Alignment Of Objects Research Articles

GLTScene: Global‐to‐Local Transformers for Indoor Scene Synthesis with General Room Boundaries

AbstractWe present GLTScene, a novel data‐driven method for high‐quality furniture layout synthesis with general room boundaries as conditions. This task is challenging since the existing indoor scene datasets do not cover the variety of general room boundaries. We incorporate the interior design principles with learning techniques and adopt a global‐to‐local strategy for this task. Globally, we learn the placement of furniture objects from the datasets without considering their alignment. Locally, we learn the alignment of furniture objects relative to their nearest walls, according to the alignment principle in interior design. The global placement and local alignment of furniture objects are achieved by two transformers respectively. We compare our method with several baselines in the task of furniture layout synthesis with general room boundaries as conditions. Our method outperforms these baselines both quantitatively and qualitatively. We also demonstrate that our method can achieve other conditional layout synthesis tasks, including object‐level conditional generation and attribute‐level conditional generation. The code is publicly available at https://github.com/WWalter-Lee/GLTScene.

6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments.

Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (tz) and the camera's focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, tz is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed tz. In the second stage, we predict the true value of tz while scaling the focal length based on the tz update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method.

Single-cavity loss-enabled nanometrology.

Optical monitoring of the position and alignment of objects with a precision of only a few nanometres is key in applications such as smart manufacturing and force sensing. Traditional optical nanometrology requires precise nanostructure fabrication, multibeam interference or complex postprocessing algorithms, sometimes hampering wider adoption of this technology. Here we show a simplified, yet robust, approach to achieve nanometric metrology down to 2 nm resolution that eliminates the need for any reference signal for interferometric measurements. We insert an erbium-doped quartz crystal absorber into a single Fabry-Pérot cavity with a length of 3 cm and then induce exceptional points by matching the optical loss with the intercavity coupling. We experimentally achieve a displacement response enhancement of 86 times compared with lossless methods, and theoretically argue that an enhancement of over 450 times, corresponding to subnanometre resolution, may be achievable. We also show a fivefold enhancement in the signal-to-noise ratio, thus demonstrating that non-Hermitian sensors can lead to improved performances over the Hermitian counterpart.

Object alignment in spatially multiplexed holograms applied to polarization sensing.

The paper presents a new algorithm for object alignment in digital holography with multiple spherical reference waves. The algorithm was applied to polarization sensing with two orthogonally polarized reference waves. A novel holographic setup was built using a diffraction grating to generate two symmetric reference waves, keeping the setup simple and highly adjustable. The angles of rotation of the polarizing elements were calculated with mean absolute errors of 0.71° and 2.96° based on intensity and phase measurements, respectively. The same algorithm can be applied to any digital holographic measurement with multiple spherical reference waves.

Nanoalignment by critical Casimir torques

The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects.

Trans‐gender things: Objects and the materiality of trans‐femininity in Ming‐Qing China

AbstractThis article demonstrates that objects, more specifically the trans usage of objects that disrupted and rearticulated the normative alignment of objects, sexed bodies and gender embodiments, served a formative role in helping male‐assigned individuals to cross gender boundaries and achieve trans‐femininity in Ming‐Qing China. The examined objects include the foot‐binding cloth for the feminine bodily image of bound feet, the embroidery needle for ‘womanly work’ and concealing underwear for feminine, penetrated sexual acts. This object‐oriented heuristic offers a new culturally specific approach to trans history beyond identarian frameworks and foregrounds the material multiplicity of trans formations and embodiments in Ming‐Qing China.

Real-Time 3D Object Detection on Crowded Pedestrians.

In the field of autonomous driving, object detection under point clouds is indispensable for environmental perception. In order to achieve the goal of reducing blind spots in perception, many autonomous driving schemes have added low-cost blind-filling LiDAR on the side of the vehicle. Unlike point cloud target detection based on high-performance LiDAR, the blind-filling LiDARs have low vertical angular resolution and are mounted on the side of the vehicle, resulting in easily mixed point clouds of pedestrian targets in close proximity to each other. These characteristics are harmful for target detection. Currently, many research works focus on target detection under high-density LiDAR. These methods cannot effectively deal with the high sparsity of the point clouds, and the recall and detection accuracy of crowded pedestrian targets tend to be low. To overcome these problems, we propose a real-time detection model for crowded pedestrian targets, namely RTCP. To improve computational efficiency, we utilize an attention-based point sampling method to reduce the redundancy of the point clouds, then we obtain new feature tensors by the quantization of the point cloud space and neighborhood fusion in polar coordinates. In order to make it easier for the model to focus on the center position of the target, we propose an object alignment attention module (OAA) for position alignment, and we utilize an additional branch of the targets' location occupied heatmap to guide the training of the OAA module. These methods improve the model's robustness against the occlusion of crowded pedestrian targets. Finally, we evaluate the detector on KITTI, JRDB, and our own blind-filling LiDAR dataset, and our algorithm achieved the best trade-off of detection accuracy against runtime efficiency.

Boosting Few-Shot Fine-Grained Recognition With Background Suppression and Foreground Alignment

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. Undoubtedly, this task inherits the main challenges from both few-shot learning and fine-grained recognition. First, the lack of labeled samples makes the learned model easy to overfit. Second, it also suffers from high intra-class variance and low inter-class differences in the datasets. To address this challenging task, we propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local-to-local (L2L) similarity metric. Specifically, the BAS is introduced to generate a foreground mask for localization to weaken background disturbance and enhance dominative foreground objects. The FOA then reconstructs the feature map of each support sample according to its correction to the query ones, which addresses the problem of misalignment between support-query image pairs. To enable the proposed method to have the ability to capture subtle differences in confused samples, we present a novel L2L similarity metric to further measure the local similarity between a pair of aligned spatial features in the embedding space. What's more, considering that background interference brings poor robustness, we infer the pairwise similarity of feature maps using both the raw image and the refined image. Extensive experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state of the art by a large margin. The source codes are available at: https://github.com/CSer-Tang-hao/BSFA-FSFG.

Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration

Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, the accuracy and robustness of these methods are limited. To overcome these challenges, this paper proposes a rotation-invariant estimation method for high-precision geo-registration in AR maps. The method introduces several innovations. Firstly, it improves the accuracy of generating heading data from low-cost hardware by utilizing Real-Time Kinematic GPS and visual-inertial fusion. This improvement contributes to the increased stability and precise alignment of virtual objects in complex environments. Secondly, a fusion method combines the true-north direction vector and the gravity vector to eliminate alignment errors between geospatial data and the ground surface. Lastly, the proposed method dynamically combines the initial attitude relative to the geographic north direction with the motion-estimated attitude using visual-inertial fusion. This approach significantly reduces the requirements on sensor hardware quality and calibration accuracy, making it applicable to various AR precision systems such as smartphones and augmented reality glasses. The experimental results show that this method achieves AR geo-registration accuracy at the 0.1-degree level, which is about twice as high as traditional AR geo-registration methods. Additionally, it exhibits better robustness for AR applications in complex scenarios.

MENILIK PERSPEKTIF POSITIF NON-MUSLIM TERHADAP URGENSI BAHASA ARAB SEBAGAI UPAYA MENAMPIK STIGMA SAKRAL DALAM MASYARAKAT UMUM DI INDONESIA

Arabic, which is closely known as part of Islam because it was chosen directly by God as the language of the Qur'an and Hadith, now seems to be an exclusive language to learn. Since long ago, the views of the Indonesian people, especially non-Muslims, towards Arabic were only limited to religious languages without seeing the urgency of the Arabic language itself. Therefore, this study wants to reveal the positive views of non-Muslims towards Arabic because researchers believe that not all non-Muslim Indonesians think that way. This is done to open the views of the sacredness of the Indonesian people towards Arabic. This study uses a mixed methods approach between qualitative and quantitative. The instrument used was the Forum Group Discussion (FGD) with each representative from a non-Islamic religion in Indonesia and as supporting data, the researcher conducted reference searches in journal articles and the like. As an introduction, the researcher reveals that there is an alignment or de-sacralization of Arabic objects. Then the researcher found the influence of Arabic on non-Muslim objects of worship and its influence on the international scene. In addition, the practical and pragmatic orientation of non-Muslims towards learning Arabic and the opportunity to make it part of the National curriculum can also have an impact on the process of open-mindedness in society, which until now still consecrates the Arabic language with parallelism or de-sacralization of Arabic objects.

Abstract 5327: Automated organoid alignment for clonal response characterization in pancreatic ductal adenocarcinoma

Abstract Background: Therapeutic screening Pancreatic Ductal Adenocarcinoma (PDAC) relies on well-level assessment for high throughput response evaluation. Patient-derived cancer organoids (PCOs) model subclonal populations, however the significance of resistant populations is uncertain when characterized with well-level response. Using a high-throughput screening assay, we present an automated alignment algorithm to characterize populations of organoid growth as compared to validated well-level therapeutic response assays. Methods: High content imaging was performed in low volume (10uL), 96-well angiogenesis plating format (Ibidi, Inc) at 4x objective with 5-frame Z-stack (600um) with brightfield imaging captured at 0h and 72h. Images underwent processing using Gen5 suite (Biotek, Inc) including Z-projection to render organoids into single two-dimensional planes. Baseline objects were defined between 50µm and 750µm, and filtration based on circularity defined as >0.4. Object alignment was performed based on root-mean-square-deviation (RMSD) between all combinations of objects to optimize match determination. This analysis was performed in drug screen of 80 independent agents in early clinical trials in combination with CDK7 inhibitor, SY-5609. Well level viability was performed using standardized 3D CellTiterGlo (CTG, Promega Inc.) (33% v/v). Response was assessed using descriptive statistics, effect size (Glass’s Δ), and Therapeutic Sensitivity Index (TSI) defined as the weighted average between elements with growth from media control versus treated population. Results: Z projection of 600um in Low-volume plating (10µL) of matrix suspension yielded 1.68 organoids per µL relative to the traditional hanging drop design (50µL) 0.75 organoids per µL (p<0.005). Organoid alignment across the continuum of RMSD yielded maximum successful matches at 75µm with 66.1% of objects versus 6.7% based on randomly assigned objects across validation experimental sets (n=1380). An optimal circularity value was determined at 0.4; an increase of circularity by 0.1 yielded a >5% reduction in of matched objects, while a decrease in circularity by 0.1 yielded a <5% reduction in unmatched objects. A poor correlation was seen for the percent of growing organoids within a population and well level normalized viability via CTG (R = 0.37). The normalized CTG value had improved in correlation when compared to effect size relative to control (R = 0.54) and TSI (R = 0.56). Conclusion: We provide a method for high fidelity alignment of PCOs in low-volume format for matrix-based screening applications. These techniques can be adapted to existing staining protocols to characterize subclonal response in the context of both molecular heterogeneity and clinical outcomes. Citation Format: Ethan Samuel Lin, Md Shahadat Hossan, Austin Stram, Eleanor E. Riedl, Luke J. Koeppel, Jaimie M. Warner, Jeremy D. Kratz. Automated organoid alignment for clonal response characterization in pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5327.

BTDM: A Bi-Directional Translating Decoding Model-Based Relational Triple Extraction

The goal of relational triple extraction is to extract knowledge-rich relational triples from unstructured text. Although the previous methods obtain considerable performance, there are still some problems, such as error propagation, the overlapping triple problem, and suboptimal subject–object alignment. To address the shortcomings above, in this paper, we decompose this task into three subtasks from a fresh perspective: entity extraction, subject–object alignment and relation judgement, as well as propose a novel bi-directional translating decoding model (BTDM). Specifically, a bidirectional translating decoding structure is designed to perform entity extraction and subject–object alignment, which decodes entity pairs from both forward and backward extraction. The bidirectional structure effectively mitigates the error propagation problem and aligns the subject–object pairs. The translating decoding approach handles the overlapping triple problem. Finally, a (entity pair, relation) bipartite graph is designed to achieve practical relationship judgement. Experiments show that our model outperforms previous methods and achieves state-of-the-art performance on NYT and WebNLG. We achieved F1-scores of 92.7% and 93.8% on the two datasets. Meanwhile, in various complementary experiments on complex scenarios, our model demonstrates consistent performance gain in various complex scenarios.

Digitally designed casting ring and sprue system - a contemporary approach to the casting process in dentistry

The current paper aims to present a new casting approach in dentistry by eliminating some conventional preliminary procedures and digitalising the process as much as possible.The experiment uses six digital crown patterns. They are aligned with each other and to the wall of a virtual casting ring. Then a digital sprue system is created. The object alignment and sizes are revised, and the optimal correlation is set. Finally, a single virtual object is created and is 3D printed of light curing acrylic resin suitable for casting, invested, and cast.The castings that are achieved have good quality and no defects. At the same time, the virtual designing and aligning of the patterns and all the components of the sprue system and casting ring save much time and allows better precision to be achieved. All these facts allow better control of the casting process results, making the process faster, more predictable, and more accurate.The limitations of this study concerns pattern selection. It is well-known that the acrylic resin sublimates and expands massively during a temperature rise, which may cause mould fracture. This obstacle is very representative as solid objects with higher volumes are examined. This may enforce alignment revision or even temperature rate modification.Suggested approach will be very useful for daily dental laboratory practice by optimizing the working time of the dental technician, making the denture production process faster, more predictable, and more accurate. As a result, the process optimizes the working process in dental offices, by allowing shortening the time of each visit and treatment plan terms.Presented approach reveals some new opportunities for dental technicians to substitute the conventional raw stages of sprue system fabrication with digital planning and designing. This allows dental technicians to take advantage of the full potential of digital technologies.

Mecanismos de controle gerencial, imprevisibilidade ambiental e resiliência organizacional

Abstract This article analyzes the (formal and informal) role of control mechanisms in fostering (proactive and reactive) organizational resilience of financial technology startups (fintechs), considering the moderating effect of environmental unpredictability. First, there is little evidence on formal and informal control mechanisms in the context of organizational resilience. Second, studies on management controls focus on some resilience approaches, however, they do not address resilience from a proactive and reactive perspective. Third, the literature on the effects of environmental unpredictability on contemporary organizations, like the fintechs, is still scarce. On the one hand, new evidence is added to the literature on management control and organizational resilience, the understanding of antecedents of resilience in startups is expanded, and insights are provided on the effects of environmental unpredictability on the alignment of management controls and objects of control. On the other hand, insights are provided on control mechanisms that fintechs can benefit from to building organizational resilience, both to anticipate and prepare and to act and formulate responses in the face of business disruptions and uncertain times. The findings benefit the building and strengthening of resilience in fintechs, which becomes key for these startups to survive and consolidate themselves in the financial market. Data have been collected by survey and analyzed through structural equation modeling. Additional investigation has been conducted via fuzzy-set qualitative comparative analysis. The main results reveal that: (i) formal and informal control mechanisms foster proactive and reactive organizational resilience; (ii) in scenarios of high environmental unpredictability, greater attention given to informal controls results in higher proactive resilience levels; and (iii) there are four (five) organizational configurations that lead fintechs to high proactive (reactive) resilience.

Quantitative 3D orientation analysis of particles and voids to differentiate hand-built pottery forming techniques using X-ray microtomography and neutron tomography

This article describes the quantitative analysis of the 3D orientation of objects (i.e. particles and voids) within pottery fabrics to differentiate two categories of pottery hand-building primary forming techniques, specifically percussion-building and coil-building, comparing the use of two independent non-destructive imaging modalities, X-ray microtomography (µ-CT) and neutron tomography (NT). For this purpose, series of experimental organic-tempered vessels and coil sections were analysed. For both imaging modalities, two separate systems were employed for quantitatively describing both the orientation of individual objects, as well as the collective preferential alignment of objects within samples, utilising respectively polar and azimuth angles within a spherical coordinate system, and projected sizes within a positive Cartesian coordinate system. While the former provided full descriptions of the orientations of objects within 3D space, the latter, through a ratio dubbed here the ‘Orientation Index’ (OI), gave a simple numerical value with which the investigated samples were differentiated according to forming technique. Both imaging modalities were able to differentiate between coil-built and percussion-built vessels with a high degree of confidence, with the strength of these findings additionally demonstrated through extensive statistical modelling using Monte Carlo simulations. Despite differences in resolution and differences in the attenuation of X-rays and neutrons, µ-CT and NT were shown to provide comparable results. The findings presented here broadly agree with earlier studies; however, the quantitative and three-dimensional nature of the results enables more subtle features to be identified, while additionally, in principle, the non-destructive nature of both imaging techniques facilitates such structural analysis without recourse to invasive sampling.

Rotational symmetry detection in 3D using reflectional symmetry candidates and quaternion-based rotation parameterization

The property of symmetry in 3D objects is helpful in various applications such as object alignment, compression, symmetrical editing or reconstruction of incomplete objects. However, its robust and efficient detection is a challenging task. The two most commonly occurring types of symmetry are probably reflectional and rotational symmetry. While reflectional symmetry detection methods are quite plentiful, this does not seem to be the case with rotational symmetry detection. In this paper a use of approximate reflectional symmetries to derive plausible approximate rotational symmetries is proposed that can be integrated with multiple different approaches for reflectional symmetry detection. One such specific approach, based on maximizing a given symmetry measure, is chosen and combined with this idea. A modification of the maximization step for rotations is further proposed using a simple, yet efficient, quaternion-based parameterization of the rotation transformation which seems novel in the field of symmetry detection. The results confirm that this combination provides a robust and efficient solution for finding rotational symmetry in a 3D point set and can handle approximate symmetry, noisy input or even partial data.

UVtac: Switchable UV Marker-Based Tactile Sensing Finger for Effective Force Estimation and Object Localization

Vision-based tactile sensors provide diverse information of external tactile stimuli on the skin of sensors using both marker and reflective membrane images. However, when markers and reflective membranes are used concurrently, conventional opaque markers inevitably disturb the camera’s view of the reflective membrane. Thus, simultaneously increasing the quality of tactile information extracted from each visual feature has remained a challenge. In this study, we present a tactile sensing finger, UVtac, that utilizes switchable ultraviolet (UV) markers to decouple the marker and reflective membrane images to offer three-axis force estimation and object localization, whose performances are unaffected by each other. Our UVtac showed improved force estimation performance by using larger-sized UV markers through quantitative evaluation. The UVtac with 1.2 mm diameter markers showed a root mean square error of 0.264 N in estimating normal forces up to 10 N and 0.219 N in estimating the shear forces up to 5 N, when indented with an 8 × 8 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> square tooltip. Based on the object localization experiment, the UVtac was verified to have a 31 % lower root mean square error than the case using opaque black markers. Finally, we demonstrated object alignment and contact force-tracking tasks using the UVtac to emphasize its multifunctionality.

Cross-view SLAM solver: Global pose estimation of monocular ground-level video frames for 3D reconstruction using a reference 3D model from satellite images

Accurate pose estimation of monocular ground-level images with respect to a satellite/aerial photogrammetric dataset is an extremely challenging task. Existing solutions often perform an offline post-registration on 3D results from both sources, which however, suffers from non-rigid geometric distortions of the 3D monocular reconstruction and the lack of overlaps between the air to ground content. This paper provides an online solution that performs accurate pose estimation of the ground images with respect to a 3D model derived from satellite images, followed by a dense 3D reconstruction. Our solution takes advantage of the simultaneous localization and mapping (SLAM) paradigm to dynamically incorporate reference observations from the satellite 3D model during the incremental pose estimation, called a cross-view SLAM solver, which leverages both ground-to-satellite error and image-level reprojection errors at the frame level to yield image poses that are well-registered to the satellite 3D model for facade point cloud reconstruction. This process also has the advantage of correcting non-rigid distortions and trajectory drifts that are often presented in monocular SLAM systems. In addition, our solution leverages both the geometric and semantic information from the satellite model and ground images to perform a per-frame correction for frame-level pose initialization, in which a novel scheme called pose buffer is introduced to initialize the pose of each keyframe through robust visual hull alignment of ground objects. The proposed approach has been experimented using four trajectories of monocular videos collections (around 7,000 frames per trajectory on average) and a 3D semantic model from multi-view satellite images to estimate the poses of the video frames and yield point clouds consistent with the satellite 3D models, evaluated by using LiDAR ground-truth. Both qualitative and quantitative experiments demonstrate that our solution yields accurate, drift-free poses and point clouds consistent with the satellite data and visually much more pleasing 3D models with facade information. Compared to the LiDAR ground-truth, the derived 3D models with ground-level images have achieved a mean absolute error of 1.78 m (improved from 3.15 m achieved using SLAM without utilizing satellite 3D models) (A testing program will be made available through https://github.com/GDAOSU/Cross-View-SLAM).

HapticProxy: Providing Positional Vibrotactile Feedback on a Physical Proxy for Virtual-Real Interaction in Augmented Reality

Consistent visual and haptic feedback is an important way to improve the user experience when interacting with virtual objects. However, the perception provided in Augmented Reality (AR) mainly comes from visual cues and amorphous tactile feedback. This work explores how to simulate positional vibrotactile feedback (PVF) with multiple vibration motors when colliding with virtual objects in AR. By attaching spatially distributed vibration motors on a physical haptic proxy, users can obtain an augmented collision experience with positional vibration sensations from the contact point with virtual objects. We first developed a prototype system and conducted a user study to optimize the design parameters. Then we investigated the effect of PVF on user performance and experience in a virtual and real object alignment task in the AR environment. We found that this approach could significantly reduce the alignment offset between virtual and physical objects with tolerable task completion time increments. With the PVF cue, participants obtained a more comprehensive perception of the offset direction, more useful information, and a more authentic AR experience.

Image processing based package volume measurement system using kinect sensor

In this study, an image processing based volume measurement system is presented. The proposed scheme provides high accuracy, low cost and ease of calibration. There are no additional requirements such as alignment of objects during measurement, positioning the objects at a certain position in the measuring field, and these advantages reduce the labor effort in real-time. In the first stage, a problem-specific calibration process was performed and then Hough transform was applied in the image processing phase. Although the proposed method has been developed primarily to measure the volume of box-type objects, it also provides an approximate volume measurement for objects of random geometry. The system tested under various external disturbances has produced satisfactory results.