3D imaging based on phase-shifting structured light is widely used in industrial measurement due to its non-contact nature. However, it typically requires a large number of additional images (multi-frequency heterodyne (M-FH) method) or introduces intensity features that compromise accuracy (space domain modulation phase-shifting (SDM-PS) method) for phase unwrapping, and it remains sensitive to motion. To overcome these issues, this article proposes a nonlinear phase coding-based stereo phase unwrapping (NPC-SPU) method that requires no additional patterns while maintaining measurement accuracy. In the encoding stage, a novel nonlinear distortion feature is introduced, while the signal-to-noise ratio of the phase codeword is preserved. In the decoding stage, a local phase unwrapping method that does not require additional auxiliary information is first proposed, closely associating the distortion information in the local wrapped phase. Then, a pre-calibrated stereo constraint system is used to filter potential matching phases, significantly reducing phase ambiguity and computational costs. Finally, to avoid the time-consuming and complex intensity kernel matching used in traditional methods, we propose a local phase correlation matching (LPCM) technique that enables lightweight and robust phase unwrapping. Experimental results demonstrate that this algorithm significantly enhances 3D reconstruction performance in scenarios with large depth, large disparity, complex colored structures, and dynamic scenes. Specifically, in dynamic environments (20mm/s), the proposed method achieves a lower measurement error rate (0.7829% vs. 6.4962%) with only 3 patterns, compared to the traditional three-frequency heterodyne (T-FH) method (using 9 patterns). Additionally, its measurement accuracy outperforms the advanced SDM-PS method, which also uses 3 patterns (0.1102 mm vs. 0.3232 mm).

This paper presents a technique for real-time texture-less object recognition and tracking on mobile devices. Our proposed algorithm is an even lighter-weight version of the recent state-of-the-art binary-based texture-less object detector BIND (Binary Integrated Net Descriptor), primarily customized for mobile device applications. This modification, termed BIND-Lite, employs various techniques to overcome the low-computational power of current mobile devices, while mostly retaining the texture-less object detection robustness of the original BIND. On current generation mobile devices, BIND-Lite was able to achieve runtime rates of up to 30 frames per second. To evaluate our algorithm, we have also designed a mobile augmented reality application coined IMPRINT, which renders logos/images onto detected objects to showcase BIND-Lite in a real-time mobile augmented reality setting.

A reversed indoor multitarget localization system employing compressive sensing (CS) theory is proposed for the first time in terms of visible light positioning (VLP). Unlike conventional VLP systems, where targets process the received light signals to localize themselves, our system works reversely by using multiple photodiodes (PDs) mounted on the ceiling to localize mobile targets that carry light emitting diodes. By utilizing its nature of sparsity, the problem of multitarget localization is formulated as a problem of sparse matrix reconstruction, and a 3-step workflow is developed to solve the problem. In this workflow, first, a sensing matrix is redesigned by using QR decomposition to enable CS theory. Next, the conventional l1-minimization (l1M) algorithm which is highly vulnerable to noise in solving a localization problem is theoretically analyzed and subsequently improved by adopting a reweighted l1M approach. Finally, a subgrid localization algorithm is proposed to overcome a common unpractical assumption of on-grid locations, tackle the false peak problem in sparse matrix reconstruction, and ultimately improve the localization precision. The feasibility of our system and supporting algorithms is verified through extensive simulations. Our system demonstrates a good positioning accuracy of 7.4 cm by using 25 PDs when SNR = 20 dB. We also investigate the impact of various factors on the positioning performance, and the obtained results provide an insightful reference paving the way to a practical system design.

This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. These lightweight nets encourage discriminative object description through their high-spatial resolution, enabling highly precise encoding of the objects edges and internal texture-less information. BIND achieved various invariant properties such as rotation, scale and edge-polarity through its unique binary logical-operated encoding and matching techniques, while performing remarkably well in occlusion and clutter. Apart from yielding efficient computational performance, BIND also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors such as BORDER, BOLD and LINE2D.

This paper presents an algorithm coined BORDER (Bounding Oriented-Rectangle Descriptors for Enclosed Regions) for texture-less object recognition. By fusing a regional object encompassment concept with descriptor-based pipelines, we extend local-patches into scalable object-sized oriented rectangles for optimal object information encapsulation with minimal outliers. We correspondingly introduce a modified line-segment detection technique termed Linelets to stabilize keypoint repeatability in homogenous conditions. In addition, a unique sampling technique facilitates the incorporation of robust angle primitives to produce discriminative rotation-invariant descriptors. BORDER's high competence in object recognition particularly excels in homogenous conditions obtaining superior detection rates in the presence of high-clutter, occlusion and scale-rotation changes when compared with modern state-of-the-art texture-less object detectors such as BOLD and LINE2D on public texture-less object databases.

The effectiveness of the renowned empirical mode decomposition (EMD) is affected by the mode-mixing problem (MMP) if a signal contains intermittent modes. The ensemble EMD (EEMD) and several modified and extended algorithms solve this problem by adding random white noises. However, the necessary large size of the ensemble and the inevitable manual intervention limits the application of EEMD. In this letter, a novel regenerated phase-shifted sinusoid-assisted EMD (RPSEMD) is proposed. Sinusoids with different scales are iteratively generated and added to cope with all possible MMPs in different intrinsic modes (IMs), where each sinusoid is designed adaptively and automatically. Furthermore, the sinusoids are shifted for better retaining the details of each IM and eliminating the added sinusoids. In the comparison experiments, the RPSEMD provides more reasonable results with less computation time.

Physically based deformable models have been a hot topic in the computer graphics and visualization community. However, most of the implemented models work only for isotropic materials, leaving the more general anisotropic materials less studied. We look into the challenging issues in dynamics simulation and real time visualization for anisotropic materials, and present our theoretic and practical work along the way to provide an ultimate solution to deformation with such materials. First, our models are based on continuum mechanics and approximated by the Finite Element Method, and different approaches such as co rotated, invertible, and total Lagrangian explicit dynamics. Secondly, for controls of the deformation, we have focused on materials that have their own internal structures (fibers) that determine the dynamics behaviors. We propose a fibers incorporated deformable model that can approximate the anisotropic elastic material properties. The orientation information is combined into the existing deformable models by element transformations along the given directions, which provide a control of the desired deformation. Thirdly, constraints for the strain density in linear elastic models are analyzed, and a positive-definite elasticity tensor is derived for an anisotropic material. Fourthly, an orthotropic deformation controlling frame-field is conceptualized and the frame construction tool is developed for the user to define the desired material properties. A quaternion Laplacian smoothing algorithm is designed for propagating the rotation minimization frames into the entire object. And finally, the co rotational linear FEM model is coupled with the orthotropic frame-field to realize a dynamics system, which can deal with large anisotropic deformations.

Prediction of the 3D structure of protein-protein complexes is crucial for understanding the biological system of life and thus for designing drugs. However it is a challenging problem due to the flexibility of the backbone and side-chains of the receptor and ligand. In this paper, we present a new framework for fast and flexible protein-protein docking based on geometric and physicochemical complementarity. In our approach, we segment the protein surfaces into several local surface patches and describe the effect of side-chain flexibility on each patch by sampling the space of conformation of its side-chains. Then the geometric complementarity can be determined by matching these surface patches. During the matching, we propose a new 3D shape descriptor, Spherical Harmonics Descriptor. The main property of the Spherical Harmonics Descriptor is rotation invariance which avoids taking an exhaustive set of rotations for each pair of patches. Thus, the geometric complementarity matching problem is simplified to a histogram matching problem. This can greatly reduce the computational cost. Finally, the docking candidates are evaluated by a scoring function which can accommodate geometric complementarity and physicochemical matching. The experimental results demonstrate the high efficiency and accuracy of the proposed method.

In this paper, we propose a feature-based registration algorithm for the confocal fluorescence images captured by an endomicroscopy system. We first extract a number of feature points from the endomicroscopy images by applying the binarization-thinning process and using the Rutovitz crossing number. These feature points are then post-processed to eliminate the spurious ones. After that we use the purified feature points to complete the image registration between every two consecutive slice images. The aligned image stack will be finally utilized to reconstruct and visualize the 3D structure of the living cell and tissue in real time, which provides the opportunity for the clinicians to diagnose various diseases including the early-stage cancers.

An explicit constraint is introduced into the Lucas-Kanade gradient based block motion model. This constraint helps the estimation process to consider surrounding motion vectors during its calculation. Consequently a better motion field can be produced than that by the original block motion model. Further, a discontinuity adaptive function is introduced into the Lucas-Kanade equation, which helps to preserve the discontinuities of motion fields