Shape Template Research Articles

Fabrication of TiO2 micro-structures by cathodic deposition

Fabrication of TiO2 micro-structures by combining photolithography with SU-8 photoresist and electrochemical cathodic deposition method was firstly demonstrated in this study. Diameter of the fabricated circular TiO2 micro-structures was ranged from 50 to 125μm, and surface morphology of the TiO2 was found to be porous. Shape of the micro-structures obtained is determined by shape of the SU-8 template prepared, therefore, it can be easily controlled by photolithography conditions. Morphology and crystal structures of the TiO2 micro-structures can be controlled by electrochemical parameters. Therefore, this result demonstrated the possibility to control properties of TiO2 micro-structures fabricated and the potential to improve performance of TiO2 micro-structures in applications.

Interface Effect on the Electropolymerized Polypyrrole Films with Hollow Micro/Nanohorn Arrays

Polypyrrole (PPy) films with hollow micro/nanohorn arrays were controllably synthesized in p-toluenesulfonate aqueous solutions by template-free electrochemical methods. The micelles which consist of pyrrole monomers and the surfactants provided the soft templates during the polymerization process. The polymerization potential and pH value of the solutions cooperatively influenced the shape of the micelles at the substrate/electrolyte interface and further controlled the morphologies of PPy films. PPy grew along the soft templates during the high potential periods of a pulse potentiostatic (PPS) method, while the pH value and the low potential were varied to modulate the shape of the soft templates. It has been shown to be most appropriate to fabricate hollow micro/nanohorn PPy films with the highest electrical conductivity (190 S cm(-1)) via PPS at pH ∼1.5. A diagram was also introduced in order to illustrate the polymerization potential and pH value dependence of nanohorn PPy morphologies. This work proposed a potential method to the in situ growth of conducting polymers with high conductivity and high specific surface area.

Animated Pose Templates for Modeling and Detecting Human Actions

This paper presents animated pose templates (APTs) for detecting short-term, long-term, and contextual actions from cluttered scenes in videos. Each pose template consists of two components: 1) a shape template with deformable parts represented in an And-node whose appearances are represented by the Histogram of Oriented Gradient (HOG) features, and 2) a motion template specifying the motion of the parts by the Histogram of Optical-Flows (HOF) features. A shape template may have more than one motion template represented by an Or-node. Therefore, each action is defined as a mixture (Or-node) of pose templates in an And-Or tree structure. While this pose template is suitable for detecting short-term action snippets in two to five frames, we extend it in two ways: 1) For long-term actions, we animate the pose templates by adding temporal constraints in a Hidden Markov Model (HMM), and 2) for contextual actions, we treat contextual objects as additional parts of the pose templates and add constraints that encode spatial correlations between parts. To train the model, we manually annotate part locations on several keyframes of each video and cluster them into pose templates using EM. This leaves the unknown parameters for our learning algorithm in two groups: 1) latent variables for the unannotated frames including pose-IDs and part locations, 2) model parameters shared by all training samples such as weights for HOG and HOF features, canonical part locations of each pose, coefficients penalizing pose-transition and part-deformation. To learn these parameters, we introduce a semi-supervised structural SVM algorithm that iterates between two steps: 1) learning (updating) model parameters using labeled data by solving a structural SVM optimization, and 2) imputing missing variables (i.e., detecting actions on unlabeled frames) with parameters learned from the previous step and progressively accepting high-score frames as newly labeled examples. This algorithm belongs to a family of optimization methods known as the Concave-Convex Procedure (CCCP) that converge to a local optimal solution. The inference algorithm consists of two components: 1) Detecting top candidates for the pose templates, and 2) computing the sequence of pose templates. Both are done by dynamic programming or, more precisely, beam search. In experiments, we demonstrate that this method is capable of discovering salient poses of actions as well as interactions with contextual objects. We test our method on several public action data sets and a challenging outdoor contextual action data set collected by ourselves. The results show that our model achieves comparable or better performance compared to state-of-the-art methods.

Shape-Matching Model Optimization Using Discrete-point Sampling and Feature Salience

The component classification and potential fault region locating in the full-automatic inspection system of a freight train require a computer vision method with the ability of classifying quickly and locating precisely, addressing anti-nonlinear deformations, and being able to perform extensible learning. Inspired by these requirements, this paper specifically optimizes the three elements of a shape-matching model, including the scene map, the shape template, and the matching. Our method uses a discrete-point sampling map (DPSM) as an intermediate representation, to enhance the stability of the scene maps, uses the criterion function based on feature salience to select a better shape-template group, and matches hand-sketches with regions in DPSMs to reduce the difficulty of the matching calculation. Based on our optimized shape-matching model, we set up a new procedure for component classifications and potential fault region locating in the full-automatic inspection system for freight trains, which has been applied successfully on more than 10 parts of freight train cars in the railway for more than 2 years. The results of anti-noise testing in laboratory and daily operation at several inspecting stations show that our method has a strong ability to survive with nonlinear deformations, and has a good extensibility to be used with different parts, which meet application demands for the full-automatic inspection system.

TemplateTagger v1.0.0: A template matching tool for jet substructure

TemplateTagger is a C++ package for jet substructure analysis with Template Overlap Method. The code operates with arbitrary models within fixed-order perturbation theory and arbitrary kinematics. Specialized template generation classes allow the user to implement any model for a decay of a boosted heavy object. In addition to Template Overlap, the code provides ability to calculate other template shape and energy flow observables. We describe in detail the structure of the package, as well as provide examples of its usage. Program summaryProgram title: Template Tagger v.1.0.0Catalogue identifier: AERZ_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERZ_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 112534No. of bytes in distributed program, including test data, etc.: 1812748Distribution format: tar.gzProgramming language: C++.Computer: PC/Mac.Operating system: Linux/Unix/Mac OS X.RAM: 50 MBClassification: 11.External routines: The code uses the PseudoJet library of the FastJet package (http://fastjet.fr/)Nature of problem:Traditional jet reconstruction techniques are inadequate to properly characterize boosted decays of heavy objects. High luminosity and pileup further complicate the matter as extra soft radiation is deposited within the large jet cones, thus both shifting and broadening the kinematic distributions boosted jets.Solution method:Template Overlap Method attempts to characterize the sub-structure of boosted jets by matching them to the underlying parton level decay configuration. Each boosted jet is analyzed with a large number of possible kinematically allowed structures called templates. The score of the best matching template is used to estimate the likelihood that the event is a heavy boosted jet of interest. The method is robust against pileup as the small template sub-cone size and the insensitivity of Template Overlap to energy depositions outside the template sub-cone limit the ability of pileup to affect the analysis.Restrictions:Depends on the problem one wishes to analyze.Running time:1–100 events per second, depending on the number of templates required for the analysis.

Mesoporous TiO2/WO3 hollow fibers with interior interconnected nanotubes for photocatalytic application

Mesoporous TiO2/WO3 hollow fibers with interconnected nanotubes were prepared by a sol–gel method using polysulfone hollow fibers as a framework template and a block copolymer as an internal template. Polysulfone fibers were immersed in a sol–gel solution and the oxides were deposited on the fibers by vacuum impregnation. The polysulfone and copolymer were removed by thermal treatment at 500 °C for 3 h to form the mesoporous hollow fibers. They preserved the shape and morphology of the polysulfone template after heat treatment. The structure and optical properties of the fibers were characterized, and the performance of the fibers for photocatalytic reaction of methylene blue was investigated. The mesoporous TiO2/WO3 hollow fibers possessed higher surface area (∼130 m2 g−1) and lower band gap energy than pure TiO2 fibers. They exhibited better photodegradation efficiency of methylene blue than TiO2 and WO3 fibers and P25 powder.

Segmentation of abdomen MR images using kernel graph cuts with shape priors

BackgroundAbdominal organs segmentation of magnetic resonance (MR) images is an important but challenging task in medical image processing. Especially for abdominal tissues or organs, such as liver and kidney, MR imaging is a very difficult task due to the fact that MR images are affected by intensity inhomogeneity, weak boundary, noise and the presence of similar objects close to each other.MethodIn this study, a novel method for tissue or organ segmentation in abdomen MR imaging is proposed; this method combines kernel graph cuts (KGC) with shape priors. First, the region growing algorithm and morphology operations are used to obtain the initial contour. Second, shape priors are obtained by training the shape templates, which were collected from different human subjects with kernel principle component analysis (KPCA) after the registration between all the shape templates and the initial contour. Finally, a new model is constructed by integrating the shape priors into the kernel graph cuts energy function. The entire process aims to obtain an accurate image segmentation.ResultsThe proposed segmentation method has been applied to abdominal organs MR images. The results showed that a satisfying segmentation without boundary leakage and segmentation incorrect can be obtained also in presence of similar tissues. Quantitative experiments were conducted for comparing the proposed segmentation with other three methods: DRLSE, initial erosion contour and KGC without shape priors. The comparison is based on two quantitative performance measurements: the probabilistic rand index (PRI) and the variation of information (VoI). The proposed method has the highest PRI value (0.9912, 0.9983 and 0.9980 for liver, right kidney and left kidney respectively) and the lowest VoI values (1.6193, 0.3205 and 0.3217 for liver, right kidney and left kidney respectively).ConclusionThe proposed method can overcome boundary leakage. Moreover it can segment liver and kidneys in abdominal MR images without segmentation errors due to the presence of similar tissues. The shape priors based on KPCA was integrated into fully automatic graph cuts algorithm (KGC) to make the segmentation algorithm become more robust and accurate. Furthermore, if a shelter is placed onto the target boundary, the proposed method can still obtain satisfying segmentation results.

Prussian blue hollow nanostructures: Sacrificial template synthesis and application in hydrogen peroxide sensing

A facile sacrificial template method was developed for the controlled synthesis of Prussian blue (PB) hollow nanostructures. The hollow nanostructures can either be hollow nanospheres or nanotubes by controlling the shape of the sacrificial template. The morphology, structure and composition of the as-prepared PB hollow nanostructures were characterized by transmission electron microscopy, scanning electron microscopy, powder X-ray diffraction, Fourier transform infrared spectroscopy and ultraviolet–visible absorption spectroscopy. The results showed that the PB hollow nanostructures had complementary shapes and sizes of the original sacrificial templates. In addition, PB hollow nanostructures were immobilized on the surface of glassy carbon electrode and applied to detect hydrogen peroxide. Cyclic voltammograms showed that the PB nanotubes kept their intrinsic electrochemical properties, and possessed prominent electrocatalytic activity toward the reduction of hydrogen peroxide. This approach can be extended to synthesize the other PB analogues for the applications in high-performance bioelectronic devices and biosensors.

Pore size effect on the formation of polymer nanotubular structures within nanoporous templates

Nanotubular structures have recently received much attention due to their potential applications in biosensors, drug delivery systems, electronic devices, and many others. The layer-by-layer (LbL) deposition technique on 3-dimensional templates has been one of the most popular methods for the formation of nanotubular structures. Any size, shape, and composition template can be utilized and the desired amount of various materials can be readily incorporated within the thin film geometry with nanoscale control. Therefore, the morphological features of those materials can easily be tuned by varying the structural properties of templates. However, LbL deposition within a confined geometry actually shows somewhat different results due to the geometrical restriction, which is still not fully understood so far. In the present study, in order to investigate such a template effect on the LbL process, we utilized a cylindrical nanoporous anodic aluminum oxide (AAO) structure as a template for the LbL process, yielding polymer nanotube structures. By varying the pore size of the porous templates, we determined the LbL process within porous structures was strongly governed by the geometrical characteristics of the utilized templates.

Direct surface nanopatterning using pyramidal silicon nanopore arrays as templates

Template-based surface nanopatterning techniques are highly efficient methods in realizing different surface nanopatterns, which are the fundamental structures of various nanodevices. We demonstrate a template of pyramidal silicon nanopore array (PSNA) for direct surface nanopatterning. Using the PSNA templates, deposition experiments of platinum (Pt) and gold-palladium (Au-Pd) particles on chromium-coated silicon substrates were performed. Individual Pt nanocubes and nanocube arrays with different shapes and feature sizes as small as 82 nm were obtained. By tuning the gap between the PSNA template and the substrate, Au-Pd microdot arrays with an average diameter of 7.6 μm were also fabricated. As the nanopore size and shape of the PSNA template can be easily controlled, the corresponding size and shape of the transferred surface nanopatterns are tunable. These results indicate the potential of the PSNA templates for large-scale production of nanopatterns with desired sizes and shapes.

AUTOMATIC PEDESTRIAN SEGMENTATION COMBINING SHAPE, PUZZLE AND APPEARANCE

In this paper, we address the problem of automatically segmenting non-rigid pedestrians in still images. Since this task is well known difficult for any type of model or cue alone, a novel approach utilizing shape, puzzle and appearance cues is presented. The major contribution of this approach lies in the combination of multiple cues to refine pedestrian segmentation successively, which has two characterizations: (1) a shape guided puzzle integration scheme, which extracts pedestrians via assembling puzzles with constraint of a shape template; (2) a pedestrian refinement scheme, which is fulfilled by optimizing an automatically generated trimap that encodes both human silhouette and skeleton. Qualitative and quantitative evaluations on several public datasets verify the approach's effectiveness to various articulated bodies, human appearance and partial occlusion, and that this approach is able to segment pedestrians more accurately than methods based only on appearance or shape cue.

Learning of grasp selection based on shape-templates

The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.

Effect of Sol Infiltration on Microstructures of Biomorphic TiO<sub>2</sub>

Biomorphic TiO2 was respectively prepared by two different titania sol infiltration routes. The morphology and microstructure of TiO2 samples were characterized by scanning electron microscopy. The results suggest that the final TiO2 poorly replicates the shape and microstructure of carbon template by repeating five infiltration procedures. By another repeated infiltration/pyrolysis route, the TiO2 better retains the shape and part microstructure of carbon template.

Direct growth of aligned graphitic nanoribbons from a DNA template by chemical vapour deposition

Graphene, laterally confined within narrow ribbons, exhibits a bandgap and is envisioned as a next-generation material for high-performance electronics. To take advantage of this phenomenon, there is a critical need to develop methodologies that result in graphene ribbons <10 nm in width. Here we report the use of metal salts infused within stretched DNA as catalysts to grow nanoscopic graphitic nanoribbons. The nanoribbons are termed graphitic as they have been determined to consist of regions of sp(2) and sp(3) character. The nanoscopic graphitic nanoribbons are micrometres in length, <10 nm in width, and take on the shape of the DNA template. The DNA strand is converted to a graphitic nanoribbon by utilizing chemical vapour deposition conditions. Depending on the growth conditions, metallic or semiconducting graphitic nanoribbons are formed. Improvements in the growth method have potential to lead to bottom-up synthesis of pristine single-layer graphene nanoribbons.

P 165. Transcranial electrical acceleration of motor skill learning: Direct comparison of different brain stimulation paradigms

Transcranial electrical stimulation (TES) has been shown to enhance motor learning. Several stimulation protocols are known to be effective, but have not yet been directly compared for practical motor learning. Electrode placement and stimulation parameters were varied on a realistic motor task to allow a direct comparison of their efficacy to enhance complex motor skill learning. A novel, complex, continuous motor task was developed for the study: the tracing task. Participants traced over a series of template words and shapes on a graphics tablet with their nondominant (left) hand in a manner similar to handwriting while performance was evaluated by calculating how close the trace was to the template. The experiment took place over three days in order to measure both online and offline learning effects. Electrode montages and stimulation parameters varied as follows: transcranial direct current stimulation (tDCS) with anodal stimulation over the contralateral motor cortex and cathodal stimulation over either the ipsilateral motor cortex or ipsilateral supraorbital area; high frequency (100–640 Hz) transcranial random noise stimulation (tRNS) over the supraorbital area and the contralateral motor cortex. These were compared with sham stimulation and tRNS of the supraorbital area and posterior temporal lobe (T6 of 10–20 system) as a reference active stimulation region. TES was applied for 20 min with a current density of 60 μ A/cm2 for tDCS and tRNS. TES of all types applied over the motor cortex improved motor skill acquisition over 2 days of training and a follow up compared to sham stimulation. In all groups, this effect was driven by large online effects within sessions, which developed faster in the tDCS groups compared to the tRNS group. In the active control experiment for region specificity (posterior temporal tRNS) tRNS was less effective and altered subcomponents of learning differently (offline stabilization). TES is effective at improving motor learning with motor cortex stimulation. tRNS is as effective as tDCS, and may be preferable as nonpolarised currents appear safer (demonstrated in deep brain stimulation) and do not have cathodal inhibition effects.

A shape analysis and template matching of building features by the Fourier transform method

Shape cognition and representation play an important role in spatial analysis because shape contains some characteristics of geographic phenomena that can be mined to discover hidden geographic principles. As a difficult cognition problem, the shape representation problem in GIS field has the properties of abstraction, indetermination and symbolization. How to use a model to represent shape cognition in our mental world and how to use a single number to compute the shape measure are interesting questions. In the image processing domain, there are many shape measure methods, but there are few proposals for corresponding vector data. This study aims to build a polygon shape measure and offers a Fourier transform-based method to compute the degree of shape similarity. The procedure first represents the boundary of the vector polygon shape as a periodic function, which is expanded in a Fourier descriptor series, and then, it obtains a set of coefficients that capture the shape information. Through the experiment on spatial shape match and shape query, the study shows that Fourier transform-based shape identification and template matching is consistent with human cognition.

Free-Viewpoint Video of Human Actors Using Multiple Handheld Kinects

We present an algorithm for creating free-viewpoint video of interacting humans using three handheld Kinect cameras. Our method reconstructs deforming surface geometry and temporal varying texture of humans through estimation of human poses and camera poses for every time step of the RGBZ video. Skeletal configurations and camera poses are found by solving a joint energy minimization problem, which optimizes the alignment of RGBZ data from all cameras, as well as the alignment of human shape templates to the Kinect data. The energy function is based on a combination of geometric correspondence finding, implicit scene segmentation, and correspondence finding using image features. Finally, texture recovery is achieved through jointly optimization on spatio-temporal RGB data using matrix completion. As opposed to previous methods, our algorithm succeeds on free-viewpoint video of human actors under general uncontrolled indoor scenes with potentially dynamic background, and it succeeds even if the cameras are moving.

User-centric incremental learning model of dynamic personal identification for mobile devices

This study presents a user-centric incremental learning model based on the proposed output selection strategy (OSS) and multiview body direction estimation for dynamic personal identification systems on mobile devices. First, the OSS filters primitive results generated from the classifier, so that the refined information can be used to update the learning model. Second, the robustness of the model is enhanced by using different views of faces as system input, which allows the learning model to adapt itself when either of facial views is not available. In addition, the body direction estimation method is proposed for estimating multiple views of a person by matching templates of human shapes and skin colors. An experiment on 168,000 test samples (20 classes with three facial views) is conducted to evaluate the proposed system. The experimental results show that the proposed method improves accuracy by more than 40 % compared to baseline, and correspondingly confirms the effectiveness of the proposed idea.

FOOD IMAGE ANALYSIS: SEGMENTATION, IDENTIFICATION AND WEIGHT ESTIMATION.

We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, we describe the image analysis tools to determine the regions where a particular food is located (image segmentation), identify the food type (feature classification) and estimate the weight of the food item (weight estimation). An image segmentation and classification system is proposed to improve the food segmentation and identification accuracy. We then estimate the weight of food to extract the nutrient content from a single image using a shape template for foods with regular shapes and area-based weight estimation for foods with irregular shapes.

Scalable preparation of three-dimensional porous structures of reduced graphene oxide/cellulose composites and their application in supercapacitors

Controlling the assembled structures of graphene has recently attracted enormous attention due to intriguing properties of the resultant structures. In this study, three-dimensional (3D) porous structures of reduced graphene oxide (RGO) with various ratios of RGO to cellulose have been fabricated by a scalable, but simple and efficient, approach that consists of ball milling assisted chemical reduction of GO, template shaping, coagulating, and lyophilization. The efficient mechanical shearing of ball milling and the hydrogen bond interactions between RGO and cellulose molecules contribute to the formation of a homogeneous RGO/cellulose hydrogel, improved thermal stability of the resultant composites, and enhanced crystallinity of the cellulose in the composites. The coagulation effect of cellulose maintains the RGO sheets in the 3D structures of cellulose; on the other hand, the RGO sheets facilitate the preservation of the 3D structures during freeze-drying, leading to the formation of 3D porous structures of RGO/cellulose composites. Benefiting from the continuous RGO network in the composites, the 3D porous structures of RGO(70)/cellulose(100) (GO:cellulose=70:100 in weight) show an electrical conductivity of 15.28Sm−1. Moreover, the 3D porous structures show potential application in supercapacitors due to the fact that they provide high specific surface area and fast charge propagation.