Journal articles: 'Posel'

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Vojvodík, Josef. "Posel, překladatel, interpret? Emil Saudek a Otokar Březina: mezi překladem a exegezí." Svět literatury 30, no. 62 (October 15, 2020): 9–26. http://dx.doi.org/10.14712/23366729.2020.2.1.

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Šulak, Tanja. "Sklenimo posel z vladarji znanja: učna pogodba. GV Izobraževanje, Ljubljana, 2003 Brečko, Daniela." Andragoška spoznanja 9, no. 2 (December 1, 2003): 77–78. http://dx.doi.org/10.4312/as.9.2.77-78.

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Becker, Rayda. "A history of the University of the Witwatersrand Art Galleries (Gertrude Posel Gallery)." de arte 35, no. 61 (April 2000): 95–100. http://dx.doi.org/10.1080/00043389.2000.11761309.

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Defant, Colin. "Poset pattern-avoidance problems posed by Yakoubov." Journal of Combinatorics 9, no. 2 (2018): 233–57. http://dx.doi.org/10.4310/joc.2018.v9.n2.a2.

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Denis, Philippe. "New Patterns of Disclosure: How HIV-Positive Support Group Members from KwaZulu-Natal Speak of their Status in Oral Narratives." Medical History 58, no. 2 (April 2014): 278–97. http://dx.doi.org/10.1017/mdh.2014.23.

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AbstractThis paper examines the representations and emotions associated with disclosure and stigma in Pietermaritzburg, KwaZulu-Natal, seven years after the start of the South African government’s ARV roll-out programme on the basis of in-depth oral history interviews of HIV-positive support group members. It argues that the wider availability of ARV treatment, the ensuing reduced fatality rate and the increased number of people, including men, who receive counselling and testing, may mean that HIV/AIDS is less stigmatised and that disclosure has become easier. This does not mean that stigma has disappeared and that the confusion created by competing world-views and belief systems has dissipated. Yet the situation of extreme denial and ideological confusion observed, for example, by Deborah Posel and her colleagues in 2003 and 2004 in the Mpumalanga province seems to have lessened. The interviews hint at the possibility that people living with HIV may have, more than a decade before, a language to express the emotions and feelings associated with HIV/AIDS. They were also found to be more assertive in matters of gender relations. These new attitudes would make disclosure easier and stigma more likely to recede.

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ZHONG, SHIJUN, and ALEXANDER D. MACKERELL. "POSE SCALING: GEOMETRICAL ASSESSMENT OF LIGAND BINDING POSES." Journal of Theoretical and Computational Chemistry 07, no. 04 (August 2008): 833–52. http://dx.doi.org/10.1142/s0219633608004155.

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A descriptor, the pose scaling factor, is proposed to quantitatively evaluate the geometrical match between a ligand and a target binding site. The pose scaling factor can be used to readily rank results of target-based in silico database screening or docking on large numbers of compounds. Such an approach will be of utility in the development and refinement of docking algorithms.

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Karim, A., A. Waibel, and A. Lechler. "Experimentelle Posenanalyse am Bearbeitungsroboter*/Pose dependancy of machining robots." wt Werkstattstechnik online 106, no. 05 (2016): 347–53. http://dx.doi.org/10.37544/1436-4980-2016-05-61.

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Die Verbesserung der Bearbeitungsqualität stellt einen aktuellen Forschungsschwerpunkt bei der spanenden Bearbeitung mit Industrierobotern dar. Dabei hängt die Bearbeitungsqualität des Werkstücks wesentlich von der eingenommenen Pose des Roboters ab. Der Fachbeitrag stellt die Durchführung einer experimentellen Analyse zur Bestimmung der Bearbeitungsqualität in unterschiedlichen Posen vor. Erste Ergebnisse werden ebenfalls präsentiert.   The improvement of machining quality is an actual focus of research in the area of machining with industrial robots. The machining quality that can be achieved is strongly dependant on the robot‘s pose. In the course of this paper the execution of experimental analysis for the determination of machining quality at different poses as well as first results are presented.

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Zhou, Desen, and Qian He. "PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation." IEEE Access 8 (2020): 15007–16. http://dx.doi.org/10.1109/access.2020.2967147.

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Moodie, T. Dunbar. "Apartheid in the 1950s - The Making of Apartheid, 1948–1961: Conflict and Compromise. By Deborah Posel. (Oxford Studies in African Affairs.) Oxford: Clarendon Press, 1991. Pp. xii+297. £37.50." Journal of African History 34, no. 1 (March 1993): 174–75. http://dx.doi.org/10.1017/s0021853700033247.

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Guo, Fangtai, Zaixing He, Shuyou Zhang, and Xinyue Zhao. "Estimation of 3D human hand poses with structured pose prior." IET Computer Vision 13, no. 8 (December 2019): 683–90. http://dx.doi.org/10.1049/iet-cvi.2018.5480.

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Wang, Chunyu, Haibo Qiu, Alan L. Yuille, and Wenjun Zeng. "Learning Basis Representation to Refine 3D Human Pose Estimations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8925–32. http://dx.doi.org/10.1609/aaai.v33i01.33018925.

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Estimating 3D human poses from 2D joint positions is an illposed problem, and is further complicated by the fact that the estimated 2D joints usually have errors to which most of the 3D pose estimators are sensitive. In this work, we present an approach to refine inaccurate 3D pose estimations. The core idea of the approach is to learn a number of bases to obtain tight approximations of the low-dimensional pose manifold where a 3D pose is represented by a convex combination of the bases. The representation requires that globally the refined poses are close to the pose manifold thus avoiding generating illegitimate poses. Second, the designed bases also have the property to guarantee that the distances among the body joints of a pose are within reasonable ranges. Experiments on benchmark datasets show that our approach obtains more legitimate poses over the baselines. In particular, the limb lengths are closer to the ground truth.

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von Marcard, T., B. Rosenhahn, M. J. Black, and G. Pons-Moll. "Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs." Computer Graphics Forum 36, no. 2 (May 2017): 349–60. http://dx.doi.org/10.1111/cgf.13131.

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Zhou, Lijuan, Wanqing Li, Philip Ogunbona, and Zhengyou Zhang. "Jointly Learning Visual Poses and Pose Lexicon for Semantic Action Recognition." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 2 (February 2020): 457–67. http://dx.doi.org/10.1109/tcsvt.2019.2890829.

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Darby, John, Baihua Li, and Nicholas Costen. "Tracking object poses in the context of robust body pose estimates." Computer Vision and Image Understanding 127 (October 2014): 57–72. http://dx.doi.org/10.1016/j.cviu.2014.06.009.

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Thompson, Matthew P., Jude Bayham, and Erin Belval. "Potential COVID-19 Outbreak in Fire Camp: Modeling Scenarios and Interventions." Fire 3, no. 3 (August 1, 2020): 38. http://dx.doi.org/10.3390/fire3030038.

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The global COVID-19 pandemic will pose unique challenges to the management of wildland fire in 2020. Fire camps may provide an ideal setting for the transmission of SARS-CoV-2, the virus that causes COVID-19. However, intervention strategies can help minimize disease spread and reduce the risk to the firefighting community. We developed a COVID-19 epidemic model to highlight the risks posed by the disease during wildland fire incidents. Our model accounts for the transient nature of the population on a wildland fire incident, which poses unique risks to the management of communicable diseases in fire camps. We used the model to assess the impact of two types of interventions: the screening of a firefighter arriving on an incident, and social distancing measures. Our results suggest that both interventions are important to mitigate the risks posed by the SARS-CoV-2 virus. However, screening is relatively more effective on short incidents, whereas social distancing is relatively more effective during extended campaigns. We conclude with a discussion of model limitations and potential extensions to the model.

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Sun, Jun, Mantao Wang, Xin Zhao, and Dejun Zhang. "Multi-View Pose Generator Based on Deep Learning for Monocular 3D Human Pose Estimation." Symmetry 12, no. 7 (July 4, 2020): 1116. http://dx.doi.org/10.3390/sym12071116.

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In this paper, we study the problem of monocular 3D human pose estimation based on deep learning. Due to single view limitations, the monocular human pose estimation cannot avoid the inherent occlusion problem. The common methods use the multi-view based 3D pose estimation method to solve this problem. However, single-view images cannot be used directly in multi-view methods, which greatly limits practical applications. To address the above-mentioned issues, we propose a novel end-to-end 3D pose estimation network for monocular 3D human pose estimation. First, we propose a multi-view pose generator to predict multi-view 2D poses from the 2D poses in a single view. Secondly, we propose a simple but effective data augmentation method for generating multi-view 2D pose annotations, on account of the existing datasets (e.g., Human3.6M, etc.) not containing a large number of 2D pose annotations in different views. Thirdly, we employ graph convolutional network to infer a 3D pose from multi-view 2D poses. From experiments conducted on public datasets, the results have verified the effectiveness of our method. Furthermore, the ablation studies show that our method improved the performance of existing 3D pose estimation networks.

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Zhang, Shuai, Jiming Guo, Nianxue Luo, Lei Wang, Wei Wang, and Kai Wen. "Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm." Remote Sensing 11, no. 6 (March 17, 2019): 652. http://dx.doi.org/10.3390/rs11060652.

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The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier using an SVM algorithm. Secondly, in the online phase, the poses of RSS data measured online are recognised by a pose classifier, and RSS data measured online are grouped with different poses. Then online RSS data from each group at an unknown user location are reclassified as corresponding RPs by the RP classifiers of the corresponding poses. Finally, user location is determined by grouped RSS data corresponding to coordinates of the RPs. By considering the user pose and user body shadowing, the observed RSS data matches the fingerprint database better, and the classification accuracy of grouped online RSS data is remarkably improved. To verify performances of the proposed method, experiments are carried out: one in an office setting, and the other in a lecture hall. The experimental results show that the positioning accuracies of the proposed PRASVM algorithm outperform the conventional weighted k-nearest neighbour (WKNN) algorithm by 52.29% and 40.89%, outperform the SVM algorithm by 73.74% and 60.45%, and outperform the pose recognition-assisted WKNN algorithm by 34.76% and 21.86%, respectively. As a result, the PRASVM algorithm noticeably improves positioning accuracy.

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Ping-Han Lee, Gee-Sern Hsu, Yun-Wen Wang, and Yi-Ping Hung. "Subject-Specific and Pose-Oriented Facial Features for Face Recognition Across Poses." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, no. 5 (October 2012): 1357–68. http://dx.doi.org/10.1109/tsmcb.2012.2191773.

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Xu, Tao. "Hand Grasping Choice and Analysis for Tasks." Applied Mechanics and Materials 644-650 (September 2014): 1752–58. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1752.

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Resulting in an optimal grasping pose of satisfying task requirements is a critical problem for hands manipulation. Based on the problem, taxonomy of grasping poses was created. This paper presents key-based search method that can obtain all hand manipulation poses matching the grasped object. However, these grasping pose must be ranked by combining with task requirements and object geometry. In this way, optimal grasping pose can be achieved. The effectiveness of the method was demonstrated by using grasping pose taxonomy comparisons from motion capture example database.

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Kasprzak, Włodzimierz, Artur Wilkowski, and Karol Czapnik. "Hand gesture recognition based on free-form contours and probabilistic inference." International Journal of Applied Mathematics and Computer Science 22, no. 2 (June 1, 2012): 437–48. http://dx.doi.org/10.2478/v10006-012-0033-6.

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Hand gesture recognition based on free-form contours and probabilistic inference A computer vision system is described that captures color image sequences, detects and recognizes static hand poses (i.e., "letters") and interprets pose sequences in terms of gestures (i.e., "words"). The hand object is detected with a double-active contour-based method. A tracking of the hand pose in a short sequence allows detecting "modified poses", like diacritic letters in national alphabets. The static hand pose set corresponds to hand signs of a thumb alphabet. Finally, by tracking hand poses in a longer image sequence, the pose sequence is interpreted in terms of gestures. Dynamic Bayesian models and their inference methods (particle filter and Viterbi search) are applied at this stage, allowing a bi-driven control of the entire system.

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SUNG, JAE-WON, and DAIJIN KIM. "REAL-TIME FACIAL POSE IDENTIFICATION WITH HIERARCHICALLY STRUCTURED ML POSE CLASSIFIER." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 02 (March 2004): 127–42. http://dx.doi.org/10.1142/s0218001404003125.

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Since pose-varying face images form nonlinear convex manifold in high dimensional image space, it is difficult to model their pose distribution in terms of a simple probabilistic density function. To solve this difficulty, we divide the pose space into many constituent pose classes and treat the continuous pose estimation problem as a discrete pose-class identification problem. We propose to use a hierarchically structured ML (Maximum Likelihood) pose classifiers in the reduced feature space to decrease the computation time for pose identification, where pose space is divided into several pose groups and each group consists of a number of similar neighboring poses. We use the CONDENSATION algorithm to find a newly appearing face and track the face with a variety of poses in real-time. Simulation results show that our proposed pose identification using the hierarchically structured ML pose classifiers can perform a faster pose identification than conventional pose identification using the flat structured ML pose classifiers. A real-time facial pose tracking system is built with high speed hierarchically structured ML pose classifiers.

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Yasin, Hashim, and Björn Krüger. "An Efficient 3D Human Pose Retrieval and Reconstruction from 2D Image-Based Landmarks." Sensors 21, no. 7 (April 1, 2021): 2415. http://dx.doi.org/10.3390/s21072415.

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We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of joints of the normalized 3D poses onto 2D image-planes by fully exploiting a variety of virtual cameras. With this approach, we not only transform 3D pose space to the normalized 2D pose space but also resolve the 2D-3D cross-domain retrieval task efficiently. The proposed architecture searches for poses from a MoCap dataset that are near to a given 2D query pose in a definite feature space made up of specific joint sets. These retrieved poses are then used to construct a weak perspective camera and a final 3D posture under the camera model that minimizes the reconstruction error. To estimate unknown camera parameters, we introduce a nonlinear, two-fold method. We exploit the retrieved similar poses and the viewing directions at which the MoCap dataset was sampled to minimize the projection error. Finally, we evaluate our approach thoroughly on a large number of heterogeneous 2D examples generated synthetically, 2D images with ground-truth, a variety of real in-the-wild internet images, and a proof of concept using 2D hand-drawn sketches of human poses. We conduct a pool of experiments to perform a quantitative study on PARSE dataset. We also show that the proposed system yields competitive, convincing results in comparison to other state-of-the-art methods.

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Zhao, H., D. Acharya, M. Tomko, and K. Khoshelham. "INDOOR LIDAR RELOCALIZATION BASED ON DEEP LEARNING USING A 3D MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 541–47. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-541-2020.

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Abstract. Indoor localization, navigation and mapping systems highly rely on the initial sensor pose information to achieve a high accuracy. Most existing indoor mapping and navigation systems cannot initialize the sensor poses automatically and consequently these systems cannot perform relocalization and recover from a pose estimation failure. For most indoor environments, a map or a 3D model is often available, and can provide useful information for relocalization. This paper presents a novel relocalization method for lidar sensors in indoor environments to estimate the initial lidar pose using a CNN pose regression network trained using a 3D model. A set of synthetic lidar frames are generated from the 3D model with known poses. Each lidar range image is a one-channel range image, used to train the CNN pose regression network from scratch to predict the initial sensor location and orientation. The CNN regression network trained by synthetic range images is used to estimate the poses of the lidar using real range images captured in the indoor environment. The results show that the proposed CNN regression network can learn from synthetic lidar data and estimate the pose of real lidar data with an accuracy of 1.9 m and 8.7 degrees.

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Su, Yongzhi, Jason Rambach, Alain Pagani, and Didier Stricker. "SynPo-Net—Accurate and Fast CNN-Based 6DoF Object Pose Estimation Using Synthetic Training." Sensors 21, no. 1 (January 5, 2021): 300. http://dx.doi.org/10.3390/s21010300.

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Estimation and tracking of 6DoF poses of objects in images is a challenging problem of great importance for robotic interaction and augmented reality. Recent approaches applying deep neural networks for pose estimation have shown encouraging results. However, most of them rely on training with real images of objects with severe limitations concerning ground truth pose acquisition, full coverage of possible poses, and training dataset scaling and generalization capability. This paper presents a novel approach using a Convolutional Neural Network (CNN) trained exclusively on single-channel Synthetic images of objects to regress 6DoF object Poses directly (SynPo-Net). The proposed SynPo-Net is a network architecture specifically designed for pose regression and a proposed domain adaptation scheme transforming real and synthetic images into an intermediate domain that is better fit for establishing correspondences. The extensive evaluation shows that our approach significantly outperforms the state-of-the-art using synthetic training in terms of both accuracy and speed. Our system can be used to estimate the 6DoF pose from a single frame, or be integrated into a tracking system to provide the initial pose.

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Wang, Hui, Peng He, Nannan Li, and Junjie Cao. "Pose Recognition of 3D Human Shapes via Multi-View CNN with Ordered View Feature Fusion." Electronics 9, no. 9 (August 23, 2020): 1368. http://dx.doi.org/10.3390/electronics9091368.

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Rapid pose classification and pose retrieval in 3D human datasets are important problems in shape analysis. In this paper, we extend the Multi-View Convolutional Neural Network (MVCNN) with ordered view feature fusion for orientation-aware 3D human pose classification and retrieval. Firstly, we combine each learned view feature in an orderly manner to form a compact representation for orientation-aware pose classification. Secondly, for pose retrieval, the Siamese network is adopted to learn descriptor vectors, where their L2 distances are close for pairs of shapes with the same poses and are far away for pairs of shapes with different poses. Furthermore, we also construct a larger 3D Human Pose Recognition Dataset (HPRD) consisting of 100,000 shapes for the evaluation of pose classification and retrieval. Experiments and comparisons demonstrate that our method obtains better results than previous works of pose classification and retrieval on the 3D human datasets, such as SHREC’14, FAUST, and HPRD.

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Marín-Jiménez, Manuel J., Francisco J. Romero-Ramirez, Rafael Muñoz-Salinas, and Rafael Medina-Carnicer. "3D human pose estimation from depth maps using a deep combination of poses." Journal of Visual Communication and Image Representation 55 (August 2018): 627–39. http://dx.doi.org/10.1016/j.jvcir.2018.07.010.

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Liu, Yongdan, and Matthew Tingchi Liu. "Celebrity poses and consumer attitudes in endorsement advertisements." Asia Pacific Journal of Marketing and Logistics 31, no. 4 (September 9, 2019): 1027–41. http://dx.doi.org/10.1108/apjml-07-2018-0270.

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Purpose The purpose of this paper is to investigate the effects of celebrity’s poses on consumer attitudes towards the endorsement advertisement by drawing from perceptual fluency hypotheses. Design/methodology/approach Study 1 used a single-factor, two-condition (distinctive pose and casual pose) between-subject design. Both Study 2a and Study 2b employed a single-factor, two-condition (distinctive pose, casual pose) between-subject design and tested the mediator of pose matchiness. Study 3 employed a 2 (pose condition: distinctive, casual)×2 (cognitive capacity: no load, load) between-subject design to test the moderator. All data were sourced from more than 600 respondents in China. Findings Study 1 illustrated that the existence of a distinctive pose can lead to higher consumer attitudes regarding advertising stimuli and the endorsed brands as well as more positive behavioural intentions towards endorsed products. Study 2a and Study 2b replicated such finding and demonstrated that the feeling of pose matchiness mediates the relationship between celebrities’ pose and endorsement outcomes. Study 3 further revealed that the cognitive capacity moderates such a relationship, that is, that the effect of a distinctive pose is stronger (lesser) when audiences’ cognitive capacity is loaded (not loaded). Originality/value Research efforts to date examining the nature of celebrity advertisement have been limited to celebrity’s faces and facial expressions. Little investigation in the marketing domain has considered the consequences of celebrities’ poses. This study takes the first step in revealing the positive effect of distinctive celebrity poses in product endorsement.

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Therriault, Thomas W., and Leif-Matthias Herborg. "A qualitative biological risk assessment for vase tunicate Ciona intestinalis in Canadian waters: using expert knowledge." ICES Journal of Marine Science 65, no. 5 (April 17, 2008): 781–87. http://dx.doi.org/10.1093/icesjms/fsn059.

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Abstract Therriault, T. W., and Herborg, L-M. 2008. A qualitative biological risk assessment for vase tunicate Ciona intestinalis in Canadian waters: using expert knowledge. – ICES Journal of Marine Science, 65: 781–787. Non-indigenous species (NIS) can pose a significant level of risk, through potential ecological or genetic consequences, to environments to which they are introduced. One way to characterize the overall risk posed by a NIS is to combine the probability and consequences of its establishment in a risk assessment that can be used to inform managers and policy-makers. The vase tunicate Ciona intestinalis is considered to be a cryptogenic species in eastern Canadian waters, but has not yet been reported from Pacific Canada. Because it is unclear what level of risk it poses for Canadian waters, we conducted a biological risk assessment for C. intestinalis and its potential pathogens, parasites, and fellow travellers. An expert survey was conducted to inform the risk assessment. The ecological risk posed by C. intestinalis was considered high (moderate uncertainty) on the Atlantic coast, and moderate (high uncertainty) on the Pacific coast. The genetic risk posed by C. intestinalis was considered moderate on both coasts, with low uncertainty on the Atlantic coast and high uncertainty on the Pacific coast, where hybridization with Ciona savignyi may be possible. Pathogens, parasites, and fellow travellers were considered to be a moderate ecological risk and a low genetic risk (with high uncertainty) for both coasts.

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Wang, X., H. Yu, and D. Feng. "Pose estimation in runway end safety area using geometry structure features." Aeronautical Journal 120, no. 1226 (April 2016): 675–91. http://dx.doi.org/10.1017/aer.2016.16.

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ABSTRACTA novel image-based method is presented in this paper to estimate the poses of commercial aircrafts in a runway end safety area. Based on the fact that similar poses of an aircraft will have similar geometry structures, this method first extracts features to describe the structure of an aircraft's fuselage and aerofoil by RANdom Sample Consensus algorithm (RANSAC), and then uses the central moments to obtain the aircrafts’ pose information. Based on the proposed pose information, a two-step feature matching strategy is further designed to identify an aircraft's particular pose. In order to validate the accuracy of the pose estimation and the effectiveness of the proposed algorithm, we construct a pose database of two common aircrafts in Asia. The experiments show that the designed low-dimension features can accurately capture the aircraft's pose information and the proposed algorithm can achieve satisfied matching accuracy.

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Križaj, Janez, Peter Peer, Vitomir Štruc, and Simon Dobrišek. "Simultaneous multi-descent regression and feature learning for facial landmarking in depth images." Neural Computing and Applications 32, no. 24 (October 23, 2019): 17909–26. http://dx.doi.org/10.1007/s00521-019-04529-7.

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AbstractFace alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations.

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Cesario, Joseph, and David J. Johnson. "Power Poseur." Social Psychological and Personality Science 9, no. 7 (August 22, 2017): 781–89. http://dx.doi.org/10.1177/1948550617725153.

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Strong claims have been made that power poses can significantly improve one’s life. Starting from an evolutionary perspective, we reason that expansive poses will have no impact in more realistic situations, as in the presence of an interaction partner or when participants are aware of what the pose should accomplish. Across four dyadic studies including both commonly used outcomes and a negotiation task (which could actually have direct benefits for one’s life), we find nearly uniform null effects of holding expansive poses, despite checks confirming the success of the manipulation. For example, in two of the studies, participants watched a popular TED talk on power poses, held an expansive pose, and then completed a negotiation in the presence of a partner, as might happen in real life. We argue that researchers should stop recommending power poses as an empirically supported strategy for improving one’s life.

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Marsocci, Valerio, and Lorenzo Lastilla. "POSE-ID-on—A Novel Framework for Artwork Pose Clustering." ISPRS International Journal of Geo-Information 10, no. 4 (April 11, 2021): 257. http://dx.doi.org/10.3390/ijgi10040257.

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In this work, we focus our attention on the similarity among works of art based on human poses and the actions they represent, moving from the concept of Pathosformel in Aby Warburg. This form of similarity is investigated by performing a pose clustering of the human poses, which are modeled as 2D skeletons and are defined as sets of 14 points connected by limbs. To build a dataset of properly annotated artwork images (that is, including the 2D skeletons of the human figures represented), we relied on one of the most popular, recent, and accurate deep learning frameworks for pose tracking of human figures, namely OpenPose. To measure the similarity between human poses, two alternative distance functions are proposed. Moreover, we developed a modified version of the K-Medians algorithm to cluster similar poses and to find a limited number of poses that are representative of the whole dataset. The proposed approach was also compared to two popular clustering strategies, that is, K-Means and the Nearest Point Algorithm, showing higher robustness to outliers. Finally, we assessed the validity of the proposed framework, which we named POSE-ID-on, in both a qualitative and in a quantitative way by simulating a supervised setting, since we lacked a proper reference for comparison.

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Su, Jianhua, Zhi-Yong Liu, Hong Qiao, and Chuankai Liu. "Pose-estimation and reorientation of pistons for robotic bin-picking." Industrial Robot: An International Journal 43, no. 1 (January 18, 2016): 22–32. http://dx.doi.org/10.1108/ir-06-2015-0129.

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Purpose – Picking up pistons in arbitrary poses is an important step on car engine assembly line. The authors usually use vision system to estimate the pose of the pistons and then guide a stable grasp. However, a piston in some poses, e.g. the mouth of the piston faces forward, is hardly to be directly grasped by the gripper. Thus, we need to reorient the piston to achieve a desired pose, i.e. let its mouth face upward, for grasping. Design/methodology/approach – This paper aims to present a vision-based picking system that can grasp pistons in arbitrary poses. The whole picking process is divided into two stages. At localization stage, a hierarchical approach is proposed to estimate the piston’s pose from image which usually involves both heavy noise and edge distortions. At grasping stage, multi-step robotic manipulations are designed to enable the piston to follow a nominal trajectory to reach to the minimum of the distance between the piston’s center and the support plane. That is, under the design input, the piston would be pushed to achieve a desired orientation. Findings – A target piston in arbitrary poses would be picked from the conveyor belt by the gripper with the proposed method. Practical implications – The designed robotic bin-picking system using vision is an advantage in terms of flexibility in automobile manufacturing industry. Originality/value – The authors develop a methodology that uses a pneumatic gripper and 2D vision information for picking up multiple pistons in arbitrary poses. The rough pose of the parts are detected based on a hierarchical approach for detection of multiple ellipses in the environment that usually involve edge distortions. The pose uncertainties of the piston are eliminated by multi-step robotic manipulations.

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Poels, Vefie. "Rooms triomfalisme in de Amsterdamse straten : De betekenis van het 27e Internationaal Eucharistisch Congres (22-27 juli 1924) voor de ontwikkeling van het Nederlandse katholicisme." DNK : Documentatieblad voor de Nederlandse kerkgeschiedenis na 1800 43, no. 93 (December 1, 2020): 95–133. http://dx.doi.org/10.5117/dnk2020.93.002.poel.

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Abstract This article analyses the preparations and the implementation of the 27th International Eucharistic Congress, held at Amsterdam in 1924. After an introduction on the (negative) image of this congress in Dutch historiography, on the person of de papal legate (the Dutch cardinal Willem van Rossum CSSR), and on the phenomenon of the ‘Eucharistic Congresses’ and its organizing committee, the author analyses the forces pro and contra the organization of such a Congress in Amsterdam. The initiative was taken by some ultramontane clergy and laypeople, gathered around the revival of the devotion of the Amsterdam Eucharistic Miracle (1345). The bishop involved, mgr. A. Callier of Harlem, felt little of inviting the organizing committee to choose for Amsterdam, and also the (Roman Catholic) Prime Minister Ruijs de Beerenbrouck kept aloof, fearing a revival of protestant antipapism. So in advance it was already clear that the government and queen Wilhelmina would avoid every diplomatic presence ‐ quit different as was the case at similar congresses in other countries. Besides, a grand procession through the Amsterdam streets was impossible because of the then still prevailing prohibition of public religious processions. The most important ceremonies thus were held in the Amsterdam soccer stadium. The Congress strengthened the feeling of unity of the ‘common’ Catholics with the Dutch cardinal as their shared national icon, but on the other hand it worsened the relations between the Dutch episcopate and the Prime Minister, and their ‘man in Rome’. In the end the Eucharistic Congress had no antipapistic consequences, and only limited political consequences, thanks to quite a lot of informal negotiations before and during the Congress. It nevertheless played a role on the background, when the government decided in 1925 to close the Dutch embassy at the Vatican.

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Poell, Thomas, and Kaouthar Darmoni. "Twitter as a multilingual space: The articulation of the Tunisian revolution through #sidibouzid." NECSUS. European Journal of Media Studies 1, no. 1 (January 1, 2012): 14–34. http://dx.doi.org/10.5117/necsus2012.1.poel.

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Koch, Erin, Famya Baig, and Qasim Zaidi. "Picture perception reveals mental geometry of 3D scene inferences." Proceedings of the National Academy of Sciences 115, no. 30 (July 9, 2018): 7807–12. http://dx.doi.org/10.1073/pnas.1804873115.

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Pose estimation of objects in real scenes is critically important for biological and machine visual systems, but little is known of how humans infer 3D poses from 2D retinal images. We show unexpectedly remarkable agreement in the 3D poses different observers estimate from pictures. We further show that all observers apply the same inferential rule from all viewpoints, utilizing the geometrically derived back-transform from retinal images to actual 3D scenes. Pose estimations are altered by a fronto-parallel bias, and by image distortions that appear to tilt the ground plane. We used pictures of single sticks or pairs of joined sticks taken from different camera angles. Observers viewed these from five directions, and matched the perceived pose of each stick by rotating an arrow on a horizontal touchscreen. The projection of each 3D stick to the 2D picture, and then onto the retina, is described by an invertible trigonometric expression. The inverted expression yields the back-projection for each object pose, camera elevation, and observer viewpoint. We show that a model that uses the back-projection, modulated by just two free parameters, explains 560 pose estimates per observer. By considering changes in retinal image orientations due to position and elevation of limbs, the model also explains perceived limb poses in a complex scene of two bodies lying on the ground. The inferential rules simply explain both perceptual invariance and dramatic distortions in poses of real and pictured objects, and show the benefits of incorporating projective geometry of light into mental inferences about 3D scenes.

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Youyang, Feng, Wang Qing, and Yang Gaochao. "Incremental 3-D pose graph optimization for SLAM algorithm without marginalization." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092530. http://dx.doi.org/10.1177/1729881420925304.

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Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

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Lindell, Annukka K. "Left cheek poses garner more likes: the effect of pose orientation on Instagram engagement." Laterality: Asymmetries of Body, Brain and Cognition 24, no. 5 (December 11, 2018): 600–613. http://dx.doi.org/10.1080/1357650x.2018.1556278.

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Agahian, Saeid, Farhood Negin, and Cemal Köse. "Improving bag-of-poses with semi-temporal pose descriptors for skeleton-based action recognition." Visual Computer 35, no. 4 (February 21, 2018): 591–607. http://dx.doi.org/10.1007/s00371-018-1489-7.

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Radifar, Muhammad, Nunung Yuniarti, and Enade Perdana Istyastono. "PyPLIF-ASSISTED REDOCKING INDOMETHACIN-(R)-ALPHA-ETHYL-ETHANOLAMIDE INTO CYCLOOXYGENASE-1." Indonesian Journal of Chemistry 13, no. 3 (December 18, 2013): 283–86. http://dx.doi.org/10.22146/ijc.21289.

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Identification of Protein-Ligand Interaction Fingerprints (PLIF) has been performed as the rescoring strategy to identify the best pose for the docked poses of indomethacin-(R)-α-ethyl-etanolamide (IMM) in the binding site of cyclooxygenase-1 (COX-1) from simulations using PLANTS molecular docking software version 1.2 (PLANTS1.2). Instead of using the scoring functions included in the docking software, the strategy presented in this article used external software called PyPLIF that could identify the interactions of the ligand to the amino acid residues in the binding pocket and presents them as binary bitstrings, which subsequently were compared to the interaction bitstrings of the co-crystal ligand pose. The results show that PyPLIF-assisted redocking strategy could select the correct pose much better compared to the pose selection without rescoring. Out of 1000 iterative attempts, PyPLIF-assisted redocking simulations could identify 971 correct poses (more than 95%), while the redocking simulations without PyPLIF could only identify 500 correct poses (50%).These works have also provided us with the initial step of the construction of a valid Structure-Based Virtual Screening (SBVS) protocol to identify COX-1 inhibitors.

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Masoner, Hannah, Alen Hajnal, Joseph D. Clark, Catherine Dowell, Tyler Surber, Ashley Funkhouser, Jonathan Doyon, Gabor Legradi, Krisztian Samu, and Jeffrey B. Wagman. "Complexity of postural sway affects affordance perception of reachability in virtual reality." Quarterly Journal of Experimental Psychology 73, no. 12 (July 27, 2020): 2362–75. http://dx.doi.org/10.1177/1747021820943757.

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Visual perception of whether an object is within reach while standing in different postures was investigated. Participants viewed a three-dimensional (3D) virtual reality (VR) environment with a stimulus object (red ball) placed at different egocentric distances. Participants reported whether the object was reachable while in a standard pose as well as in two separate active balance poses (yoga tree pose and toe-to-heel pose). Feedback on accuracy was not provided, and participants were not allowed to attempt to reach. Response time, affordance judgements (reachable and not reachable), and head movements were recorded on each trial. Consistent with recent research on perception of reaching ability, the perceived boundary occurred at approximately 120% of arm length, indicating overestimation of perceived reaching ability. Response times increased with distance, and were shortest for the most difficult pose—the yoga tree pose. Head movement amplitude increased with increases in balance demands. Unexpectedly, the coefficient of variation was comparable in the two active balance poses, and was more extreme in the standard control pose for the shortest and longest distances. More complex descriptors of postural sway (i.e., effort-to-compress) were predictive of perception while in the tree pose and the toe-to-heel pose, as compared with control stance. This demonstrates that standard measures of central tendency are not sufficient for describing multiscale interactions of postural dynamics in functional tasks.

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Kawanaka, Haruki, Fuminori Matsubara, and Yuji Iwahori. "Soccer Player’s Pose Recognition by Creative Search for Generating Free Viewpoint Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 193–203. http://dx.doi.org/10.20965/jaciii.2009.p0193.

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The low-cost generation we have proposed for a free viewpoint image of soccer games uses a multiple viewpoint image database designed with computer graphics for recognizing player poses in the real image and for generating virtual scenes. A pose is recognized from a player’s silhouette and applying parametric eigenspace method. Because only silhouette information is used, however, limb positioning may be backward or the body misdirected. Our new proposal for excluding misplaced left and right limb poses assumes that changes in a pose, especially limb positioning, between sequential frames are continuous, so the limb positioning in 3D space and 2D images can be determined and the search range restricted in eigenspace. We also propose generating continuous frames for cases in which a correct pose exists outside of the restricted range by setting an initial state and handling error.

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Li, Yang, Kan Li, Shuai Jiang, Ziyue Zhang, Congzhentao Huang, and Richard Yi Da Xu. "Geometry-Driven Self-Supervised Method for 3D Human Pose Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11442–49. http://dx.doi.org/10.1609/aaai.v34i07.6808.

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The neural network based approach for 3D human pose estimation from monocular images has attracted growing interest. However, annotating 3D poses is a labor-intensive and expensive process. In this paper, we propose a novel self-supervised approach to avoid the need of manual annotations. Different from existing weakly/self-supervised methods that require extra unpaired 3D ground-truth data to alleviate the depth ambiguity problem, our method trains the network only relying on geometric knowledge without any additional 3D pose annotations. The proposed method follows the two-stage pipeline: 2D pose estimation and 2D-to-3D pose lifting. We design the transform re-projection loss that is an effective way to explore multi-view consistency for training the 2D-to-3D lifting network. Besides, we adopt the confidences of 2D joints to integrate losses from different views to alleviate the influence of noises caused by the self-occlusion problem. Finally, we design a two-branch training architecture, which helps to preserve the scale information of re-projected 2D poses during training, resulting in accurate 3D pose predictions. We demonstrate the effectiveness of our method on two popular 3D human pose datasets, Human3.6M and MPI-INF-3DHP. The results show that our method significantly outperforms recent weakly/self-supervised approaches.

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Desingh, Karthik, Shiyang Lu, Anthony Opipari, and Odest Chadwicke Jenkins. "Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects." Science Robotics 4, no. 30 (May 22, 2019): eaaw4523. http://dx.doi.org/10.1126/scirobotics.aaw4523.

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Robots working in human environments often encounter a wide range of articulated objects, such as tools, cabinets, and other jointed objects. Such articulated objects can take an infinite number of possible poses, as a point in a potentially high-dimensional continuous space. A robot must perceive this continuous pose to manipulate the object to a desired pose. This problem of perception and manipulation of articulated objects remains a challenge due to its high dimensionality and multimodal uncertainty. Here, we describe a factored approach to estimate the poses of articulated objects using an efficient approach to nonparametric belief propagation. We consider inputs as geometrical models with articulation constraints and observed RGBD (red, green, blue, and depth) sensor data. The described framework produces object-part pose beliefs iteratively. The problem is formulated as a pairwise Markov random field (MRF), where each hidden node (continuous pose variable) is an observed object-part’s pose and the edges denote the articulation constraints between the parts. We describe articulated pose estimation by a “pull” message passing algorithm for nonparametric belief propagation (PMPNBP) and evaluate its convergence properties over scenes with articulated objects. Robot experiments are provided to demonstrate the necessity of maintaining beliefs to perform goal-driven manipulation tasks.

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Grudinschi, Melanie, Kyle Norland, Sang Won Lee, and Sol Lim. "Task Analysis on Yoga Poses Toward a Wearable Sensor-based Learning System for Users with Visual Impairment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 634–38. http://dx.doi.org/10.1177/1071181320641144.

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People with visual impairments may experience difficulties in learning new physical exercises due to a lack of visual feedback. Learning and practicing yoga is especially challenging for this population as yoga requires imitation-oriented learning. A typical yoga class requires students to observe and copy poses and movements as the instructor presents them, while maintaining postural balance during the practice. Without additional, nonvisual feedback, it can be difficult for students with visual impairments to understand whether they have accurately copied a pose – and if they have not, how to fix an inaccurate pose. Therefore, there is a need for an intelligent learning system that can capture a person’s physical posture and provide additional, nonvisual feedback to guide them into a correct pose. This study is a preliminary step toward the development of a wearable inertial sensor-based virtual learning system for people who are blind or have low vision. Using hierarchical task analysis, we developed a step-by-step conceptual model of yoga poses, which can be used in constructing an effective nonvisual feedback system. We also ranked sensor locations according to their importance by analyzing postural deviations in each pose compared to the reference starting pose.

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Li, Chun Ling, and Yu Feng Lu. "Head Pose Recognition Based on 2-D KPCA." Applied Mechanics and Materials 373-375 (August 2013): 468–72. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.468.

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One’s head pose can be estimated using face images. The hidden manifold of head pose in the high dimensional space can be successfully embedded into a 2 dimensional space using Kernel Principal Component Analysis (KPCA). A pose curve is gotten using KPCA train samples and new pose image is projected onto this curve. The pose angle can be estimated using interpolation method. The disadvantage of traditional linear method is conquered by using 2-D KPCA and the experimental results that the method is effective to estimate head poses. The kernel functions effects on estimation accuracy are also discussed.

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Chen, Songle, Xuejian Zhao, Bingqing Luo, and Zhixin Sun. "Visual Browse and Exploration in Motion Capture Data with Phylogenetic Tree of Context-Aware Poses." Sensors 20, no. 18 (September 13, 2020): 5224. http://dx.doi.org/10.3390/s20185224.

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Visual browse and exploration in motion capture data take resource acquisition as a human–computer interaction problem, and it is an essential approach for target motion search. This paper presents a progressive schema which starts from pose browse, then locates the interesting region and then switches to online relevant motion exploration. It mainly addresses three core issues. First, to alleviate the contradiction between the limited visual space and ever-increasing size of real-world database, it applies affinity propagation to numerical similarity measure of pose to perform data abstraction and obtains representative poses of clusters. Second, to construct a meaningful neighborhood for user browsing, it further merges logical similarity measures of pose with the weight quartets and casts the isolated representative poses into a structure of phylogenetic tree. Third, to support online motion exploration including motion ranking and clustering, a biLSTM-based auto-encoder is proposed to encode the high-dimensional pose context into compact latent space. Experimental results on CMU’s motion capture data verify the effectiveness of the proposed method.

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Kawale, Manas, Juhun Lee, Shi Yin Leung, Michelle C. Fingeret, Gregory P. Reece, Melissa A. Crosby, Elisabeth K. Beahm, Mia K. Markey, and Fatima A. Merchant. "3D Symmetry Measure Invariant to Subject Pose during Image Acquisition." Breast Cancer: Basic and Clinical Research 5 (January 2011): BCBCR.S7140. http://dx.doi.org/10.4137/bcbcr.s7140.

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In this study we evaluate the influence of subject pose during image acquisition on quantitative analysis of breast morphology. Three (3D) and two-dimensional (2D) images of the torso of 12 female subjects in two different poses; (1) hands-on-hip (HH) and (2) hands-down (HD) were obtained. In order to quantify the effect of pose, we introduce a new measure; the 3D pBRA (Percentage Breast Retraction Assessment) index, and validate its use against the 2D pBRA index. Our data suggests that the 3D pBRA index is linearly correlated with the 2D counterpart for both of the poses, and is independent of the localization of fiducial points within a tolerance limit of 7 mm. The quantitative assessment of 3D asymmetry was found to be invariant of subject pose. This study further corroborates the advantages of 3D stereophotogrammetry over 2D photography. Problems with pose that are inherent in 2D photographs are avoided and fiducial point identification is made easier by being able to panoramically rotate the 3D surface enabling views from any desired angle.

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Liu, Yuanyuan, Xingmei Li, Fang Fang, Fayong Zhang, Jingying Chen, and Zhizhong Zeng. "Visual Focus of Attention and Spontaneous Smile Recognition Based on Continuous Head Pose Estimation by Cascaded Multi-Task Learning." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 07 (June 7, 2019): 1940006. http://dx.doi.org/10.1142/s0218001419400068.

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Multi-person Visual focus of attention (M-VFOA) and spontaneous smile (SS) recognition are important for persons’ behavior understanding and analysis in class. Recently, promising results have been reported using special hardware in constrained environment. However, M-VFOA and SS remain challenging problems in natural and crowd classroom environment, e.g. various poses, occlusion, expressions, illumination and poor image quality, etc. In this study, a robust and un-invasive M-VFOA and SS recognition system has been developed based on continuous head pose estimation in the natural classroom. A novel cascaded multi-task Hough forest (CM-HF) combined with weighted Hough voting and multi-task learning is proposed for continuous head pose estimation, tip of the nose location and SS recognition, which improves accuracies of recognition and reduces the training time. Then, M-VFOA can be recognized based on estimated head poses, environmental cues and prior states in the natural classroom. Meanwhile, SS is classified using CM-HF with local cascaded mouth-eyes areas normalized by the estimated head poses. The method is rigorously evaluated for continuous head pose estimation, multi-person VFOA recognition, and SS recognition on some public available datasets and real-class video sequences. Experimental results show that our method reduces training time greatly and outperforms the state-of-the-art methods for both performance and robustness with an average accuracy of 83.5% on head pose estimation, 67.8% on M-VFOA recognition and 97.1% on SS recognition in challenging environments.

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Croassacipto, Muhammad, Muhammad Ichwan, and Dina Budhi Utami. "Tone Classification Matches Kodàly Handsign with the K-Nearest Neighbor Method at Leap Motion Controller." International Journal on Information and Communication Technology (IJoICT) 5, no. 2 (June 10, 2020): 40. http://dx.doi.org/10.21108/ijoict.2019.52.283.

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<p>Hands can produce a variety of poses in which each pose can have a meaning or purpose that can be used as a form of communication determined according to a general agreement or who communicate. Hand pose can be used as human interaction with the computer is faster, intuitive, and in line with the natural function of the human body called Handsign. One of them is Kodàly Handsign, made by a Hungarian composer named Zoltán Kodály, which is a concept in music education in Hungary. This hand sign is used in interactive angklung performances in determining the tone that will be played by the K-Nearest Neighbor (KNN) algorithm classification process based on hand poses. This classification process is performed on the extracted data from Leap Motion Controller, which takes Pitch, Roll, and Yaw values based on basic aircraft principle. The results of the research were conducted five times with the value of k periodically 1,3,5,7,9 with test data consisting pose of 874 Do', 702 Si, 913 La, 612 Sol, 661 Fa, 526 Mi, 891 Re, and 1004 Do punctuation on 21099 training data. The test results can recognize hand poses with the optimal k value k=1 with an accuracy level of 94.87%.</p>

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Journal articles on the topic 'Posel'

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