A Web-Based Demo to Interactive Multimodal Transcription of Historic Text images

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04346-8_58[EN] Paleography experts spend many hours transcribing historic documents, and state-of-the-art handwritten text recognition systems are not suitable for performing this task automatically. In this paper we present the modifications oil a previously developed interactive framework for transcription of handwritten text. This system, rather than full automation, aimed at assisting the user with the recognition-transcription process.This work has been supported by the EC (FEDER), the Spanish MEC under grant TIN2006-15694-C02-01 and the research programme Consolider Ingenio 2010 MIPRCV (CSD2007-00018) and by the UPV (FPI fellowship 2006-04).Romero Gómez, V.; Leiva Torres, LA.; Alabau Gonzalvo, V.; Toselli, AH.; Vidal Ruiz, E. (2009). A Web-Based Demo to Interactive Multimodal Transcription of Historic Text images. En Research and Advanced Technology for Digital Libraries: 13th European Conference, ECDL 2009, Corfu, Greece, September 27 - October 2, 2009. Proceedings. Springer Verlag (Germany). 459-460. https://doi.org/10.1007/978-3-642-04346-8_58S459460Toselli, A.H., et al.: Computer assisted transcription of handwritten text. In: Proc. of ICDAR 2007, pp. 944–948. IEEE Computer Society, Los Alamitos (2007)Romero, V., Toselli, A.H., Rodríguez, L., Vidal, E.: Computer assisted transcription for ancient text images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1182–1193. Springer, Heidelberg (2007)Toselli, A.H., et al.: Computer assisted transcription of text images and multimodal interaction. In: Popescu-Belis, A., Stiefelhagen, R. (eds.) MLMI 2008. LNCS, vol. 5237, pp. 296–308. Springer, Heidelberg (2008)Romero, V.: et al.: Interactive multimodal transcription of text images using a web-based demo system. In: Proc. of the IUI, Florida, pp. 477–478 (2009)Romero, V., et al.: Improvements in the computer assisted transciption system of handwritten text images. In: Proc. of the PRIS 2008, pp. 103–112 (2008

Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy

14 págs.; 10 figs.; 7 tabs.; 1 app.© 2014 Elsevier B.V. Besides the well-established healthy properties of pollen, palynology and apiculture are of extreme importance to avoid hard and fast unbalances in our ecosystems. To support such disciplines computer vision comes to alleviate tedious recognition tasks. In this paper we present an applied study of the state of the art in pattern recognition techniques to describe, analyze, and classify pollen grains in an extensive dataset specifically collected (15 types, 120 samples/type). We also propose a novel contour-inner segmentation of grains, improving 50% of accuracy. In addition to published morphological, statistical, and textural descriptors, we introduce a new descriptor to measure the grain's contour profile and a logGabor implementation not tested before for this purpose. We found a significant improvement for certain combinations of descriptors, providing an overall accuracy above 99%. Finally, some palynological features that are still difficult to be integrated in computer systems are discussed.This work has been supported by the European project APIFRESH FP7-SME-2008-2 ‘‘Developing European standards for bee pollen and royal jelly: quality, safety and authenticity’’ and we would like to thank to Mr. Walter Haefeker, President of the European Professional Beekeepers Association (EPBA). J. Victor Marcos is a ‘‘Juan de la Cierva’’ research fellow funded by the Spanish Ministry of Economy and Competitiveness. Rodrigo Nava thanks Consejo Nacional de Ciencia y Tecnología (CONACYT) and PAPIIT Grant IG100814.Peer Reviewe

Score Fusion by Maximizing the Area under the ROC Curve

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02172-5_61Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve. This approach has the convenience that the fusion parameters are learned without having to specify the client and impostor priors or the costs for the different errors. Empirical results on several datasets show the effectiveness of the proposed approach.Work supported by the Spanish projects DPI2006-15542-C04 and TIN2008-04571 and the Generalitat Valenciana - Consellería d’Educació under an FPI scholarship.Villegas Santamaría, M.; Paredes Palacios, R. (2009). Score Fusion by Maximizing the Area under the ROC Curve. En Pattern Recognition and Image Analysis: 4th Iberian Conference, IbPRIA 2009 Póvoa de Varzim, Portugal, June 10-12, 2009 Proceedings. Springer Verlag (Germany). 473-480. https://doi.org/10.1007/978-3-642-02172-5_61S473480Toh, K.A., Kim, J., Lee, S.: Biometric scores fusion based on total error rate minimization. Pattern Recognition 41(3), 1066–1082 (2008)Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38(12), 2270–2285 (2005)Gutschoven, B., Verlinde, P.: Multi-modal identity verification using support vector machines (svm). In: Proceedings of the Third International Conference on Information Fusion. FUSION 2000, vol. 2, pp. THB3/3–THB3/8 (July 2000)Ma, Y., Cukic, B., Singh, H.: A classification approach to multi-biometric score fusion. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 484–493. Springer, Heidelberg (2005)Maurer, D.E., Baker, J.P.: Fusing multimodal biometrics with quality estimates via a bayesian belief network. Pattern Recogn. 41(3), 821–832 (2008)Ling, C.X., Huang, J., Zhang, H.: Auc: a statistically consistent and more discriminating measure than accuracy. In: Proc. of IJCAI 2003, pp. 519–524 (2003)Yan, L., Dodier, R.H., Mozer, M., Wolniewicz, R.H.: Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic. In: Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), Washington, DC, USA, pp. 848–855. AAAI Press, Menlo Park (2003)Marrocco, C., Molinara, M., Tortorella, F.: Exploiting auc for optimal linear combinations of dichotomizers. Pattern Recogn. Lett. 27(8), 900–907 (2006)Marrocco, C., Duin, R.P.W., Tortorella, F.: Maximizing the area under the roc curve by pairwise feature combination. Pattern Recogn. 41(6), 1961–1974 (2008)Paredes, R., Vidal, E.: Learning prototypes and distances: a prototype reduction technique based on nearest neighbor error minimization. Pattern Recognition 39(2), 180–188 (2006)Villegas, M., Paredes, R.: Simultaneous learning of a discriminative projection and prototypes for nearest-neighbor classification. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2008, pp. 1–8 (2008)Nandakumar, K., Chen, Y., Dass, S.C., Jain, A.: Likelihood ratio-based biometric score fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 342–347 (2008)Poh, N., Bengio, S.: A score-level fusion benchmark database for biometric authentication. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1059–1070. Springer, Heidelberg (2005)National Institute of Standards and Technology: NIST Biometric Scores Set - Release 1 (BSSR1) (2004), http://www.itl.nist.gov/iad/894.03/biometricscores/Bengio, S., Mariéthoz, J., Keller, M.: The expected performance curve. In: Proceedings of the Second Workshop on ROC Analysis in ML, pp. 9–16 (2005

Using latent features for short-term person re-identification with RGB-D cameras

This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works.The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.Oliver Moll, J.; Albiol Colomer, A.; Albiol Colomer, AJ.; Mossi García, JM. (2016). Using latent features for short-term person re-identification with RGB-D cameras. Pattern Analysis and Applications. 19(2):549-561. https://doi.org/10.1007/s10044-015-0489-8S549561192http://kinectforwindows.org/http://www.gpiv.upv.es/videoresearch/personindexing.htmlAlbiol A, Albiol A, Oliver J, Mossi JM (2012) Who is who at different cameras. Matching people using depth cameras. Comput Vis IET 6(5):378–387Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using haar-based and dcd-based signature. In: 2nd workshop on activity monitoring by multi-camera surveillance systems, AMMCSS 2010, in conjunction with 7th IEEE international conference on advanced video and signal-based surveillance, AVSS. AVSSBak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using spatial covariance regions of human body parts. In: Seventh IEEE international conference on advanced video and signal based surveillance. pp. 435–440Bak S, Corvee E, Bremond F, Thonnat M (2011) Multiple-shot human re-identification by mean riemannian covariance grid. In: Advanced video and signal-based surveillance. Klagenfurt, Autriche. http://hal.inria.fr/inria-00620496Baltieri D, Vezzani R, Cucchiara R, Utasi A, BenedeK C, Szirányi T (2011) Multi-view people surveillance using 3d information. In: ICCV workshops. pp. 1817–1824Barbosa BI, Cristani M, Del Bue A, Bazzani L, Murino V (2012) Re-identification with rgb-d sensors. In: First international workshop on re-identificationBasilevsky A (1994) Statistical factor analysis and related methods: theory and applications. Willey, New YorkBäuml M, Bernardin K, Fischer k, Ekenel HK, Stiefelhagen R (2010) Multi-pose face recognition for person retrieval in camera networks. In: International conference on advanced video and signal-based surveillanceBazzani L, Cristani M, Perina A, Farenzena M, Murino V (2010) Multiple-shot person re-identification by hpe signature. In: Proceedings of the 2010 20th international conference on pattern recognition. Washington, DC, USA, pp. 1413–1416Bird ND, Masoud O, Papanikolopoulos NP, Isaacs A (2005) Detection of loitering individuals in public transportation areas. IEEE Trans Intell Transp Syst 6(2):167–177Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, SecaucusCha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1(4):300–307Cheng YM, Zhou WT, Wang Y, Zhao CH, Zhang SW (2009) Multi-camera-based object handoff using decision-level fusion. In: Conference on image and signal processing. pp. 1–5Dikmen M, Akbas E, Huang TS, Ahuja N (2010) Pedestrian recognition with a learned metric. In: Asian conference in computer visionDoretto G, Sebastian T, Tu P, Rittscher J (2011) Appearance-based person reidentification in camera networks: problem overview and current approaches. J Ambient Intell Humaniz Comput 2:1–25Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition (CVPR 2010). IEEE Computer Society, San Francisco, CA, USAFodor I (2002) A survey of dimension reduction techniques. Technical report. Lawrence Livermore National LaboratoryFreund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933–969Gandhi T, Trivedi M (2006) Panoramic appearance map (pam) for multi-camera based person re-identification. Advanced Video and Signal Based Surveillance, IEEE Conference on, p. 78Garcia J, Gardel A, Bravo I, Lazaro J (2014) Multiple view oriented matching algorithm for people reidentification. Ind Inform IEEE Trans 10(3):1841–1851Gheissari N, Sebastian TB, Hartley R (2006) Person reidentification using spatiotemporal appearance. CVPR 2:1528–1535Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE international workshop on performance evaluation for tracking and surveillance (PETS)Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. 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Evaluating intelligent interfaces for post-editing automatic transcriptions of online video lectures

Video lectures are fast becoming an everyday educational resource in higher education. They are being incorporated into existing university curricula around the world, while also emerging as a key component of the open education movement. In 2007, the Universitat Politècnica de València (UPV) implemented its poliMedia lecture capture system for the creation and publication of quality educational video content and now has a collection of over 10,000 video objects. In 2011, it embarked on the EU-subsidised transLectures project to add automatic subtitles to these videos in both Spanish and other languages. By doing so, it allows access to their educational content by non-native speakers and the deaf and hard-of-hearing, as well as enabling advanced repository management functions. In this paper, following a short introduction to poliMedia, transLectures and Docència en Xarxa (Teaching Online), the UPV s action plan to boost the use of digital resources at the university, we will discuss the three-stage evaluation process carried out with the collaboration of UPV lecturers to find the best interaction protocol for the task of post-editing automatic subtitles.Valor Miró, JD.; Spencer, RN.; Pérez González De Martos, AM.; Garcés Díaz-Munío, GV.; Turró Ribalta, C.; Civera Saiz, J.; Juan Císcar, A. (2014). Evaluating intelligent interfaces for post-editing automatic transcriptions of online video lectures. Open Learning: The Journal of Open and Distance Learning. 29(1):72-85. doi:10.1080/02680513.2014.909722S7285291Fujii, A., Itou, K., & Ishikawa, T. (2006). LODEM: A system for on-demand video lectures. Speech Communication, 48(5), 516-531. doi:10.1016/j.specom.2005.08.006Gilbert, M., Knight, K., & Young, S. (2008). Spoken Language Technology [From the Guest Editors]. IEEE Signal Processing Magazine, 25(3), 15-16. doi:10.1109/msp.2008.918412Leggetter, C. J., & Woodland, P. C. (1995). Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech & Language, 9(2), 171-185. doi:10.1006/csla.1995.0010Proceedings of the 9th ACM SIGCHI New Zealand Chapter’s International Conference on Human-Computer Interaction Design Centered HCI - CHINZ ’08. (2008). doi:10.1145/1496976Martinez-Villaronga, A., del Agua, M. A., Andres-Ferrer, J., & Juan, A. (2013). Language model adaptation for video lectures transcription. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. doi:10.1109/icassp.2013.6639314Munteanu, C., Baecker, R., & Penn, G. (2008). Collaborative editing for improved usefulness and usability of transcript-enhanced webcasts. Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems - CHI ’08. doi:10.1145/1357054.1357117Repp, S., Gross, A., & Meinel, C. (2008). Browsing within Lecture Videos Based on the Chain Index of Speech Transcription. IEEE Transactions on Learning Technologies, 1(3), 145-156. doi:10.1109/tlt.2008.22Proceedings of the 2012 ACM international conference on Intelligent User Interfaces - IUI ’12. (2012). doi:10.1145/2166966Serrano, N., Giménez, A., Civera, J., Sanchis, A., & Juan, A. (2013). Interactive handwriting recognition with limited user effort. International Journal on Document Analysis and Recognition (IJDAR), 17(1), 47-59. doi:10.1007/s10032-013-0204-5Torre Toledano, D., Ortega Giménez, A., Teixeira, A., González Rodríguez, J., Hernández Gómez, L., San Segundo Hernández, R., & Ramos Castro, D. (Eds.). (2012). Advances in Speech and Language Technologies for Iberian Languages. Communications in Computer and Information Science. doi:10.1007/978-3-642-35292-8Wald, M. (2006). Creating accessible educational multimedia through editing automatic speech recognition captioning in real time. Interactive Technology and Smart Education, 3(2), 131-141. doi:10.1108/1741565068000005

Word graphs size impact on the performance of handwriting document applications

[EN] Two document processing applications are con- sidered: computer-assisted transcription of text images (CATTI) and Keyword Spotting (KWS), for transcribing and indexing handwritten documents, respectively. Instead of working directly on the handwriting images, both of them employ meta-data structures called word graphs (WG), which are obtained using segmentation-free hand- written text recognition technology based on N-gram lan- guage models and hidden Markov models. A WG contains most of the relevant information of the original text (line) image required by CATTI and KWS but, if it is too large, the computational cost of generating and using it can become unafordable. Conversely, if it is too small, relevant information may be lost, leading to a reduction of CATTI or KWS performance. We study the trade-off between WG size and performance in terms of effectiveness and effi- ciency of CATTI and KWS. Results show that small, computationally cheap WGs can be used without loosing the excellent CATTI and KWS performance achieved with huge WGs.Work partially supported by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMAMATER, by the Spanish MECD as part of the Valorization and I+D+I Resources program of VLC/CAMPUS in the International Excellence Campus program, and through the EU projects: HIMANIS (JPICH programme, Spanish Grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, Grant Ref. 674943).Toselli ., AH.; Romero Gómez, V.; Vidal, E. (2017). Word graphs size impact on the performance of handwriting document applications. Neural Computing and Applications. 28(9):2477-2487. https://doi.org/10.1007/s00521-016-2336-2S24772487289Amengual JC, Vidal E (1998) Efficient error-correcting Viterbi parsing. IEEE Trans Pattern Anal Mach Intell 20(10):1109–1116Bazzi I, Schwartz R, Makhoul J (1999) An omnifont open-vocabulary OCR system for English and Arabic. 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Snowbird, UtahTanha J, de Does J, Depuydt K (2015) Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition. In: ESANN 2015 proceedings, European symposium on artificial neural networks, computational intelligence and machine learning, pp 361–366Toselli A, Romero V, i Gadea MP, Vidal E (2010) Multimodal interactive transcription of text images. Pattern Recogn 43(5):1814–1825Toselli A, Romero V, Vidal E (2015) Word-graph based applications for handwriting documents: impact of word-graph size on their performances. In: Paredes R, Cardoso JS, Pardo XM (eds) Pattern recognition and image analysis. Lecture Notes in Computer Science, vol 9117, pp 253–261. Springer International PublishingToselli AH, Juan A, Keysers D, Gonzlez J, Salvador I, Ney H, Vidal E, Casacuberta F (2004) Integrated handwriting recognition and interpretation using finite-state models. 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A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

[EN] This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Diego-Mas, JA.; Alcañiz Raya, ML. (2019). A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images. EURASIP Journal on Image and Video Processing (Online). 2019(1):1-14. https://doi.org/10.1186/s13640-019-0473-0S11420191A. Radman, K. Jumari, N. Zainal, Fast and reliable iris segmentation algorithm. IET Image Process.7(1), 42–49 (2013).M. Erbilek, M. Fairhurst, M. C. D. C Abreu, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Age prediction from iris biometrics (London, 2013), pp. 1–5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6913712&isnumber=6867223 .A. Abbasi, M. Khan, Iris-pupil thickness based method for determining age group of a person. Int. Arab J. Inf. Technol. (IAJIT). 13(6) (2016).G. Mabuza-Hocquet, F. Nelwamondo, T. Marwala, in Intelligent Information and Database Systems. ed. by N. Nguyen, S. Tojo, L. Nguyen, B. Trawiński. Ethnicity Distinctiveness Through Iris Texture Features Using Gabor Filters. ACIIDS 2017. Lecture Notes in Computer Science, vol. 10192 (Springer, Cham, 2017).S. Lagree, K. W. Bowyer, in 2011 IEEE International Conference on Technologies for Homeland Security (HST). Predicting ethnicity and gender from iris texture (IEEEWaltham, 2011). p. 440–445. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6107909&isnumber=6107829 .J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell.15(11), 1148–1161 (1993).N. Kourkoumelis, M. Tzaphlidou. Medical Safety Issues Concerning the Use of Incoherent Infrared Light in Biometrics, eds. A. Kumar, D. Zhang. Ethics and Policy of Biometrics. ICEB 2010. Lecture Notes in Computer Science, vol 6005 (Springer, Berlin, Heidelberg, 2010).R. P. Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE. 85(9), 1348–1363 (1997).M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models. Int. J. Comput. Vision. 1(4), 321–331 (1988).S. J. Pundlik, D. L. Woodard, S. T. Birchfield, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Non-ideal iris segmentation using graph cuts (IEEEAnchorage, 2008). p. 1–6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4563108&isnumber=4562948 .H. Proença, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell.32(8), 1502–1516 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5156505&isnumber=5487331 .T. Tan, Z. He, Z. Sun, Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vision Comput.28(2), 223–230 (2010).C. -W. Tan, A. Kumar, in CVPR 2011 WORKSHOPS. Automated segmentation of iris images using visible wavelength face images (Colorado Springs, 2011). p. 9–14. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981682&isnumber=5981671 .Y. -H. Li, M. Savvides, An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell.35(4), 784–796 (2013).M. Yahiaoui, E. Monfrini, B. Dorizzi, Markov chains for unsupervised segmentation of degraded nir iris images for person recognition. Pattern Recogn. Lett.82:, 116–123 (2016).A. Radman, N. Zainal, S. A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit. Signal Proc.64:, 60–70 (2017).N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, in 2016 International Conference on Biometrics (ICB). Accurate iris segmentation in non-cooperative environments using fully convolutional networks (Halmstad, 2016). p. 1–8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7550055&isnumber=7550036 .Z. Zhao, A. Kumar, in 2017 IEEE International Conference on Computer Vision (ICCV). Towards more accurate iris recognition using deeply learned spatially corresponding features (Venice, 2017). p. 3829–3838. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237673&isnumber=8237262 .P. Li, X. Liu, L. Xiao, Q. Song, Robust and accurate iris segmentation in very noisy iris images. Image Vision Comput.28(2), 246–253 (2010).D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. -K. Park, J. Kim, A new iris segmentation method for non-ideal iris images. Image Vision Comput.28(2), 254–260 (2010).Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput. 28(2), 261–269 (2010).Z. Zhao, A. Kumar, in 2015 IEEE International Conference on Computer Vision (ICCV). An accurate iris segmentation framework under relaxed imaging constraints using total variation model (Santiago, 2015). p. 3828–3836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410793&isnumber=7410356 .Y. Hu, K. Sirlantzis, G. Howells, Improving colour iris segmentation using a model selection technique. Pattern Recogn. Lett.57:, 24–32 (2015).E. Ouabida, A. Essadique, A. Bouzid, Vander lugt correlator based active contours for iris segmentation and tracking. Expert Systems Appl.71:, 383–395 (2017).C. -W. Tan, A. Kumar, Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Proc.21(9), 4068–4079 (2012).C. -W. Tan, A. Kumar, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Human identification from at-a-distance images by simultaneously exploiting iris and periocular features (Tsukuba, 2012). p. 553–556. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460194&isnumber=6460043 .C. -W. Tan, A. Kumar, Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Proc.22(10), 3751–3765 (2013).K. Y. Shin, Y. G. Kim, K. R. Park, Enhanced iris recognition method based on multi-unit iris images. Opt. Eng.52(4), 047201–047201 (2013).CASIA iris databases. http://biometrics.idealtest.org/ . Accessed 06 Sept 2017.WVU iris databases. hhttp://biic.wvu.edu/data-sets/synthetic-iris-dataset . Accessed 06 Sept 2017.UBIRIS iris database. http://iris.di.ubi.pt . Accessed 06 Sept 2017.MICHE iris database. http://biplab.unisa.it/MICHE/ . Accessed 06 Sept 2017.P. J. Phillips, et al, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1. 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Constrained local neural fields for robust facial landmark detection in the wild (Sydney, 2013). p. 354–361. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6755919&isnumber=6755862 .X. Zhu, D. Ramanan, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On. Face detection, pose estimation, and landmark localization in the wild (IEEEBerlin Heidelberg, 2012), pp. 2879–2886.G. Tzimiropoulos, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Project-out cascaded regression with an application to face alignment (Boston, 2015). p. 3659–3667. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298989&isnumber=7298593 .H. Hofbauer, F. Alonso-Fernandez, P. Wild, J. Bigun, A. Uhl, in 2014 22nd International Conference on Pattern Recognition. A ground truth for iris segmentation (Stockholm, 2014). p. 527–532. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6976811&isnumber=6976709 .H. Proença, L. A. Alexandre, in 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. The NICE.I: Noisy Iris Challenge Evaluation - Part I (Crystal City, 2007). p. 1–4. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401910&isnumber=4401902 .J. Daugman, in European Convention on Security and Detection. High confidence recognition of persons by rapid video analysis of iris texture, (1995). p. 244–251. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=491729&isnumber=10615 .Code of Matlab implementation of Daugman’s integro-differential operator (IDO). https://es.mathworks.com/matlabcentral/fileexchange/15652-iris-segmentation-using-daugman-s-integrodifferential-operator/ . Accessed 06 Sept 2017.Code of Matlab implementation of Zhao and Kumar’s iris segmentation framework under relaxed imaging constraints using total variation model. http://www4.comp.polyu.edu.hk/~csajaykr/tvmiris.htm/ . Accessed 06 Sept 2017.Code of Matlab implementation of presented work. https://gitlab.com/ffuentes/hybrid_iris_segmentation/ . Accessed 06 Sept 2017.Face and eye detection with OpenCV. https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html . Accessed 07 Sept 2018.A. K. Boyat, B. K. Joshi, 6. A review paper:noise models in digital image processing signal & image processing. An International Journal (SIPIJ), (2015), pp. 63–75. https://doi.org/10.5121/sipij.2015.6206 .A. Buades, Y. Lou, J. M. Morel, Z. Tang, Multi image noise estimation and denoising (2010). Available: https://hal.archives-ouvertes.fr/hal-00510866/

Escritoire: A Multi-touch Desk with e-Pen Input for Capture, Management and Multimodal Interactive Transcription of Handwritten Documents

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_53A large quantity of documents used every day are still handwritten. However, it is interesting to transform each of these documents into its digital version for managing, archiving and sharing. Here we present Escritoire, a multi-touch desk that allows the user to capture, transcribe and work with handwritten documents. The desktop is continuously monitored using two cameras. Whenever the user makes a specific hand gesture over a paper, Escritoire proceeds to take an image. Then, the capture is automatically preprocesses, obtaining as a result an improved representation. Finally, the text image is transcribed using automatic techniques and finally the transcription is displayed on Escritoire.This work was partially supported by the Spanish MEC under FPU scholarship (AP2010-0575), STraDA research project (TIN2012-37475-C02-01) and MITTRAL research project (TIN2009-14633-C03-01); the EU’s 7th Framework Programme under tranScriptorium grant agreement (FP7/2007-2013/600707).Martín-Albo Simón, D.; Romero Gómez, V.; Vidal Ruiz, E. (2015). Escritoire: A Multi-touch Desk with e-Pen Input for Capture, Management and Multimodal Interactive Transcription of Handwritten Documents. En Pattern Recognition and Image Analysis. Springer. 471-478. https://doi.org/10.1007/978-3-319-19390-8_53S471478Andrew, A.: Another efficient algorithm for convex hulls in two dimensions. Inf. Process. Lett. 9(5), 216–219 (1979)Bosch, V., Toselli, A.H., Vidal, E.: Statistical text line analysis in handwritten documents. In: Proceedings of ICFHR (2012)Eisenstein, J., Puerta, A.: Adaptation in automated user-interface design. In: Proceedings of International Conference on Intelligent User Interfaces (2000)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82(Series D), 35–45 (1960)Keysers, D., Shafait, F., Breuel, T.M.: Document image zone classification - a simple high-performance approach. In: Proceedings of International Conference on Computer Vision Theory (2007)Kozielski, M., Forster, J., Ney, H.: Moment-based image normalization for handwritten text recognition. In: Proceedings of ICFHR (2012)Lampert, C.H., Braun, T., Ulges, A., Keysers, D., Breuel, T.M.: Oblivious document capture and real-time retrieval. In: International Workshop on Camera Based Document Analysis and Recognition (2005)Liang, J., Doermann, D., Li, H.: Camera based analysis of text and documents a survey. Int. J. Doc. Anal. Recogn. 7(2–3), 84–104 (2005)Liwicki, M., Rostanin, O., El-Neklawy, S.M., Dengel, A.: Touch & write: a multi-touch table with pen-input. In: Proceedings of International Workshop on Document Analysis Systems (2010)Marti, U.V., Bunke, H.: Text line segmentation and word recognition in a system for general writer independent handwriting recognition. In: Proceedings of ICDAR (2001)Martín-Albo, D., Romero, V., Toselli, A.H., Vidal, E.: Multimodal computer-assisted transcription of text images at character-level interaction. Int. J. Pattern Recogn. Artif. Intell. 26(5), 19 (2012)Martín-Albo, D., Romero, V., Vidal, E.: Interactive off-line handwritten text transcription using on-line handwritten text as feedback. In: Proceedings of ICDAR (2013)Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. B Cybern. 37(3), 311–324 (2007)Terry, M., Mynatt, E.D.: Recognizing creative needs in user interface design. In: Proceedings of C&C (2002)Toselli, A.H., Juan, A., Keysers, D., González, J., Salvador, I., Ney, H., Vidal, E., Casacuberta, F.: Integrated handwriting recognition and interpretation using finite-state models. Int. J. Pattern Recognit. Artif. Intell. 18(4), 519–539 (2004)Toselli, A.H., Romero, V., Pastor, M., Vidal, E.: Multimodal interactive transcription of text images. Pattern Recognit. 43(5), 1814–1825 (2010)Toselli, A.H., Romero, V., Vidal, E.: Computer assisted transcription of text images and multimodal interaction. In: Popescu-Belis, A., Stiefelhagen, R. (eds.) MLMI 2008. LNCS, vol. 5237, pp. 296–308. Springer, Heidelberg (2008)Wachs, J.P., Kolsch, M., Stern, H., Edan, Y.: Vision-based hand-gesture applications. Commun. ACM. 54(2), 60–71 (2011)Wobbrock, J.O., Morris, M.R., Wilson, A.D.: User-defined gestures for surface computing. In: Proceedings of CHI (2009

Automatic classification of human facial features based on their appearance

[EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. This procedure deals with the difficulties associated with classifying features using judgements from human observers, and facilitates the development of taxonomies of facial features. Taxonomies obtained through this procedure are presented for eyes, mouths and noses.Fuentes-Hurtado, F.; Diego-Mas, JA.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2019). Automatic classification of human facial features based on their appearance. PLoS ONE. 14(1):1-20. https://doi.org/10.1371/journal.pone.0211314S120141Damasio, A. R. (1985). Prosopagnosia. Trends in Neurosciences, 8, 132-135. doi:10.1016/0166-2236(85)90051-7Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xTodorov, A. (2011). Evaluating Faces on Social Dimensions. Social Neuroscience, 54-76. doi:10.1093/acprof:oso/9780195316872.003.0004Little, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). 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Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8 29Computer Assisted Transcription of Text Images (CATTI) and Key-Word Spotting (KWS) applications aim at transcribing and indexing handwritten documents respectively. They both are approached by means of Word Graphs (WG) obtained using segmentation-free handwritten text recognition technology based on N-gram Language Models and Hidden Markov Models. A large WG contains most of the relevant information of the original text (line) image needed for CATTI and KWS but, if it is too large, the computational cost of generating and using it can become unaffordable. Conversely, if it is too small, relevant information may be lost, leading to a reduction of CATTI/KWS in performance accuracy. We study the trade-off between WG size and CATTI &KWS performance in terms of effectiveness and efficiency. Results show that small, computationally cheap WGs can be used without loosing the excellent CATTI/KWS performance achieved with huge WGs.Work partially supported by the Spanish MICINN projects STraDA (TIN2012-37475-C02-01) and by the EU 7th FP tranScriptorium project (Ref:600707).Toselli, AH.; Romero Gómez, V.; Vidal Ruiz, E. (2015). Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances. En Pattern Recognition and Image Analysis. Springer. 253-261. https://doi.org/10.1007/978-3-319-19390-8_29S253261Romero, V., Toselli, A.H., Vidal, E.: Multimodal Interactive Handwritten Text Transcription. Series in Machine Perception and Artificial Intelligence (MPAI). World Scientific Publishing, Singapore (2012)Toselli, A.H., Vidal, E., Romero, V., Frinken, V.: Word-graph based keyword spotting and indexing of handwritten document images. Technical report, Universitat Politècnica de València (2013)Oerder, M., Ney, H.: Word graphs: an efficient interface between continuous-speech recognition and language understanding. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 119–122, April 1993Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Trans. Pattern Anal. Mach. Intell. 21(6), 495–504 (1999)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Ström, N.: Generation and minimization of word graphs in continuous speech recognition. In: Proceedings of IEEE Workshop on ASR 1995, Snowbird, Utah, pp. 125–126 (1995)Ortmanns, S., Ney, H., Aubert, X.: A word graph algorithm for large vocabulary continuous speech recognition. Comput. Speech Lang. 11(1), 43–72 (1997)Wessel, F., Schluter, R., Macherey, K., Ney, H.: Confidence measures for large vocabulary continuous speech recognition. IEEE Trans. Speech Audio Process. 9(3), 288–298 (2001)Robertson, S.: A new interpretation of average precision. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 689–690. ACM, USA (2008)Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, USA (2008)Romero, V., Toselli, A.H., Rodríguez, L., Vidal, E.: Computer assisted transcription for ancient text images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1182–1193. Springer, Heidelberg (2007)Fischer, A., Wuthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.: Automatic transcription of handwritten medieval documents. In: 15th International Conference on Virtual Systems and Multimedia, VSMM 2009, pp. 137–142 (2009)Pesch, H., Hamdani, M., Forster, J., Ney, H.: Analysis of preprocessing techniques for latin handwriting recognition. In: ICFHR, pp. 280–284 (2012)Evermann, G.: Minimum Word Error Rate Decoding. Ph.D. thesis, Churchill College, University of Cambridge (1999