Academic literature on the topic 'Visual search 3D'
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Journal articles on the topic "Visual search 3D"
Lmaati, Elmustapha Ait, Ahmed El Oirrak, and M. N. Kaddioui. "A Visual Similarity-Based 3D Search Engine." Data Science Journal 8 (2009): 78–87. http://dx.doi.org/10.2481/dsj.007-069.
Full textFinlayson, Nonie J., and Philip M. Grove. "Visual search is influenced by 3D spatial layout." Attention, Perception, & Psychophysics 77, no. 7 (May 14, 2015): 2322–30. http://dx.doi.org/10.3758/s13414-015-0924-3.
Full textFinlayson, N., and P. Grove. "Visual search is influenced by 3D spatial layout." Journal of Vision 14, no. 10 (August 22, 2014): 914. http://dx.doi.org/10.1167/14.10.914.
Full textOstrovsky, Y., and P. Sinha. "The role of 3D perspective in visual search." Journal of Vision 1, no. 3 (March 14, 2010): 122. http://dx.doi.org/10.1167/1.3.122.
Full textLi, Chia-Ling, M. Pilar Aivar, Dmitry M. Kit, Matthew H. Tong, and Mary M. Hayhoe. "Memory and visual search in naturalistic 2D and 3D environments." Journal of Vision 16, no. 8 (June 14, 2016): 9. http://dx.doi.org/10.1167/16.8.9.
Full textChristmann, Olivier, Noëlle Carbonell, and Simon Richir. "Visual search in dynamic 3D visualisations of unstructured picture collections." Interacting with Computers 22, no. 5 (September 2010): 399–416. http://dx.doi.org/10.1016/j.intcom.2010.02.005.
Full textBernhard, Matthias, Efstathios Stavrakis, Michael Hecher, and Michael Wimmer. "Gaze-to-Object Mapping during Visual Search in 3D Virtual Environments." ACM Transactions on Applied Perception 11, no. 3 (October 28, 2014): 1–17. http://dx.doi.org/10.1145/2644812.
Full textLago Angel, Miguel Angel, Craig Abbey, and Miguel Eckstein. "Dissociations in ideal and human observer visual search in 3D images." Journal of Vision 18, no. 10 (September 1, 2018): 131. http://dx.doi.org/10.1167/18.10.131.
Full textGhose, Tandra, Aman Mathur, and Rupak Majumdar. "Study of Visual Search in 3D Space using Virtual Reality (VR)." Journal of Vision 18, no. 10 (September 1, 2018): 286. http://dx.doi.org/10.1167/18.10.286.
Full textShen, Helong, Yong Yin, Yongjin Li, and Pengcheng Wang. "Real-time Dynamic Simulation of 3D Cloud for Marine Search and Rescue Simulator." International Journal of Virtual Reality 8, no. 2 (January 1, 2009): 59–63. http://dx.doi.org/10.20870/ijvr.2009.8.2.2725.
Full textDissertations / Theses on the topic "Visual search 3D"
Wu, Hanwei. "Object Ranking for Mobile 3D Visual Search." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175146.
Full textEbri, Mars David. "Multi-View Vocabulary Trees for Mobile 3D Visual Search." Thesis, KTH, Kommunikationsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-162268.
Full textBai, Hequn. "Mobile 3D Visual Search based on Local Stereo Image Features." Thesis, KTH, Ljud- och bildbehandling, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102603.
Full textMcIntire, John Paul. "Visual Search Performance in a Dynamic Environment with 3D Auditory Cues." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1175611457.
Full textPuglia, Luca. "About the development of visual search algorithms and their hardware implementations." Doctoral thesis, Universita degli studi di Salerno, 2017. http://hdl.handle.net/10556/2570.
Full textThe main goal of my work is to exploit the benefits of a hardware implementation of a 3D visual search pipeline. The term visual search refers to the task of searching objects in the environment starting from the real world representation. Object recognition today is mainly based on scene descriptors, an unique description for special spots in the data structure. This task has been implemented traditionally for years using just plain images: an image descriptor is a feature vector used to describe a position in the images. Matching descriptors present in different viewing of the same scene should allows the same spot to be found from different angles, therefore a good descriptor should be robust with respect to changes in: scene luminosity, camera affine transformations (rotation, scale and translation), camera noise and object affine transformations. Clearly, by using 2D images it is not possible to be robust with respect to the change in the projective space, e.g. if the object is rotated with respect to the up camera axes its 2D projection will dramatically change. For this reason, alongside 2D descriptors, many techniques have been proposed to solve the projective transformation problem using 3D descriptors that allow to map the shape of the objects and consequently the surface real appearance. This category of descriptors relies on 3D Point Cloud and Disparity Map to build a reliable feature vector which is invariant to the projective transformation. More sophisticated techniques are needed to obtain the 3D representation of the scene and, if necessary, the texture of the 3D model and obviously these techniques are also more computationally intensive than the simple image capture. The field of 3D model acquisition is very broad, it is possible to distinguish between two main categories: active and passive methods. In the active methods category we can find special devices able to obtain 3D information projecting special light and. Generally an infrared projector is coupled with a camera: while the infrared light projects a well known and fixed pattern, the camera will receive the information of the patterns reflection on a certain surface and the distortion in the pattern will give the precise depth of every point in the scene. These kind of sensors are of i i “output” — 2017/6/22 — 18:23 — page 3 — #3 i i i i i i 3 course expensive and not very efficient from the power consumption point of view, since a lot of power is wasted projecting light and the use of lasers also imposes eye safety rules on frame rate and transmissed power. Another way to obtain 3D models is to use passive stereo vision techniques, where two (or more) cameras are required which only acquire the scene appearance. Using the two (or more) images as input for a stereo matching algorithm it is possible to reconstruct the 3D world. Since more computational resources will be needed for this task, hardware acceleration can give an impressive performance boost over pure software approach. In this work I will explore the principal steps of a visual search pipeline composed by a 3D vision and a 3D description system. Both systems will take advantage of a parallelized architecture prototyped in RTL and implemented on an FPGA platform. This is a huge research field and in this work I will try to explain the reason for all the choices I made for my implementation, e.g. chosen algorithms, applied heuristics to accelerate the performance and selected device. In the first chapter we explain the Visual Search issues, showing the main components required by a Visual Search pipeline. Then I show the implemented architecture for a stereo vision system based on a Bio-informatics inspired approach, where the final system can process up to 30fps at 1024 × 768 pixels. After that a clever method for boosting the performance of 3D descriptor is presented and as last chapter the final architecture for the SHOT descriptor on FPGA will be presented. [edited by author]
L’obiettivo principale di questo lavoro e’ quello di esplorare i benefici di una implementazione hardware per una pipeline di visual search 3D. Il termine visual search si riferisce al problema di ricerca di oggetti nell’ambiente. L’object recognition ai giorni nostri e’ principalmente basato sull’uso di descrittori della scena, una descrizione univoca per i punti salienti. Questo compito e’ stato implementato per anni utilizzando immagini: il descrittore di un punto dell’immagine e’ un semplice vettore di caratteristiche. Accoppiando i descrittori presenti in differenti viste della stessa scena permette di trovare punti nello spazio visibili da entrambe le viste. Chiaramente, utilizzando immagini 2D non e’ possibile avere descrittori che sono robusti a cambiamenti della prospettiva, per questo motivo, molte tecniche sono state proposte per risolvere questo problema utilizzando descrittori 3D. Questa categoria di descrittori si avvale di 3D point cloud e mappe di disparita’. Ovviamente tecniche piu’ sofisticate sono necessarie per ottenere la rappresentazione 3D della scena. Il campo dell’acquisizione 3D e’ molto vasto ed e’ possibile distinguere tra due categorie di sensori: sensori attivi e passivi. Tra i sensori attivi possiamo annoverare dispositivi in grado di proiettare un pattern di luce infrarossa sulla scena, questo pattern noto presenta delle variazioni dovute agli oggetti presenti nella scena. Una camera infrarossi riceve l’immagine distorta del pattern e deduce la geometria della scena. Questo tipo di dispositivi non sono molto efficienti dal punto di vista energetico dato che un sacco di corrente viene consumata per proiettare il pattern. Un altro modo per ottenere un modello 3D e’ quello di usare sensori passivi, una coppia di telecamere puo’ essere utilizzata per ottenere informazioni utilizzando metodi di triangolazione. Questi metodi pero’ richiedono un sacco di potenza computazionale nel caso di applicazioni real time, per questo motivo e’ necessario utilizzare dispositivi ad-hoc quali architetture hardware dedicate implementate mediante l’uso di FPGA e ASIC. In questo lavoro ho esplorato gli step principali di una pipeline per la visual search composta da un sistema di visione 3D e uno per la descrizione di punti. Entrambi i sistemi si avvalgono di achitetture hardware dedicate prototipate in RTL e implementate su FPGA. Questo e’ un grosso campo di lavoro e provo ad esplorare i benefici di una implementazione harwadere per l’accelerazione degli algoritmi stessi e il risparmi di energia elettrica. [a cura dell'autore]
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Andersson, Ulrika. "Effect of depth cues on visual search in a web-based environment." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204465.
Full textChiu, Mi-chun, and 邱米淳. "Visual Search in Dynamic 3D Picture Collections." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/91038244916483938767.
Full text國立雲林科技大學
工業設計系碩士班
100
The purpose of this paper is to discuss the appropriate speed of dynamic 3D collections, and participant’s performance. The research is divided into two stages. The purpose of the first stage is to discuss the appropriate speed of two dynamic 3D collections. In the second stages, picture collections group by their dominant colors and form. The speed of picture collection revolve is the result of first stage. The aim is assess the possible effects on visual search efficiency by comparing participant’s performance. According to debriefings, the picture collections revolve speed not only has effect on efficiency, buy also make user feel tired and lose confidence. The group modus have effect on participant’s performance. The task grouped by form and organized on OV interface has the best performance. The picture collecting grouped by form could improve performance. Organized on IV interface could improve performance while pictures no grouped and the dominate color of pictures are evident.
Book chapters on the topic "Visual search 3D"
Sukno, Federico M., John L. Waddington, and Paul F. Whelan. "Comparing 3D Descriptors for Local Search of Craniofacial Landmarks." In Advances in Visual Computing, 92–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33191-6_10.
Full textYi, Zili, Yang Li, and Minglun Gong. "An Efficient Algorithm for Feature-Based 3D Point Cloud Correspondence Search." In Advances in Visual Computing, 485–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_44.
Full textKondarattsev, Vadim L., Alexander Yu Kryuchkov, and Roman M. Chumak. "3D Object Classification, Visual Search from RGB-D Data." In Applied Mathematics and Computational Mechanics for Smart Applications, 353–75. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4826-4_24.
Full textMoustakas, Konstantinos, G. Stavropoulos, and Dimitrios Tzovaras. "Protrusion Fields for 3D Model Search and Retrieval Based on Range Image Queries." In Advances in Visual Computing, 610–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_58.
Full textCaunce, Angela, Chris Taylor, and Tim Cootes. "Adding Facial Actions into 3D Model Search to Analyse Behaviour in an Unconstrained Environment." In Advances in Visual Computing, 132–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_13.
Full textZhang, Yunhong, Ruifeng Yu, Lei Feng, and Xin Wu. "A Comparative Study on 3D/2D Visual Search Performance on Different Visual Display Terminal." In Advances in Neuroergonomics and Cognitive Engineering, 233–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41691-5_20.
Full textBanchs, Rafael E. "A Comparative Evaluation of 2D And 3D Visual Exploration of Document Search Results." In Information Retrieval Technology, 100–111. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12844-3_9.
Full textSchoeffmann, Klaus, David Ahlström, and Laszlo Böszörmenyi. "A User Study of Visual Search Performance with Interactive 2D and 3D Storyboards." In Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation, 18–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37425-8_2.
Full textvan Schooten, Boris W., Betsy van Dijk, Avan Suinesiaputra, Anton Nijholt, and Johan H. C. Reiber. "Evaluating Visualisations and Automatic Warning Cues for Visual Search in Vascular Images." In Cognitively Informed Intelligent Interfaces, 68–83. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1628-8.ch005.
Full textAndreas Lauterbach, Helge, and Andreas Nüchter. "Aerial 3D Mapping with Continuous Time ICP for Urban Search and Rescue." In Autonomous Mobile Mapping Robots [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108260.
Full textConference papers on the topic "Visual search 3D"
Schoeffmann, Klaus, David Ahlstrom, and Laszlo Boszormenyi. "3D Storyboards for Interactive Visual Search." In 2012 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2012. http://dx.doi.org/10.1109/icme.2012.62.
Full textPetrelli, Alioscia, Danilo Pau, Emanuele Plebani, and Luigi Di Stefano. "RGB-D Visual Search with Compact Binary Codes." In 2015 International Conference on 3D Vision (3DV). IEEE, 2015. http://dx.doi.org/10.1109/3dv.2015.17.
Full textWu, Hanwei, Haopeng Li, and Markus Flierl. "An embedded 3D geometry score for mobile 3D visual search." In 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2016. http://dx.doi.org/10.1109/mmsp.2016.7813366.
Full textRasouli, Amir, and John K. Tsotsos. "Sensor Planning for 3D Visual Search with Task Constraints." In 2016 13th Conference on Computer and Robot Vision (CRV). IEEE, 2016. http://dx.doi.org/10.1109/crv.2016.11.
Full textGifford, Howard C. "Tests of a 3D visual-search model observer for SPECT." In SPIE Medical Imaging, edited by Craig K. Abbey and Claudia R. Mello-Thoms. SPIE, 2013. http://dx.doi.org/10.1117/12.2008073.
Full textJaballah, Sami, Mohamed-Chaker Larabi, and Jamel Belhadj Tahar. "Heuristic inspired search method for fast wedgelet pattern decision in 3D-HEVC." In 2016 6th European Workshop on Visual Information Processing (EUVIP). IEEE, 2016. http://dx.doi.org/10.1109/euvip.2016.7764607.
Full textLi, Haopeng, and Markus Flierl. "Mobile 3D visual search using the Helmert transformation of stereo features." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738716.
Full textBernhard, Matthias, Efstathios Stavrakis, Michael Hecher, and Michael Wimmer. "Gaze-to-object mapping during visual search in 3D virtual environments." In the ACM Symposium. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2628257.2656419.
Full textYan, Yan, and Lin Kunhui. "3D Visual Design for Mobile Search Result on 3G Mobile Phone." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.489.
Full textYoshida, Syunsuke, Makoto Sei, Akira Utsumi, and Hirotake Yamazoe. "Preliminary analysis of effective assistance timing for iterative visual search tasks using gaze-based visual cognition estimation." In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2022. http://dx.doi.org/10.1109/vrw55335.2022.00179.
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