Academic literature on the topic 'Da xue sheng- sheng huo'

Traditional Chinese medicine (TCM) divides fracture treatment into three stages. Many TCM herbs and formulas have been used to treat fractures for thousands of years. However, research regarding the Chinese herbal products (CHPs) that should be used at different periods of treatment is still lacking. This study aims to identify the CHPs that should be used at different periods of treatment as well as confirm the TCM theory of fracture periods medicine. We used prescriptions of TCM outpatients with fracture diagnoses analyzed using the Chang Gung Research Database (CGRD) from 2000 to 2015. According to the number of days between the date of the fracture and the clinic visit date, all patients were assigned to one of three groups. Patients with a date gap of 0-13 days were assigned to the early period group; those with a date gap of 14-82 days were assigned to the middle period group; and those with a date gap of 83-182 days were assigned to the late period group. We observed the average number of herbal formulas prescribed by the TCM doctor at each visit was 2.78, and the average number of single herbs prescribed was 6.47. The top three prescriptions in the early fracture period were Zheng-gu-zi-jin-dang, Shu-jing-huo-xue-tang, and Wu-ling-san. In the middle fracture period, the top three formulas were Zheng-gu-zi-jin-dang, Shu-jing-huo-xue-tang, and Zhi-bai-di-huang-wan. In the late fracture period, the top three formulas were Shu-jing-huo-xue-tang, Gui-lu-er-xian-jiao, and Du-huo-ji-sheng-tang. The main single herbs used in the early fracture period were Yan-hu-suo, Gu-sui-bu, and Dan-shen. From the middle to the late period, the most prescribed single herbs were Xu-duan, Gu-sui-bu, and Yan-hu-suo. We concluded that the results showed that the CGRD utilization pattern roughly meets the TCM theory at different fracture periods.

ObjectivesPatients with type 2 diabetes have a higher risk of colorectal cancer (CRC), but whether Chinese herbal medicines (CHMs) can reduce this risk is unknown. This study investigated the effect that CHMs have on CRC risk in patients with type 2 diabetes.Research design and methodsThis cohort study used the Taiwanese National Health Insurance Research Database to identify 54 744 patients, newly diagnosed with type 2 diabetes, aged 20–70 years, who were receiving treatment between 1998 and 2007. From this sample, we randomly selected 14 940 CHMs users and 14 940 non-CHMs users, using propensity scores matching. All were followed through 2012 to record CRC incidence. Cox proportional hazards regression was used to compute the hazard ratio (HR) of CRC by CHMs use.ResultsDuring follow-up, 235 CHMs users and 375 non-CHMs users developed CRC, incidence rates of 1.73% and 2.47% per 1000 person-years, respectively. CHM users had a significantly reduced risk of CRC compared with non-CHM users (adjusted HR=0.71; 95% CI 0.60 to 0.84). The greatest effect was in those receiving CHMs for more than 1 year. Huang-Qin, Xue-Fu-Zhu-Yu-Tang, Shu-Jing-Huo-Xue-Tang, Liu-Wei-Di-Huang-Wan, Ji-Sheng-Shen-Qi-Wan, Gan-Lu-Yin, Shao-Yao-Gan-Cao-Tang and Ban-Xia-Xie-Xin-Tang were significantly associated with lower risk of CRC.ConclusionIntegrating CHMs into the clinical management of patients with type 2 diabetes may be beneficial in reducing the risk of CRC.

Objetivo: Esse estudo objetivou investigar percepções de estudantes de Odontologia quanto ao medo e à ansiedade em relação ao manejo de pacientes e ao risco de infecção por COVID-19. Materiais e métodos: Esse estudo transversal envolveu todos os alunos regularmente matriculados em Odontologia, no primeiro semestre de 2020, da Universidade Federal de Pelotas. Um questionário foi aplicado, coletando dados demográficos, nível de formação e perguntas relacionadas ao medo e ansiedade frente à pandemia de COVID-19. Quatro comparações de acordo com a fase da graduação (fase pré-clínica ou clínica), nível de graduação e pós-graduação e de acordo com os sexos foram feitas. Análises independentes para as comparações entre os sexos foram realizadas para os alunos de graduação e de pós-graduação (α<5%). Resultados: Foram incluídos 408 estudantes. Na graduação, mulheres relataram sentirem-se mais ansiosas ao realizar tratamento em pacientes com suspeita de COVID-19 (54%) e sentem mais medo ao ouvir que a infecção tem causado mortes (92,4%), na pós-graduação, responderam ser mais nervosas para conversar com pacientes em ambientes fechados em comparações com homens (P<0,05). Alunos em fase pré-clínica possuem significativamente menor receio (65,5%), ansiedade (32,3%) e nervosismo (28,3%) do contágio do COVID-19 quando comparados com aqueles na fase clínica. Conclusões: Mulheres e alunos na fase clínica apresentam maior ansiedade e nervosismo. Descritores: Ansiedade; Estudantes de Odontologia; Medo; Infecções por Coronavírus. Referências Chang J, Yuan Y, Wang D. [Mental health status and its influencing factors among college students during the epidemic of COVID-19]. Nan Fang Yi Ke Da Xue Xue Bao. 2020;40(2):171-176. World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19- 11 March 2020. 2020. Disponível em: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020. Acesso em: 8 de novembro de 2020. Pascarella G, Strumia A, Piliego C, Bruno F, Del Buono R, Costa F, Scarlata S, Agrò FE. COVID-19 diagnosis and management: a comprehensive review. J Intern Med. 2020;288(2):192-206. Chen E, Lerman K, Ferrara E. Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health Surveill. 2020;6(2):e19273. Iyer P, Aziz K, Ojcius DM. Impact of COVID-19 on dental education in the United States. J Dent Educ. 2020;84(6):718-722. Meng L, Hua F, Bian Z. Coronavirus Disease 2019 (COVID-19): Emerging and Future Challenges for Dental and Oral Medicine. J Dent Res. 2020;99(5):481-487. Peng X, Xu X, Li Y, Cheng L, Zhou X, Ren B. Transmission routes of 2019-nCoV and controls in dental practice. Int J Oral Sci. 2020;12(1):9. Machado RA, Bonan PRF, Perez DEDC, Martelli Júnior H. COVID-19 pandemic and the impact on dental education: discussing current and future perspectives. Braz Oral Res. 2020;34:e083. Ataş O, Talo Yildirim T. Evaluation of knowledge, attitudes, and clinical education of dental students about COVID-19 pandemic. PeerJ. 2020;8:e9575. Deery C. The COVID-19 pandemic: implications for dental education. Evid Based Dent. 2020;21(2):46-47. Basudan S, Binanzan N, Alhassan A. Depression, anxiety and stress in dental students. Int J Med Educ. 2017;8:179-186. Elani HW, Allison PJ, Kumar RA, Mancini L, Lambrou A, Bedos C. A systematic review of stress in dental students. J Dent Educ. 2014; 78(2):226-42. Sahu P. Closure of Universities Due to Coronavirus Disease 2019 (COVID-19): Impact on Education and Mental Health of Students and Academic Staff. Cureus. 2020;12(4):e7541. Ahmed MA, Jouhar R, Ahmed N, Adnan S, Aftab M, Zafar MS, Khurshid Z. Fear and Practice Modifications among Dentists to Combat Novel Coronavirus Disease (COVID-19) Outbreak. Int J Environ Res Public Health. 2020;17(8):2821. Talevi D, Socci V, Carai M, Carnaghi G, Faleri S, Trebbi E, di Bernardo A, Capelli F, Pacitti F. Mental health outcomes of the CoViD-19 pandemic. Riv Psichiatr. 2020;55(3):137-44. Mijiritsky E, Hamama-Raz Y, Liu F, Datarkar AN, Mangani L, Caplan J, Shacham A, Kolerman R, Mijiritsky O, Ben-Ezra M, Shacham M. Subjective Overload and Psychological Distress among Dentists during COVID-19. Int J Environ Res Public Health. 2020;17:5074. Rymarowicz J, Stefura T, Major P, Szeliga J, Wallner G, Nowakowski M, Pędziwiatr M. General surgeons' attitudes towards COVID-19: A national survey during the SARS-CoV-2 virus outbreak. Eur Surg. 2020;1-6. Adams JG, Walls RM. Supporting the Health Care Workforce During the COVID-19 Global Epidemic. JAMA. 2020;323(15):1439-40. Naz N, Iqbal S, Mahmood A. Stress, anxiety and depression among the dental students of university college of medicine and dentistry Lahore; Pakistan. Pak J Med Health Sci. 2017;11(4):1277-81. Waqas A, Iftikhar A, Malik Z, Aedma KK, Meraj H, Naveed S. Association of severity of depressive symptoms with sleep quality, social support and stress among Pakistani medical and dental students: A cross-sectional study. Global Psychiatry. 2019;2(2):211-20. Wang Y, Di Y, Ye J, Wei W. Study on the public psychological states and its related factors during the outbreak of coronavirus disease 2019 (COVID-19) in some regions of China. Psychol Health Med. 2020;1-10. Xiong J, Lipsitz O, Nasri F, Lui LMW, Gill H, Phan L, Chen-Li D, Iacobucci M, Ho R, Majeed A, McIntyre RS. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J Affect Disord. 2020;277:55-64. Liu N, Zhang F, Wei C, Jia Y, Shang Z, Sun L, Wu L, Sun Z, Zhou Y, Wang Y, Liu W. Prevalence and predictors of PTSS during COVID-19 outbreak in China hardest-hit areas: Gender differences matter. Psychiatry Res. 2020;287;112921. Terán E, Mayta-Tovalino F. Risk Factors, Self-perceived Stress, and Clinical Training among Dentistry Students in Peru: A Cross-sectional Study. J Contemp Dent Pract. 2019;20(5):561-5. Uraz A, Tocak YS, Yozgatligil C, Cetiner S, Bal B. Psychological well-being, health, and stress sources in Turkish dental students. J Dent Educ. 2013:77(10):1345-55. Agius AM, Gatt G, Vento Zahra E, Busuttil A, Gainza-Cirauqui ML, Cortes ARG et al. Self-reported dental student stressors and experiences during the COVID-19 pandemic. J Dent Educ. 2020. doi: 10.1002/jdd.12409. Hu J, Zou H, Dai Y, Feng Z. How to keep students engaged in oral health education during the COVID-19 pandemic. J Dent Educ. 2020. doi: 10.1002/jdd.12420. Liu S, Yang L, Zhang C, Xiang YT, Liu Z, Hu S, Zhang B. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatry. 2020;7(4):e17-8. Maia BR, Dias PC. Anxiety, depression and stress in university students: the impact of COVID-19. Estudos de Psicologia (Campinas). 2020;37:e200067.

Introdução: a candidose oral é uma doença oportunista que acomete principalmente pacientes imunossuprimidos e usuários de prótese dentária total. Apesar da eficácia terapêutica dos antifúngicos utilizados atualmente para o tratamento desta doença, essas drogas apresentam muitos efeitos adversos relacionados à dose. Além disso, tem-se observado um aumento da resistência microbiana para esses agentes. Baseado nisso, uma fonte de busca promissora de princípios ativos alternativos para o tratamento da candidose oral são os produtos naturais. Objetivo: revisar a literatura sobre ensaios clínicos com produtos naturais para o tratamento da candidose oral, a fim de responder se existe evidência que os produtos naturais podem ser utilizados como tratamento alternativo para esta doença. Material e método: foi realizada uma síntese de todos os ensaios clínicos com produtos naturais para tratamento da candidose indexados banco de dados Pubmed. Resultado: seguindo os critérios de inclusão e exclusão entraram nesta revisão 6 estudos. Os estudos avaliaram a eficácia dos produtos naturais quanto aos parâmetros clínicos e micológicos e grau de satisfação do paciente. Conclusão: apesar da pouca quantidade de ensaios clínicos nesta temática, há evidência de que os produtos naturais podem ser usdos para tratar a candidose oral, especialmente alho, mamona, româ e melaleuca.Descritores: Produtos Biológicos; Candida; Imunidade.ReferênciasHani U, Shivakumar HG, Vaghela R, Osmani RAM, Shrivastava A. Candidiasis: A fungal infection- current challenges and progress in prevention and treatment. Infect Disorders Drug Targets. 2015;15(1):42-52.Pereira CA, Domingues N, Araújo MI, Junqueira JC, Back-Brito GN, Jorge AO. Production of virulence factors in Candida strains isolated from patients with denture stomatitis and control individuals. Diagn Microbiol Infect Dis. 2016;85(1):66-72.Patil S, Rao RS, Majumdar B, Anil S. Clinical appearance of oral candida infection and therapeutic strategies. Front Microbiol. 2015;6:1391.Dos Santos AL. HIV aspartyl protease inhibitors as promising compounds against Candida albicans. World J Biol Chem.2010;1(2):21-30.Shao LC, Sheng CQ, Zhang WN. Recent advances in the study of antifungal lead compounds with new chemical scaffolds. Yao Xue Xue Bao. 2007;42(11):1129-36.Kullberg BJ, Arendrup MC. Invasive candidiasis. N Engl J Med. 2015. 373:1445-56.Williams D, Lewis M. Pathogenesis and treatment of oral candidosis. J Oral Microbiol. 2011;3:5771. doi: 10.3402/jom.v3i0.5771.Kuriyama T, Williams DW, Bagg J, Couçter WA, Ready D, Lewis MAO. In vitro susceptibility of oral Candida to seven antifungal agents. Oral Microbiol Immunol. 2005;20(6):349-53.Wong SS, Samaranayake LP, Andseneviratne CJ. In pursuit of the ideal antifungal agent for Candida infections: high-throughput screening of small molecules. Drug Discov Today. 2014;19(11):1721-30.Jandourek A, Vaishampayan JK, Vazquez JA. Efficacy of melaleuca oral solution for the treatment of fluconazole refractory oral candidiasis in AIDS patients. AIDS. 1998;12(9):1033-37.Vasconcelos LC, Sampaio MC, Sampaio FC, Higino JS. Use of Punica granatum as an antifungal agent against candidosis associated with denture stomatitis. Mycoses. 2003;46(5-6):192-96.Catalán A, Pacheco JG, Martínez A, Mondaca MA. In vitro and in vivo activity of Melaleuca alternifolia mixed with tissue conditioner on Candida albicans. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;105(3):327-32.Wright SC, Maree JE, Sibanyoni M. Treatment of oral thrush in HIV/AIDS patients with lemon juice and lemon grass (Cymbopogon citratus) and gentian violet. Phytomedicine. 2009; 16(2/3):118-24.Bakhshi M, Taheri JB, Shabestari SB, Tanik A, Pahlevan R. Comparison of therapeutic effect of aqueous extract of garlic and nystatin mouthwash in denture stomatitis. Gerodontology. 2012;29(2):e680-84.Pinelli LA, Montandon AA, Corbi SC, Moraes TA, Fais LM. Ricinus communis treatment of denture stomatitis in institutionalised elderly. J Oral Rehabil. 2013;40(5):375-80.Pai MB, Prashant GM, Murlikrishna KS, Shivakumar KM, Chandu GN. Antifungal efficacy of Punica granatum, Acacia nilotica, Cuminum cyminum and Foeniculum vulgare on Candida albicans: an in vitro study. Indian J Dent Res. 2010;21(3):334-36.Höfling JF, Anibal PC, Obando-Pereda GA, Peixoto IA, Furletti VF, Foglio MA et al. Antimicrobial potential of some plant extracts against Candida species. Braz J Biol. 2010;70(4):1065-68.Doddanna SJ, Patel S, Sundarrao MA, Veerabhadrappa RS. Antimicrobial activity of plant extracts on Candida albicans: an in vitro study. Indian J Dent Res. 2013;24(4):401-5.Maubon D, Garnaud C, Calandra T, Sanglard D, Cornet M. Resistance of Candida spp. to antifungal drugs in the ICU: where are we now? Intensive Care Med. 2014; 40(9):1241-55.Alexander BD, Johnson MD, Pfeiffer CD, Jiménez-Ortigosa C, Catania J, Booker R et al. Increasing echinocandin resistance in Candida glabrata: clinical failure correlates with presence of FKS mutations and elevated minimum inhibitory concentrations. Clin Infect Dis. 2013;56(12):1724-32.Shields RK, Nguyen MH, Press EG, Updike CL, Clancy CJ. Caspofungin MICs correlate with treatment outcomes among patients with Candida glabrata invasive candidiasis and prior echinocandin exposure. Antimicrob Agents Chemother. 2013;57(8):3528-35.

Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and retrieval. It can be seen as a two-phase process: object detection and segmentation. Object segmentation becomes more challenging in case there is no prior knowledge about the object in the scene. In such conditions, visual attention analysis via saliency mapping may offer a mean to predict the object location by using visual contrast, local or global, to identify regions that draw strong attention in the image. However, in such situations as clutter background, highly varied object surface, or shadow, regular and salient object segmentation approaches based on a single image feature such as color or brightness have shown to be insufficient for the task. This work proposes a new salient object segmentation method which uses a depth map obtained from the input image for enhancing the accuracy of saliency mapping. A deep learning-based method is employed for depth map estimation. Our experiments showed that the proposed method outperforms other state-of-the-art object segmentation algorithms in terms of recall and precision. KeywordsSaliency map, Depth map, deep learning, object segmentation References[1] Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on pattern analysis and machine intelligence 20(11) (1998) 1254-1259.[2] Goferman, L. Zelnik-Manor, A. Tal, Context-aware saliency detection, IEEE transactions on pattern analysis and machine intelligence 34(10) (2012) 1915-1926.[3] Kanan, M.H. Tong, L. Zhang, G.W. Cottrell, Sun: Top-down saliency using natural statistics, Visual cognition 17(6-7) (2009) 979-1003.[4] Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, H.-Y. Shum, Learning to detect a salient object, IEEE Transactions on Pattern analysis and machine intelligence 33(2) (2011) 353-367.[5] Perazzi, P. Krähenbühl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 733-740.[6] M. Cheng, N.J. Mitra, X. Huang, P.H. Torr, S.M. Hu, Global contrast based salient region detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3) (2015) 569-582.[7] Borji, L. Itti, State-of-the-art in visual attention modeling, IEEE transactions on pattern analysis and machine intelligence 35(1) (2013) 185-207.[8] Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034.[9] Li, Y. Yu, Visual saliency based on multiscale deep features, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5455-5463.[10] Liu, J. Han, Dhsnet: Deep hierarchical saliency network for salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 678-686.[11] Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned saliency detection model, CVPR: Proc IEEE, 2009, pp. 1597-604.Fu, J. Cheng, Z. Li, H. Lu, Saliency cuts: An automatic approach to object segmentation, in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 2008, pp. 1-4Borenstein, J. Malik, Shape guided object segmentation, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1, IEEE, 2006, pp. 969-976.Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, S. Li, Automatic salient object segmentation based on context and shape prior., in: BMVC. 6 (2011) 9.Ciptadi, T. Hermans, J.M. Rehg, An in depth view of saliency, Georgia Institute of Technology, 2013.Desingh, K.M. Krishna, D. Rajan, C. Jawahar, Depth really matters: Improving visual salient region detection with depth., in: BMVC, 2013.Li, J. Ye, Y. Ji, H. Ling, J. Yu, Saliency detection on light field, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2806-2813.Koch, S. Ullman, Shifts in selective visual attention: towards the underlying neural circuitry, in: Matters of intelligence, Springer, 1987, pp. 115-141.Laina, C. Rupprecht, V. Belagiannis, F. Tombari, N. Navab, Deeper depth prediction with fully convolutional residual networks, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 239-248.Bruce, J. Tsotsos, Saliency based on information maximization, in: Advances in neural information processing systems, 2006, pp. 155-162.Ren, X. Gong, L. Yu, W. Zhou, M. Ying Yang, Exploiting global priors for rgb-d saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 25-32.Fang, J. Wang, M. Narwaria, P. Le Callet, W. Lin, Saliency detection for stereoscopic images., IEEE Trans. Image Processing 23(6) (2014) 2625-2636.Hou, L. Zhang, Saliency detection: A spectral residual approach, in: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE, 2007, pp. 1-8.Guo, Q. Ma, L. Zhang, Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, in: Computer vision and pattern recognition, 2008. cvpr 2008. ieee conference on, IEEE, 2008, pp. 1-8.Fang, W. Lin, B.S. Lee, C.T. Lau, Z. Chen, C.W. Lin, Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum, IEEE Transactions on Multimedia 14(1) (2012) 187-198.Lang, T.V. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, S. Yan, Depth matters: Influence of depth cues on visual saliency, in: Computer vision-ECCV 2012, Springer, 2012, pp. 101-115.Zhang, G. Jiang, M. Yu, K. Chen, Stereoscopic visual attention model for 3d video, in: International Conference on Multimedia Modeling, Springer, 2010, pp. 314-324.Wang, M.P. Da Silva, P. Le Callet, V. Ricordel, Computational model of stereoscopic 3d visual saliency, IEEE Transactions on Image Processing 22(6) (2013) 2151-2165.Peng, B. Li, W. Xiong, W. Hu, R. Ji, Rgbd salient object detection: A benchmark and algorithms, in: European Conference on Computer Vision (ECCV), 2014, pp. 92-109.Wu, L. Duan, L. Kong, Rgb-d salient object detection via feature fusion and multi-scale enhancement, in: CCF Chinese Conference on Computer Vision, Springer, 2015, pp. 359-368.Xue, Y. Gu, Y. Li, J. Yang, Rgb-d saliency detection via mutual guided manifold ranking, in: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, 2015, pp. 666-670.Katz, A. Adler, Depth camera based on structured light and stereo vision, uS Patent App. 12/877,595 (Mar. 8 2012).Chatterjee, G. Molina, D. Lelescu, Systems and methods for determining depth from multiple views of a scene that include aliasing using hypothesized fusion, uS Patent App. 13/623,091 (Mar. 21 2013).Matthies, T. Kanade, R. Szeliski, Kalman filter-based algorithms for estimating depth from image sequences, International Journal of Computer Vision 3(3) (1989) 209-238.Y. Schechner, N. Kiryati, Depth from defocus vs. stereo: How different really are they?, International Journal of Computer Vision 39(2) (2000) 141-162.Delage, H. Lee, A.Y. Ng, A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2, IEEE, 2006, pp. 2418-2428.Saxena, M. Sun, A.Y. Ng, Make3d: Learning 3d scene structure from a single still image, IEEE transactions on pattern analysis and machine intelligence 31(5) (2009) 824-840.Hedau, D. Hoiem, D. Forsyth, Recovering the spatial layout of cluttered rooms, in: Computer vision, 2009 IEEE 12th international conference on, IEEE, 2009, pp. 1849-1856.Liu, S. Gould, D. Koller, Single image depth estimation from predicted semantic labels, in: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 1253-1260.Ladicky, J. Shi, M. Pollefeys, Pulling things out of perspective, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 89-96.K. Nathan Silberman, Derek Hoiem, R. Fergus, Indoor segmentation and support inference from rgbd images, in: ECCV, 2012.Liu, J. Yuen, A. Torralba, Sift flow: Dense correspondence across scenes and its applications, IEEE transactions on pattern analysis and machine intelligence 33(5) (2011) 978-994.Konrad, M. Wang, P. Ishwar, 2d-to-3d image conversion by learning depth from examples, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, 2012, pp. 16-22.Liu, C. Shen, G. Lin, Deep convolutional neural fields for depth estimation from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5162-5170.Wang, X. Shen, Z. Lin, S. Cohen, B. Price, A.L. Yuille, Towards unified depth and semantic prediction from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2800-2809.Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: The kitti dataset, International Journal of Robotics Research (IJRR).Achanta, S. Süsstrunk, Saliency detection using maximum symmetric surround, in: Image processing (ICIP), 2010 17th IEEE international conference on, IEEE, 2010, pp. 2653-2656.E. Rahtu, J. Kannala, M. Salo, J. Heikkilä, Segmenting salient objects from images and videos, in: Computer Vision-ECCV 2010, Springer, 2010, pp. 366-37.