Academic literature on the topic 'Mid-level'

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Journal articles on the topic "Mid-level"

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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.

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Anderson, 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.

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Gallagher, 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.

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A gap exists in understanding organizational career ladders for IT professionals. This gap is especially pronounced in organizations externally sourcing IT related activities to vendors, given that technical positions often feed the pipeline to mid-level roles. Based on prior research and qualitative discussions with key informants, this paper discusses the skills necessary for mid-level roles and proposes individual differences in social skills / traits for several key positions (project manager, senior system analyst, and relationship manager). The authors’ research informs IT professionals as to traits that are most strongly related to success within particular roles. Furthermore, the proposed model and the concept of “fit” will help employers and universities improve coaching and counseling regarding career paths, and contribute to the effectiveness of university curriculum. Finally, this research will inform the types of motivational techniques necessary for organizations to incentivize and achieve results in the form of increased performance, lower turnover, and increased satisfaction.
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Dorn, 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.

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Jalian, 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.

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Sawin, 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.

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Fey, 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.

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Volkov, 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.

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Introduction. The study utilizes communicative approach to management, which emphasizes communicative potential of the leader as an important factor of effective business. Communicative potential is understood as a compound of empathy, affiliation tendency and sensitivity to rejection among mid-level managers. Materials and methods. Communicative potential was measured with set of tools by A. Mehrabian. Study sample included 84 mid-level managers from industrial and trade organizations. All respondents have higher education and are involved in management activities over 5 to 12 subordinates. Respondents were 34 female and 50 male, aged 24 to 45. Management experience of respondents varied from 2 to 5 years. Data analysis methods included descriptive methods and comparison of means.Results. The study revealed insufficient level of communicative potential of mid-level managers compared to successful managers. Comparison of genders and work experience showed that sensitivity to rejection is higher among female managers, and also drops down with experience for both men and women. Empathy is also lower for those who are more experienced.Discussion and Conclusions. Communicative potential of mid-level managers needs to be developed through special communication training. The trends revealed I the study can provide the targets for training programs.
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Raymond, 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.

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Sanchez-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.

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Dissertations / Theses on the topic "Mid-level"

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Trulls, Fortuny Eduard. "Enhancing low-level features with mid-level cues." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/286325.

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Local features have become an essential tool in visual recognition. Much of the progress in computer vision over the past decade has built on simple, local representations such as SIFT or HOG. SIFT in particular shifted the paradigm in feature representation. Subsequent works have often focused on improving either computational efficiency, or invariance properties. This thesis belongs to the latter group. Invariance is a particularly relevant aspect if we intend to work with dense features. The traditional approach to sparse matching is to rely on stable interest points, such as corners, where scale and orientation can be reliably estimated, enforcing invariance; dense features need to be computed on arbitrary points. Dense features have been shown to outperform sparse matching techniques in many recognition problems, and form the bulk of our work. In this thesis we present strategies to enhance low-level, local features with mid-level, global cues. We devise techniques to construct better features, and use them to handle complex ambiguities, occlusions and background changes. To deal with ambiguities, we explore the use of motion to enforce temporal consistency with optical flow priors. We also introduce a novel technique to exploit segmentation cues, and use it to extract features invariant to background variability. For this, we downplay image measurements most likely to belong to a region different from that where the descriptor is computed. In both cases we follow the same strategy: we incorporate mid-level, "big picture" information into the construction of local features, and proceed to use them in the same manner as we would the baseline features. We apply these techniques to different feature representations, including SIFT and HOG, and use them to address canonical vision problems such as stereo and object detection, demonstrating that the introduction of global cues yields consistent improvements. We prioritize solutions that are simple, general, and efficient. Our main contributions are as follows: (a) An approach to dense stereo reconstruction with spatiotemporal features, which unlike existing works remains applicable to wide baselines. (b) A technique to exploit segmentation cues to construct dense descriptors invariant to background variability, such as occlusions or background motion. (c) A technique to integrate bottom-up segmentation with recognition efficiently, amenable to sliding window detectors.
Les "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".
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Tsogkas, Stavros. "Mid-level representations for modeling objects." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC012/document.

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Dans cette thèse, nous proposons l'utilisation de représentations de niveau intermédiaire, et en particulier i) d'axes médians, ii) de parties d'objets, et iii) des caractéristiques convolutionnels, pour modéliser des objets.La première partie de la thèse traite de détecter les axes médians dans des images naturelles en couleur. Nous adoptons une approche d'apprentissage, en utilisant la couleur, la texture et les caractéristiques de regroupement spectral pour construire un classificateur qui produit une carte de probabilité dense pour la symétrie. Le Multiple Instance Learning (MIL) nous permet de traiter l'échelle et l'orientation comme des variables latentes pendant l'entraînement, tandis qu'une variante fondée sur les forêts aléatoires offre des gains significatifs en termes de temps de calcul.Dans la deuxième partie de la thèse, nous traitons de la modélisation des objets, utilisant des modèles de parties déformables (DPM). Nous développons une approche « coarse-to-fine » hiérarchique, qui utilise des bornes probabilistes pour diminuer le coût de calcul dans les modèles à grand nombre de composants basés sur HOGs. Ces bornes probabilistes, calculés de manière efficace, nous permettent d'écarter rapidement de grandes parties de l'image, et d'évaluer précisément les filtres convolutionnels seulement à des endroits prometteurs. Notre approche permet d'obtenir une accélération de 4-5 fois sur l'approche naïve, avec une perte minimale en performance.Nous employons aussi des réseaux de neurones convolutionnels (CNN) pour améliorer la détection d'objets. Nous utilisons une architecture CNN communément utilisée pour extraire les réponses de la dernière couche de convolution. Nous intégrons ces réponses dans l'architecture DPM classique, remplaçant les descripteurs HOG fabriqués à la main, et nous observons une augmentation significative de la performance de détection (~14.5% de mAP).Dans la dernière partie de la thèse nous expérimentons avec des réseaux de neurones entièrement convolutionnels pous la segmentation de parties d'objets.Nous réadaptons un CNN utilisé à l'état de l'art pour effectuer une segmentation sémantique fine de parties d'objets et nous utilisons un CRF entièrement connecté comme étape de post-traitement pour obtenir des bords fins.Nous introduirons aussi un à priori sur les formes à l'aide d'une Restricted Boltzmann Machine (RBM), à partir des segmentations de vérité terrain.Enfin, nous concevons une nouvelle architecture entièrement convolutionnel, et l'entraînons sur des données d'image à résonance magnétique du cerveau, afin de segmenter les différentes parties du cerveau humain.Notre approche permet d'atteindre des résultats à l'état de l'art sur les deux types de données
In 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
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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.

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Ng, 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.

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Albaradei, Somayah. "Learning Mid-Level Features from Object Hierarchy for Image Classification." IEEE, 2014. http://hdl.handle.net/1993/28540.

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One of the most active research areas in computer vision is image classification. Although there have been many research efforts in this area, it remains a difficult problem, especially when the number of categories is large. Most of the previous work in image classification uses low-level image features. We believe low-level features ignore a lot of the semantic structures of the image classes. In this thesis, we go beyond simple low-level features and propose new approaches for constructing mid-level visual features for image classification. We represent an image using the outputs of a collection of binary classifiers. These binary classifiers are trained to differentiate pairs of object classes in an object hierarchy. Our feature representations implicitly capture the hierarchical structure in object classes. We show that our proposed approach outperforms other baseline methods in image classification.
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Flanders, 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.

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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb. 22, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dr. Glenn E. Good, Dissertation Supervisor. Vita. Includes bibliographical references.
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Feng, Tang. "Feature based representations for mid- and high-level vision /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.

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Subirana-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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (p. 215-239).
by J. Brian Subirana-Vilanova.
Ph.D.
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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.

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Mossmyr, 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.

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Self-training with noisy student is a consistency-based semi-supervised self- training method that achieved state-of-the-art accuracy on ImageNet image classification upon its release. It makes use of data noise and model noise when fitting a model to both labelled data and a large amount of artificially labelled data. In this work, we use self- training with noisy student to fit a VGG- style deep CNN model to a dataset of music piece excerpts labelled with perceptual mid-level features and compare its performance with the benchmark. To achieve this, we experiment with some common data warping augmentations and find that pitch shifting, time stretching, and time translation applied on the excerpt spectrograms can improve the model's invariance. We also apply stochastic depth to the VGG-style model — a method which randomly drops entire layers of a model during training—and find that it too can increase model invariance. This is a novel application since stochastic depth has not been used outside the ResNet architecture to our knowledge. Finally, we apply self-training with noisy student with the aforementioned methods as sources of noise and find that it reduces the mean squared error of the testing subset by an impressive amount, although the overall performance of the model can still be questioned.
Självträning 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.
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Books on the topic "Mid-level"

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Coleman, John L. Operational mid-level management for police. 3rd ed. Springfield, Ill., U.S.A: Charles C. Thomas, 2002.

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Operational mid-level management for police. 2nd ed. Springfield, Ill., U.S.A: C.C. Thomas, 1995.

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Operational mid-level management for police. 4th ed. Springfield, Il: Charles C. Thomas, 2012.

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Coleman, John L. Operational mid-level management for police. Springfield, Ill., U.S.A: C.C. Thomas, 1988.

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A, Nicolas. The mid-oceanic ridges: Mountains below sea level. Berlin: Springer, 1995.

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Mid-level management: Leadership as a performing art. Lanham, MD: University Press of America, 1986.

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Food, Ontario Ministry of Agriculture and. Low flow, mid-level stream and ditch crossings with culverts. Toronto, Ont: Ministry of Agriculture and Food, 1992.

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McGonagle, 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.

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Habte, M. Leeann. Mid-level providers in Minnesota's primary care centers: A profile. Minneapolis, Minn: Office of Rural Health, Minnesota Dept. of Health, 1993.

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Mining the middle ground: Developing mid-level managers for strategic change. Boca Raton, Fla: St. Lucie Press, 2001.

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Book chapters on the topic "Mid-level"

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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.

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Borowsky, 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.

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Kahn, 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.

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Singh, 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.

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Ravichandran, 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.

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Förstner, 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.

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Zhang, 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.

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Ringbauer, 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.

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Souza, 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.

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Almirall, 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.

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Conference papers on the topic "Mid-level"

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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.

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Cummings, 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.

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Boureau, 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.

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Weir, 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.

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Jain, 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.

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Lam, 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.

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Zoran, 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.

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Stankiewicz, 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.

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Hassan, 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.

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Lin, 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.

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Reports on the topic "Mid-level"

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Aggarwal, V. Mid-Level Networks Potential Technical Services. RFC Editor, December 1991. http://dx.doi.org/10.17487/rfc1291.

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Johnson, 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.

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Thomas, 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.

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Saenko, 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.

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Theiss, 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.

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Maurice, 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.

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Shoffner, 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.

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Palarca, 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.

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Boyce, 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.

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Mullarkey, 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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