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Relevant bibliographies by topics / Recognition (Psychology) Image processing

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Academic literature on the topic 'Recognition (Psychology) Image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Recognition (Psychology) Image processing"

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Grill-Spector, Kalanit, and Nancy Kanwisher. "Visual Recognition." Psychological Science 16, no. 2 (2005): 152–60. http://dx.doi.org/10.1111/j.0956-7976.2005.00796.x.

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What is the sequence of processing steps involved in visual object recognition? We varied the exposure duration of natural images and measured subjects' performance on three different tasks, each designed to tap a different candidate component process of object recognition. For each exposure duration, accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds) than on a perceptual categorization task (e.g., birds vs. cars). However, strikingly, at each exposure duration, subjects performed just as quickly and accurately o

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Dillon, Craig, Terry Caelli, Gerhard Krieger, and Erol Osman. "The IPRS Image Processing and Pattern Recognition System." Spatial Vision 11, no. 1 (1997): 107–15. http://dx.doi.org/10.1163/156856897x00131.

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Liu, Jun Ling. "A Comparative Study of Visual Salient Features Affect Object Recognition." Applied Mechanics and Materials 713-715 (January 2015): 2165–68. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2165.

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The color, shape and texture are the basic intrinsic features of visual scene and important features for scene recognition and visual salient feature. The recognition of four targets of “bird”, “ball”, “butterfly” and “flower” under the color image, gray image, edge image and low-pass blurred image was completed in the paper through psychology experiment, and the experimental result showed that the speed to recognize the color image was quickest, it was a little slower to recognize the gray image with the same accuracy of the color image, the edge image had a great impact on the recognition of

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Balas, Benjamin, Jacob Gable, and Hannah Pearson. "The Effects of Blur and Inversion on the Recognition of Ambient Face Images." Perception 48, no. 1 (2018): 58–71. http://dx.doi.org/10.1177/0301006618812581.

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When viewing unfamiliar faces that vary in expressions, angles, and image quality, observers make many recognition errors. Specifically, in unconstrained identity-sorting tasks, observers struggle to cope with variation across different images of the same person while succeeding at telling different people apart. The use of ambient face images in this simple card-sorting task reveals the magnitude of these face recognition errors and suggests a useful platform to reexamine the nature of face processing using naturalistic stimuli. In the present study, we chose to investigate the impact of two

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van der Linde, Ian, Umesh Rajashekar, Alan C. Bovik, and Lawrence K. Cormack. "Visual Memory for Fixated Regions of Natural Images Dissociates Attraction and Recognition." Perception 38, no. 8 (2009): 1152–71. http://dx.doi.org/10.1068/p6142.

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Recognition memory for fixated regions from briefly viewed full-screen natural images is examined. Low-level image statistics reveal that observers fixated, on average (pooled across images and observers), image regions that possessed greater visual saliency than non-fixated regions, a finding that is robust across multiple fixation indices. Recognition-memory performance indicates that, of the fixation loci tested, observers were adept at recognising those with a particular profile of image statistics; visual saliency was found to be attenuated for unrecognised loci, despite that all regions

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Lv, Feng, Chunmei ZHANG та Changwei Lv. "Image recognition of individual cow based on SIFT in Lαβ color space". MATEC Web of Conferences 176 (2018): 01023. http://dx.doi.org/10.1051/matecconf/201817601023.

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Using image recognition technology to identify individual dairy cattle with her biological features shows strong stability. This kind of non-contact, high precision and low cost individual recognition methods based on image processing are more and more popular recently to replace the electronic tag and ear mark which can hurt the cattle’s psychology and physical health and can affect cattle’s behavior. By comparing the various color space transformations, he proposed a scale-invariant feature transform algorithm based on the Luminace of Lαβ color space. With this algorithm, a biological featur

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Blazhenkova, Olesya. "Boundary Extension in Face Processing." i-Perception 8, no. 5 (2017): 204166951772480. http://dx.doi.org/10.1177/2041669517724808.

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Boundary extension is a common false memory error, in which people confidently remember seeing a wider angle view of the scene than was viewed. Previous research found that boundary extension is scene-specific and did not examine this phenomenon in nonscenes. The present research explored boundary extension in cropped face images. Participants completed either a short-term or a long-term condition of the task. During the encoding, they observed photographs of faces, cropped either in a forehead or in a chin area, and subsequently performed face recognition through a forced-choice selection. Th

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Stevenson, Natasha, and Kun Guo. "Image Valence Modulates the Processing of Low-Resolution Affective Natural Scenes." Perception 49, no. 10 (2020): 1057–68. http://dx.doi.org/10.1177/0301006620957213.

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In natural vision, noisy and distorted visual inputs often change our perceptual strategy in scene perception. However, it is unclear the extent to which the affective meaning embedded in the degraded natural scenes modulates our scene understanding and associated eye movements. In this eye-tracking experiment by presenting natural scene images with different categories and levels of emotional valence (high-positive, medium-positive, neutral/low-positive, medium-negative, and high-negative), we systematically investigated human participants’ perceptual sensitivity (image valence categorization

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Heravi, Hamed, Roghaieh Aghaeifard, Ali Rahimpour Jounghani, Afshin Ebrahimi, and Masumeh Delgarmi. "EXTRACTING FEATURES OF THE HUMAN FACE FROM RGB-D IMAGES TO PLAN FACIAL SURGERIES." Biomedical Engineering: Applications, Basis and Communications 32, no. 06 (2020): 2050042. http://dx.doi.org/10.4015/s1016237220500428.

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Biometric identification of the human face is a pervasive subject which deals with a wide range of disciplines such as image processing, computer vision, pattern recognition, artificial intelligence, and cognitive psychology. Extracting key face points for developing software and commercial devices of face surgery analysis is one of the most challenging fields in computer image and vision processing. Many studies have developed a variety of techniques to extract facial features from color and gray images. In recent years, using depth information has opened up new approaches to researchers in t

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Cutting, Joe, Paul Cairns, and Gustav Kuhn. "Nothing else matters: Video games create sustained attentional selection away from task-irrelevant features." Attention, Perception, & Psychophysics 82, no. 8 (2020): 3907–19. http://dx.doi.org/10.3758/s13414-020-02122-y.

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Abstract Feature-based attention allocates resources to particular stimulus features and reduces processing and retention of unattended features. We performed four experiments using self-paced video games to investigate whether sustained attentional selection of features could be created without a distractor task requiring continuous processing. Experiments 1 and 2 compared two versions of the game Two Dots, each containing a sequence of images. For the more immersive game post-game recognition of images was very low, but for the less immersive game it was significantly higher. Experiments 3 a

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Dissertations / Theses on the topic "Recognition (Psychology) Image processing"

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Durán, Gabriela. "Effects of concurrent task performance on object processing." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Scott, Guy Lindsay. "Local and global interpretation of moving images /." Brighton [East Sussex] : University of Sussex, School of Social Sciences, Cognitive Studies Programme, 1986. http://doi.library.cmu.edu/10.1184/OCLC/17842732.

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Ali, Afiya. "Recognition of facial affect in individuals scoring high and low in psychopathic personality characteristics." The University of Waikato, 2007. http://adt.waikato.ac.nz/public/adt-uow20070129.190938/index.html.

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Seshadri, M. D. "Image reconstruction, recognition, using image processing, pattern recognition and the Hough transform /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1258488573.

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Siyuan, Lin, and Wang Yaojia. "Currency recognition system using image processing." Thesis, University of Gävle, Department of Industrial Development, IT and Land Management, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-6907.

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<p>It is difficult for people to recognize currencies from different countries. Our aim is to help people solve this problem. However, currency recognition systems that are based on image analysis entirely are not sufficient. Our system is based on image processing and makes the process automatic and robust. We use SEK and Chinese RMB as examples to illustrate the technique. Color and shape information are used in our algorithm.</p>

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Hall, Arthur Daniel. "Pipelined image processing for pattern recognition." Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/251523.

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Li, Guancong. "Postage stamp recognition using image processing." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9425.

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Postage stamp is widely consumed in our daily life. In most of the cases, citizens need to purchase stamps in the post office or the local shop and may even need to wait in a long queue. This paper provides a stamp recognition system for supporting auto postage stamp selling machine, in order to convenient citizens and save labor cost for shops. Both citizens and stamp collectors can benefit from this system. This recognition system uses image processing is programmed using MATLAB®. Having scanned different pieces of postage stamps, our system could detect and recognize which type each piece o

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Wang, Yihong. "Détection des défauts internes et externes des noix en coque : application du traitement d'images et de la reconnaissance de formes au contrôle de qualité." Grenoble 1, 1993. http://www.theses.fr/1993GRE10098.

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Le triage des noix en coque a pour but d'eliminer toutes celles qui presentent des defauts externes et internes. Son automatisation permet d'en augmenter la productivite et une meilleure qualite de noix vendues. Cette automatisation repose sur un systeme d'inspection par vision artificielle. Celui-ci est constitue de deux parties: l'une relative a la prise d'images internes et externes de noix et l'autre a la qualification de la qualite des noix a partir de leurs images, laquelle fait appel aux techniques de traitement d'images et de reconnaissance de formes. Dans cette etude, un tel systeme d

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Bibby, Geoffrey Thomas. "Digital image processing using parallel processing techniques." Thesis, Liverpool John Moores University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304539.

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Pairmain, D. "Image processing in geophysics." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375293.

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Books on the topic "Recognition (Psychology) Image processing"

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Pattern recognition and image processing. Horwood, 1998.

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Shih, Frank Y. Image Processing and Pattern Recognition. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470590416.

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Vasilenko, G. I. Image recognition by holography. Consultants Bureau, 1989.

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P, Banks Stephen. Signal processing, image processing, and pattern recognition. Prentice Hall, 1990.

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Ślęzak, Dominik, Sankar K. Pal, Byeong-Ho Kang, Junzhong Gu, Hideo Kuroda, and Tai-hoon Kim, eds. Signal Processing, Image Processing and Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10546-3.

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Kim, Tai-hoon, Hojjat Adeli, Carlos Ramos, and Byeong-Ho Kang, eds. Signal Processing, Image Processing and Pattern Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27183-0.

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Bow, Sing-Tze. Pattern recognition and image preprocessing. M. Dekker, 1992.

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Pattern recognition and image preprocessing. 2nd ed. Marcel Dekker, 2002.

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Paulus, Dietrich W. R. Pattern Recognition and Image Processing in C++. Vieweg+Teubner Verlag, 1995.

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Paulus, Dietrich W. R., and Joachim Hornegger. Pattern Recognition and Image Processing in C++. Vieweg+Teubner Verlag, 1995. http://dx.doi.org/10.1007/978-3-322-87867-0.

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Book chapters on the topic "Recognition (Psychology) Image processing"

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Bräunl, Thomas, Stefan Feyrer, Wolfgang Rapf, and Michael Reinhardt. "Texture Recognition." In Parallel Image Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04327-1_11.

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Jiang, Xiaoyi, and Yung-Fu Chen. "Facial Image Processing." In Applied Pattern Recognition. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76831-9_2.

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Bozga, Marius, George Chasapis, Vassilios V. Dimakopoulos, and Aggelis Aggelis. "Image Processing: Object Recognition." In Smart Multicore Embedded Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8800-2_8.

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Gevers, T., and A. W. M. Smeulders. "Color based object recognition." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_217.

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Sonka, Milan, Vaclav Hlavac, and Roger Boyle. "Object recognition." In Image Processing, Analysis and Machine Vision. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-3216-7_7.

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Aggarwal, J. K., and Shishir Shah. "Object recognition and performance bounds." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_220.

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Albanesi, Maria Grazia, and Luca Lombardi. "Wavelets for multiresolution shape recognition." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_133.

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Ravaut, Frédéric, and Georges Stamon. "Face Image Processing Supporting Epileptic Seizure Analysis." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_40.

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Chen, Wen-Yuan, Yu-Ming Kuo, and Chin-Ho Chung. "Palm Image Recognition Using Image Processing Techniques." In Recent Trends in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38577-3_59.

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Jiang, Hao, Wilson Tiu, Shinji Yamamoto, and Shun-ichi Iisaku. "Automatic recognition of spicules in mammograms." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_148.

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Conference papers on the topic "Recognition (Psychology) Image processing"

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Udupa, Jayaram K., YUBING TONG, Dewey Odhner, et al. "Thoracic lymph node station recognition on CT images based on automatic anatomy recognition with an optimal parent strategy." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293258.

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Tascini, Guido, and Primo Zingaretti. "Image sequence recognition." In Visual Communications and Image Processing '94, edited by Aggelos K. Katsaggelos. SPIE, 1994. http://dx.doi.org/10.1117/12.185964.

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Kumari, Rupashree Brahma, and Anamitra Basu. "Language Processing of Bilinguals: A Visual Word Recognition Study." In Annual International Conference on Cognitive and Behavioral Psychology. Global Science & technology Forum ( GSTF ), 2016. http://dx.doi.org/10.5176/2251-1865_cbp16.53.

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Gesù, Vito Di, Bertrand Zavidovique, and Marco Elio Tabacchi. "Face Expression Recognition through Broken Symmetries." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.39.

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Sharma, Avinash, and Anoop Namboodiri. "Object Category Recognition with Projected Texture." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.62.

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Elss, Tanja, Hannes Nickisch, Tobias Wissel, et al. "Deep-learning-based CT motion artifact recognition in coronary arteries." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2292882.

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Chen, Hanfeng, Su Zhang, and Feihu Qi. "From image sequence to mosaic image." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441607.

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Leh Luoh, Chih-Chang Huang, and Hsueh-Yen Liu. "Image processing based emotion recognition." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551816.

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Feyzkhanov, Rustem, and Irina Zhelavskaya. "Image processing for drawing recognition." In IS&T/SPIE Electronic Imaging, edited by Qian Lin, Jan P. Allebach, and Zhigang Fan. SPIE, 2014. http://dx.doi.org/10.1117/12.2041189.

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Dholay, Surekha, and Akassh A. Mishra. "Fingerprint recognition using image processing." In 3rd International Conference on Digital Image Processing, edited by Ting Zhang. SPIE, 2011. http://dx.doi.org/10.1117/12.896751.

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Reports on the topic "Recognition (Psychology) Image processing"

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Van Nevel, Alan, Larry Peterson, and Charles Kenney. Image Processing for LADAR Automatic Target Recognition. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada389111.

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Sherlock, Barry G. Wavelet-Based Signal and Image Processing for Target Recognition. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada409223.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada295580.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada315017.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada310034.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada305420.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada276405.

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Willsky, Alan S. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425745.

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