, are microstructural features that play a fundamental role on crack propagation and bifurcation during mechanical loading. Methods based on local orientation estimation extend the notion of using local gradient to reveal variation of semi-planar pattern orientation in the 3D image. The study introduces a computational approach that accelerates the calculation of the eigenvectors from the local matrices of inertia of all voxels composing the 3D image. Then different paths are proposed to segment colonies or inter-colony boundaries, i.e. polar orientation map and minimum scalar product map, in order to delimitate regions of similar orientations. The investigated segmentation methods have been compared with other methods that are mainly based on the popular solution of filter banks. Tests, which have been performed on both synthetic and real 3D images, show that the proposed local orientation-based methods better delineate object boundaries than the counterparts.

Multiple Model Adaptive Complementary Filter for Attitude Estimation

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods.

This paper proposes new estimators for the local orientation of elongated structures. Classical methods derive local orientation from the argument of gradient estimator. As they were originally designed for edge detection, such operators can lead to high bias in orientation estimation. We introduce the line orientation operator which applies an oriented model made with straight parallel lines on the local neighborhood. We present results either on synthetic and real images. We compare this new operator with other classical orientation operators.

Inspired by the early visual system of many mammalians we consider the construction of-and reconstruction from- an orientation score $${\it U_f}:\mathbb{R}^2 \times S^{1} \to \mathbb{C}$$ as a local orientation representation of an image, $$f:\mathbb{R}^2 \to \mathbb{R}$$ . The mapping $$f\mapsto {\it U_f}$$ is a wavelet transform $$\mathcal{W}_{\psi}$$ corresponding to a reducible representation of the Euclidean motion group onto $$\mathbb{L}_{2}(\mathbb{R}^2)$$ and oriented wavelet $$\psi \in \mathbb{L}_{2}(\mathbb{R}^2)$$ . This wavelet transform is a special case of a recently developed generalization of the standard wavelet theory and has the practical advantage over the usual wavelet approaches in image analysis (constructed by irreducible representations of the similitude group) that it allows a stable reconstruction from one (single scale) orientation score. Since our wavelet transform is a unitary mapping with stable inverse, we directly relate operations on orientation scores to operations on images in a robust manner. Furthermore, by geometrical examination of the Euclidean motion group $$G=\mathbb{R}^2 \mathbb{R}\times \mathbb{T}$$ , which is the domain of our orientation scores, we deduce that an operator ? on orientation scores must be left invariant to ensure that the corresponding operator $$\mathcal{W}_{\psi}^{-1}\Phi \mathcal{W}_{\psi}$$ on images is Euclidean invariant. As an example we consider all linear second order left invariant evolutions on orientation scores corresponding to stochastic processes on G. As an application we detect elongated structures in (medical) images and automatically close the gaps between them. Finally, we consider robust orientation estimates by means of channel representations, where we combine robust orientation estimation and learning of wavelets resulting in an auto-associative processing of orientation features. Here linear averaging of the channel representation is equivalent to robust orientation estimation and an adaptation of the wavelet to the statistics of the considered image class leads to an auto-associative behavior of the system.

The problem of inferring 3D orientation of a camera from video sequences has been mostly addressed by first computing correspondences of image features. This intermediate step is now seen as the main bottleneck of those approaches. In this paper, we propose a new 3D orientation estimation method for urban (indoor and outdoor) environments, which avoids correspondences between frames. The scene property exploited by our method is that many edges are oriented along three orthogonal directions; this is the recently introduced Manhattan world (MW) assumption. The main contributions of this paper are: the definition of equivalence classes of equiprojective orientations, the introduction of a new small rotation model, formalizing the fact that the camera moves smoothly, and the decoupling of elevation and twist angle estimation from that of the compass angle. We build a probabilistic sequential orientation estimation method, based on an MW likelihood model, with the above-listed contributions allowing a drastic reduction of the search space for each orientation estimate. We demonstrate the performance of our method using real video sequences.