Academic literature on the topic 'Mid-level'
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Journal articles on the topic "Mid-level"
Owens, Victoria F., Tina L. Palmieri, and David G. Greenhalgh. "Mid-Level Providers." Journal of Burn Care & Research 37, no. 2 (2016): 122–26. http://dx.doi.org/10.1097/bcr.0000000000000229.
Full textAnderson, Barton L. "Mid-level vision." Current Biology 30, no. 3 (February 2020): R105—R109. http://dx.doi.org/10.1016/j.cub.2019.11.088.
Full textGallagher, Vickie Coleman, Kevin P. Gallagher, and Kate M. Kaiser. "Mid-Level Information Technology Professionals." International Journal of Social and Organizational Dynamics in IT 3, no. 2 (April 2013): 22–40. http://dx.doi.org/10.4018/ijsodit.2013040102.
Full textDorn, Spencer D. "Mid-Level Providers in Gastroenterology." American Journal of Gastroenterology 105, no. 2 (February 2010): 246–51. http://dx.doi.org/10.1038/ajg.2009.275.
Full textJalian, H. Ray, and Mathew M. Avram. "Mid-Level Practitioners in Dermatology." JAMA Dermatology 150, no. 11 (November 1, 2014): 1149. http://dx.doi.org/10.1001/jamadermatol.2014.1922.
Full textSawin, Kenneth E. "Cell Division: Mid-Level Management." Current Biology 17, no. 3 (February 2007): R93—R95. http://dx.doi.org/10.1016/j.cub.2006.11.054.
Full textFey, Charles J., and D. Stanley Carpenter. "Mid-Level Student Affairs Administrators." NASPA Journal 33, no. 3 (April 1, 1996): 218–31. http://dx.doi.org/10.1080/00220973.1996.11072410.
Full textVolkov, I. V. "Communicative potential of mid-level managers." Vestnik of Minin University 7, no. 4 (December 19, 2019): 10. http://dx.doi.org/10.26795/2307-1281-2019-7-4-10.
Full textRaymond, D., S. Gjorgjievska, S. Sessions, and K. Fuchs. "Tropical cyclogenesis and mid-level vorticity." Australian Meteorological and Oceanographic Journal 64, no. 1 (March 2014): 11–25. http://dx.doi.org/10.22499/2.6401.003.
Full textSanchez-Mendoza, David, David Masip, and Agata Lapedriza. "Emotion recognition from mid-level features." Pattern Recognition Letters 67 (December 2015): 66–74. http://dx.doi.org/10.1016/j.patrec.2015.06.007.
Full textDissertations / Theses on the topic "Mid-level"
Trulls, Fortuny Eduard. "Enhancing low-level features with mid-level cues." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/286325.
Full textLes "features" locals s'han convertit en una eina fonamental en el camp del reconeixement visual. Gran part del progrés experimentat en el camp de la visió per computador al llarg de l'última decada es basa en representacions locals de baixa complexitat, com SIFT o HOG. SIFT, en concret, ha canviat el paradigma en representació de característiques visuals. Els treballs que l'han succeït s'acostumen a centrar o bé a millorar la seva eficiencia computacional, o bé propietats d'invariança. El treball presentat en aquesta tesi pertany al segon grup. L'invariança es un aspecte especialment rellevant quan volem treballab amb "features" denses, és a dir per a cada pixel. La manera tradicional d'atacar el problema amb "features" de baixa densitat consisteix en seleccionar punts d'interés estables, com per exemple cantonades, on l'escala i l'orientació poden ser estimades de manera robusta. Les "features" denses, per definició, han de ser calculades en punts arbitraris de la imatge. S'ha demostrat que les "features" denses obtenen millors resultats en tècniques de correspondència per a molts problemes en reconeixement, i formen la major part del nostre treball. En aquesta tesi presentem estratègies per a enriquir "features" locals de baix nivell amb "cues" o dades globals, de mitja complexitat. Dissenyem tècniques per a construïr millors "features", que usem per a atacar problemes tals com correspondències amb un grau elevat d'ambigüetat, oclusions, i canvis del fons de la imatge. Per a atacar ambigüetats, explorem l'ús del moviment per a imposar consistència espai-temporal mitjançant informació d'"optical flow". També presentem una tècnica per explotar dades de segmentació que fem servir per a extreure "features" invariants a canvis en el fons de la imatge. Aquest mètode consisteix en atenuar els components de la imatge (i per tant les "features") que probablement corresponguin a regions diferents a la del descriptor que estem calculant. En ambdós casos seguim la mateixa estratègia: la nostra voluntat és incorporar dades globals d'un nivell de complexitat mitja a la construcció de "features" locals, que procedim a utilitzar de la mateixa manera que les "features" originals. Aquestes tècniques són aplicades a diferents tipus de representacions, incloent SIFT i HOG, i mostrem com utilitzar-les per a atacar problemes fonamentals en visió per computador tals com l'estèreo i la detecció d'objectes. En aquest treball demostrem que introduïnt informació global en la construcció de "features" locals podem obtenir millores consistentment. Donem prioritat a solucions senzilles, generals i eficients. Aquestes són les principals contribucions de la tesi: (a) Una tècnica per a reconstrucció estèreo densa mitjançant "features" espai-temporals, amb l'avantatge respecte a treballs existents que podem aplicar-la a càmeres en qualsevol configuració geomètrica ("wide-baseline"). (b) Una tècnica per a explotar dades de segmentació dins la construcció de descriptors densos, fent-los invariants a canvis al fons de la imatge, i per tant a problemes com les oclusions en estèreo o objectes en moviment. (c) Una tècnica per a integrar segmentació de manera ascendent ("bottom-up") en problemes de reconeixement d'una manera eficient, dissenyada per a detectors de tipus "sliding window".
Tsogkas, Stavros. "Mid-level representations for modeling objects." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC012/document.
Full textIn this thesis we propose the use of mid-level representations, and in particular i) medial axes, ii) object parts, and iii)convolutional features, for modelling objects.The first part of the thesis deals with detecting medial axes in natural RGB images. We adopt a learning approach, utilizing colour, texture and spectral clustering features, to build a classifier that produces a dense probability map for symmetry. Multiple Instance Learning (MIL) allows us to treat scale and orientation as latent variables during training, while a variation based on random forests offers significant gains in terms of running time.In the second part of the thesis we focus on object part modeling using both hand-crafted and learned feature representations. We develop a coarse-to-fine, hierarchical approach that uses probabilistic bounds for part scores to decrease the computational cost of mixture models with a large number of HOG-based templates. These efficiently computed probabilistic bounds allow us to quickly discard large parts of the image, and evaluate the exact convolution scores only at promising locations. Our approach achieves a $4times-5times$ speedup over the naive approach with minimal loss in performance.We also employ convolutional features to improve object detection. We use a popular CNN architecture to extract responses from an intermediate convolutional layer. We integrate these responses in the classic DPM pipeline, replacing hand-crafted HOG features, and observe a significant boost in detection performance (~14.5% increase in mAP).In the last part of the thesis we experiment with fully convolutional neural networks for the segmentation of object parts.We re-purpose a state-of-the-art CNN to perform fine-grained semantic segmentation of object parts and use a fully-connected CRF as a post-processing step to obtain sharp boundaries.We also inject prior shape information in our model through a Restricted Boltzmann Machine, trained on ground-truth segmentations.Finally, we train a new fully-convolutional architecture from a random initialization, to segment different parts of the human brain in magnetic resonance image data.Our methods achieve state-of-the-art results on both types of data
McGovern, David. "Mid-level Vision : Combining the Outputs from V1." Thesis, University of Nottingham, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523678.
Full textNg, Siu-kan, and 吳少芹. "Diversity of elevated space along Mid-level-escalator." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B47153052.
Full textAlbaradei, Somayah. "Learning Mid-Level Features from Object Hierarchy for Image Classification." IEEE, 2014. http://hdl.handle.net/1993/28540.
Full textFlanders, Melanie Good Glenn E. "Characteristics of effective mid-level leaders in higher education." Diss., Columbia, Mo. : University of Missouri--Columbia, 2008. http://hdl.handle.net/10355/7106.
Full textFeng, Tang. "Feature based representations for mid- and high-level vision /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.
Full textSubirana-Vilanova, J. Brian. "Mid-level vision and recognition of non-rigid objects." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/37708.
Full textIncludes bibliographical references (p. 215-239).
by J. Brian Subirana-Vilanova.
Ph.D.
Lindsay, Adam Taro. "Using contour as a mid-level representation of melody." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/61826.
Full textMossmyr, Simon. "Noisy recognition of perceptual mid-level features in music." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294229.
Full textSjälvträning med störningar är en delvis övervakad självträningsmetod som uppnådde en avsevärd pricksäkerhet på ImageNets bildigenkänningsprov. Den använder sig av dataförstärkning och störningar i modellen när den ska anpassas till en stor mängd artificiellt annoterad träningsdata tillsammans med vanlig träningsdata. I den här uppsatsen så använder vi självträning med störningar för att träna ett VGG-liknande faltningsnätverk med en datamängd av musikstycken annoterade med perceptuella mellanliggande särdrag. För att uppnå detta så börjar vi med att experimentera med dataförstärkning och finner att förändring av tonhöjd, tidsuttöjning och tidsförflyttning (applicerat direkt på musikstyckenas spektrogram) kan öka modellens tolerans för förändringar i datan. Vi experimenterar även med stokastiskt djup — en metod som inaktiverar hela lager av ett neuronnätverk under träning—och finner att detta också kan öka modellens tolerans. Detta är en nyanvändning av stokastiskt djup eftersom metoden såvitt vi känner till inte har använts i annat än varianter av ResNet. Slutligen så använder vi självträning med störningar med de tidigare nämnda metoderna och finner en avsevärd minskning i modellens fel, även om dess övergripande prestanda kan ifrågasättas.
Books on the topic "Mid-level"
Coleman, John L. Operational mid-level management for police. 3rd ed. Springfield, Ill., U.S.A: Charles C. Thomas, 2002.
Find full textOperational mid-level management for police. 2nd ed. Springfield, Ill., U.S.A: C.C. Thomas, 1995.
Find full textOperational mid-level management for police. 4th ed. Springfield, Il: Charles C. Thomas, 2012.
Find full textColeman, John L. Operational mid-level management for police. Springfield, Ill., U.S.A: C.C. Thomas, 1988.
Find full textA, Nicolas. The mid-oceanic ridges: Mountains below sea level. Berlin: Springer, 1995.
Find full textMid-level management: Leadership as a performing art. Lanham, MD: University Press of America, 1986.
Find full textFood, Ontario Ministry of Agriculture and. Low flow, mid-level stream and ditch crossings with culverts. Toronto, Ont: Ministry of Agriculture and Food, 1992.
Find full textMcGonagle, Sara Rusk. Mid-level practitioners: Their role in providing quality health care. Austin, Tex: Policy Research Project on Health Care Cost and Access, Lyndon B. Johnson School of Public Affairs, University of Texas at Austin, 1992.
Find full textHabte, M. Leeann. Mid-level providers in Minnesota's primary care centers: A profile. Minneapolis, Minn: Office of Rural Health, Minnesota Dept. of Health, 1993.
Find full textMining the middle ground: Developing mid-level managers for strategic change. Boca Raton, Fla: St. Lucie Press, 2001.
Find full textBook chapters on the topic "Mid-level"
Lee, Tom, Sanja Fidler, and Sven Dickinson. "Multi-cue Mid-level Grouping." In Computer Vision -- ACCV 2014, 376–90. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16811-1_25.
Full textBorowsky, Peter, Jacob Blanchett, Kyle Pilz, and Eric C. Makhni. "Recruiting and Incorporating Mid-Level Providers." In Orthopedic Practice Management, 43–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96938-1_4.
Full textKahn, Marc J., and Neil Baum. "The Role of Mid-level Providers." In The Business Basics of Building and Managing a Healthcare Practice, 117–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27776-5_17.
Full textSingh, Saurabh, Abhinav Gupta, and Alexei A. Efros. "Unsupervised Discovery of Mid-Level Discriminative Patches." In Computer Vision – ECCV 2012, 73–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33709-3_6.
Full textRavichandran, Avinash, Chaohui Wang, Michalis Raptis, and Stefano Soatto. "SuperFloxels: A Mid-level Representation for Video Sequences." In Computer Vision – ECCV 2012. Workshops and Demonstrations, 131–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_14.
Full textFörstner, Wolfgang. "Mid-Level Vision Processes for Automatic Building Extraction." In Automatic Extraction of Man-Made Objects from Aerial and Space Images, 179–88. Basel: Birkhäuser Basel, 1995. http://dx.doi.org/10.1007/978-3-0348-9242-1_17.
Full textZhang, Qiang, Jinfu Yang, and Shanshan Zhang. "Indoor Scene Classification Based on Mid-Level Features." In Advances in Intelligent Systems and Computing, 235–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38789-5_32.
Full textRingbauer, Stefan, Pierre Bayerl, and Heiko Neumann. "Neural Mechanisms for Mid-Level Optical Flow Pattern Detection." In Lecture Notes in Computer Science, 281–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_29.
Full textSouza, Renato, Raquel Almeida, Roberto Miranda, Zenilton Kleber G. do Patrocinio, Simon Malinowski, and Silvio Jamil F. Guimarães. "BRIEF-Based Mid-Level Representations for Time Series Classification." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 449–57. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33904-3_42.
Full textAlmirall, Esteve, and Jonathan Wareham. "Living Labs: Arbiters of Mid- and Ground-Level Innovation." In Global Sourcing of Information Technology and Business Processes, 233–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15417-1_13.
Full textConference papers on the topic "Mid-level"
Li, Yao, Lingqiao Liu, Chunhua Shen, and Anton van den Hengel. "Mid-level deep pattern mining." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298699.
Full textCummings, Joel, and Deborah Stacey. "The Mid Level Data Collection Ontology (DCO) - Generic Data Collection using a Mid Level Ontology." In 9th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006497501750182.
Full textBoureau, Y.-Lan, Francis Bach, Yann LeCun, and Jean Ponce. "Learning mid-level features for recognition." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539963.
Full textWeir, David H., and Allen J. Clark. "A Survey of Mid-Level Driving Simulators." In International Congress & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1995. http://dx.doi.org/10.4271/950172.
Full textJain, Arpit, Abhinav Gupta, Mikel Rodriguez, and Larry S. Davis. "Representing Videos Using Mid-level Discriminative Patches." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.332.
Full textLam, Vu, Sang Phan, Thanh Duc Ngo, Duy-Dinh Le, Duc Anh Duong, and Shin'ichi Satoh. "Violent scene detection using mid-level feature." In the Fourth Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2542050.2542070.
Full textZoran, Daniel, Phillip Isola, Dilip Krishnan, and William T. Freeman. "Learning Ordinal Relationships for Mid-Level Vision." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.52.
Full textStankiewicz, Olgierd, Marek Domanski, and Krzysztof Wegner. "Stereoscopic depth refinement by mid-level hypothesis." In 2010 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2010. http://dx.doi.org/10.1109/icme.2010.5583541.
Full textHassan, Mahmudul, and Anuja Dharmaratne. "‘Affordance’ detection by mid-level physical parts." In 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2015. http://dx.doi.org/10.1109/ivcnz.2015.7761532.
Full textLin, Angran, Xuhui Jia, and Kowk Ping Chan. "Fast Discovery of Discriminative Mid-level Patches." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005183200530061.
Full textReports on the topic "Mid-level"
Aggarwal, V. Mid-Level Networks Potential Technical Services. RFC Editor, December 1991. http://dx.doi.org/10.17487/rfc1291.
Full textJohnson, Caley, Emily Newes, Aaron Brooker, Robert McCormick, Steve Peterson, Paul Leiby, Rocio Uria Martinez, Gbadebo Oladosu, and Maxwell L. Brown. High-Octane Mid-Level Ethanol Blend Market Assessment. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1351596.
Full textThomas, Scott E. Mid-Level Service Doctrine: Is There a Need? Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada370684.
Full textSaenko, Kate, Ben Packer, C. Chen, S. Bandla, Y. Lee, Yangqing Jia, J. Niebles, et al. Mid-level Features Improve Recognition of Interactive Activities. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada570728.
Full textTheiss, Timothy J., Teresa Alleman, Aaron Brooker, Amgad Elgowainy, Gina Fioroni, Jeongwoo Han, Shean P. Huff, et al. Summary of High-Octane Mid-Level Ethanol Blends Study. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1286966.
Full textMaurice, Craig J. A Study of Army Civilian Entry Level and Mid-Level Program Management Leadership Development. Fort Belvoir, VA: Defense Technical Information Center, May 2016. http://dx.doi.org/10.21236/ad1011617.
Full textShoffner, Brent, Ryan Johnson, Martin J. Heimrich, and Michael Lochte. Powertrain Component Inspection from Mid-Level Blends Vehicle Aging Study. Office of Scientific and Technical Information (OSTI), November 2010. http://dx.doi.org/10.2172/1008841.
Full textPalarca, Christine C. The Relevant Competencies for Mid-Level Navy Nurse Corps Leadership. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada477479.
Full textBoyce, K., and J. T. Chapin. Dispensing Equipment Testing with Mid-Level Ethanol/Gasoline Test Fluid: Summary Report. Office of Scientific and Technical Information (OSTI), November 2010. http://dx.doi.org/10.2172/992805.
Full textMullarkey, David D. Development of an Orientation Program for Mid-level Managers at a Rural Civilian Community Hospital. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada372167.
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