A novel localization and orientation method for an objective embedded with a rectangle magnet

To wirelessly obtain the accurate location and orientation of an objective and exert an appropriate guidance for the objective, a feasible approach is to enclose a small rectangular permanent mag- net in the objective. The magnetic field, produced by the rectangular magnet can be detected by magnetic sensors outside the objective. With these sensor data, the 3D localization and 3D orienta- tion parameters can be computed based on the mathematic model of the rectangular magnet magnetic field. In this 6D localization and orientation system, we first obtain 5D parameters of the objective by dipole model, then based on these parameters we can obtain 6D parameters by the model of rectangular magnet magnetic field using the particle swarm optimization （PSO） algorithm. Simulation experiments show that the proposed approach achieves ~ood performance.

Geometric phase helical PSF for simultaneous orientation and 3D localization microscopy

The 3D location and dipole orientation of light emitters provide essential information in many biological,chemical,and physical systems.Simultaneous acquisition of both information types typically requires pupil engineering for 3D localization and dual-channel polarization splitting for orientation deduction.Here we report a geometric phase helical point spread function for simultaneously estimating the 3D position and dipole orientation of point emitters.It has a compact and simpler optical configuration compared to polarization-splitting techniques and yields achromatic phase modulation in contrast to pupil engineering based on dynamic phase,showing great potential for single-molecule orientation and localization microscopy.

Single frame super-resolution reconstruction based on sparse representation

In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.

STUDY ON DAMAGE BIFURCATION AND INSTABILITY OF ROCK-LIKE MATERIALS

The critical bifurcation orientation and its corresponding hardening modulus for rock-like geomaterials are derived by considering the effect of stiffness degradation and volumetric dilatancy under the assumption of isotropic damage. The dependency of the localized orientation on the degree of damage and initial Poisson＇s ratio of rock is examined and the bifurcation behavior of the uniaxial compression sample under the plane-stress condition is compared with that under plane-strain condition. It is shown that the localization orientation angle intimately depends on both the initial Poisson＇s ratio and degree of damage for the rock sample under the uniaxial compression condition. As the initial Poisson＇s ratio or degree of damage increases, the orientation angle of the plane on which localization tends to be initiated gets to decrease. At the same time, the localization orientation angle of a rock sample under the plane-stress condition is larger than that under the plane-strain condition.

Fingerprint Matching Based on Local Relative Orientation Field

A fingerprint matching method based on local relative orientation field is proposed. It extracts local relative orientation field around each minutia for minutiae matching. Local orientation features are also used to sorting minutiae in order to speed up searching a minutia when pairing minutiae. The experimental result reveals that this method achieves improved recognition accuracy. Key words fingerprint matching - ridge-based minutiae matching - local relative orientation field - sorting minutiae CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: ZHU En (1976-), male, Ph. D candidate, research direction: pattern recognition, image processing and information security.

3D face registration based on principal axis analysis and labeled regions orientation

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the multi-pose models to the standard frontal model＇s reference frame using the principal axis analysis algorithm.Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics.These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration.Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

An invariant interest point detector under image affine transformation

For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.

3D object retrieval based on histogram of local orientation using one-shot score support vector machine

In this paper, a content based descriptor is pro- posed to retrieve 3D models, which employs histogram of local orientation （HLO） as a geometric property of the shape. The proposed 3D model descriptor scheme consists of three steps. In the first step, Poisson equation is utilized to define a 3D model signature. Next, the local orientation is calculated for each voxel of the model using Hessian matrix. As the final step, a histogram-based 3D model descriptor is extracted by accumulating the values of the local orientation in bins. Due to efficiency of Poisson equation in describing the models with various structures, the proposed descriptor is capable of discriminating these models accurately. Since, the inner vox- els have a dominant contribution in the formation of the de- scriptor, sufficient robustness against noise can be achieved. This is because the noise mostly influences the boundary vox- els. Furthermore, we improve the retrieval performance us- ing support vector machine based one-shot score （SVM-OSS） similarity measure, which is more efficient than the conven- tional methods to compute the distance of feature vectors. The rotation normalization is performed employing the prin- cipal component analysis. To demonstrate the applicability of HLO, we implement experimental evaluations of precision- recall curve on ESB, PSB and WM-SHREC databases of 3D models. Experimental results validate the effectiveness of the proposed descriptor compared to some current methods.

Dipole and charge effects of chloroaluminum phthalocyanine revealed by local work function measurements at sub-molecular level

Work function plays a significant role in surface chemistry. Local work function provides the information of local d/pole-d/pole interaction and charge distribution between adsorbates and substrate, highlighting the local charge effect of the targeted spot which is normally smeared out in conventional average work function measurements. Chloroaluminum phthalocyanine （CIA1Pc）, an important optoelectronic molecule with a permanent dipole moment pointing from the Pc ring to the ending CI atom, adsorbed on Au（111） in either Cl-up or Cl-down configuration. Scanning tunneling microscopy/spectroscopy measurements revealed that at the centers of Cl-up and CI-down molecules, the local work functions changed oppositely with respect to the Au（111） substrate. At their Pc lobes, however, the local work functions unanimously increased due to charging effect of the indole lobes in the CIAIPc molecule.