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Relevant bibliographies by topics / Image scoring

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Academic literature on the topic 'Image scoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Image scoring"

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Russell, Yvan I., Josep Call, and Robin I. M. Dunbar. "Image scoring in great apes." Behavioural Processes 78, no. 1 (2008): 108–11. http://dx.doi.org/10.1016/j.beproc.2007.10.009.

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Russell, Yvan I., Yana Stoilova, and Aura-Adriana Dosoftei. "Cooperation through Image Scoring: A Replication." Games 11, no. 4 (2020): 58. http://dx.doi.org/10.3390/g11040058.

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“Image scoring” is a type of social evaluation, originally used in agent-based models, where the reputation of another is numerically assessed. This phenomenon has been studied in both theoretical models and real-life psychology experiments (using human participants). The latter are aimed to create conditions in the laboratory where image scoring can be elicited. One influential paper is that of Wedekind and Milinski (2000), WM. Our paper is a replication of that study, deliberately employing very similar methodology to the original. Accordingly, we had six groups of ten participants play an economic game. In each round, each player was randomly paired with another player whose identity was unknown. The participant was given a binary choice of either (1) donating money to that person, or (2) not donating money. In each round, the player was passively exposed to information about the past generosity of the other player. In our study, we successfully replicated the central result of WM. Participants in our replication gave significantly more money to partners with higher image scores (more generous reputations) than those with lower image scores (less generous reputations). This paper also provides a critical review of the methodology of WM and the study of image scoring.

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Wedekind, C. "Cooperation Through Image Scoring in Humans." Science 288, no. 5467 (2000): 850–52. http://dx.doi.org/10.1126/science.288.5467.850.

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Truong, Khuong, Jack Boenders, Zofia Maciorowski, et al. "Signal Amplification of FISH for Automated Detection Using Image Cytometry." Analytical Cellular Pathology 13, no. 3 (1997): 137–46. http://dx.doi.org/10.1155/1997/297216.

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The purpose of this study was to improve the detection of FISH signals, in order that spot counting by a fully automated image cytometer be comparable to that obtained visually under the microscope. Two systems of spot scoring, visual and automated counting, were investigated in parallel on stimulated human lymphocytes with FISH using a biotinylated centromeric probe for chromosome 3. Signal characteristics were first analyzed on images recorded with a coupled charge device (CCD) camera. Number of spots per nucleus were scored visually on these recorded images versus automatically with a DISCOVERY image analyzer. Several fluochromes, amplification systems and pretreatments were tested. Our results for both visual and automated scoring show that the tyramide amplification system (TSA) gives the best amplification of signal if pepsin treatment is applied prior to FISH. Accuracy of the automated scoring, however, remained low (58% of nuclei containing two spots) compared to the visual scoring because of the high intranuclear variation between FISH spots.

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Awadh, A., S. Sairam, and J. S. Carvalho. "OC150: Audit of second-trimester cardiac images: image scoring method." Ultrasound in Obstetrics and Gynecology 28, no. 4 (2006): 403. http://dx.doi.org/10.1002/uog.3010.

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Sande, Nina Krafft, Pernille Bøyesen, Anna-Birgitte Aga, et al. "Development and reliability of a novel ultrasonographic joint-specific scoring system for synovitis with reference atlas for patients with juvenile idiopathic arthritis." RMD Open 7, no. 2 (2021): e001581. http://dx.doi.org/10.1136/rmdopen-2021-001581.

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ObjectiveTo develop an ultrasonographic image acquisition protocol and a joint-specific scoring system for synovitis with reference atlas in patients with juvenile idiopathic arthritis (JIA) and to assess the reliability of the system.MethodsSeven rheumatologists with extensive ultrasound experience developed a scanning protocol and a semiquantitative joint-specific scoring system for B-mode (BM) synovitis for the elbow, wrist, metacarpophalangeal 2–3, proximal interphalangeal 2–3, hip, knee, ankle and metatarsophalangeal 2–3 joints. An ultrasonographic reference atlas for BM synovitis, divided in four age groups (2–4, 5–8, 9–12, 13–18 years), and power Doppler (PD) activity was then developed. Reliability was assessed for all joints on still images and in a live exercise including 10 patients with JIA, calculated by intraclass correlation coefficient (ICC) and weighted kappa.ResultsA scanning protocol and scoring system for multiple joints with reference atlas composed of images with four different score levels for BM and PD were developed. Still image scoring for BM synovitis on joint level showed good to excellent intra-reader reliability (ICC/kappa ranges: 0.75–0.95/0.63–0.91) and moderate to excellent inter-reader reliability (ICC/kappa ranges: 0.89–0.99/0.50–0.91). Still image scoring for PD activity showed excellent intra-reader and inter-reader reliability (ICC/kappa: 0.96/0.91 and ICC/kappa: 0.97/0.80, respectively). In the live scoring, inter-reader reliability (ICC/kappa) was moderate to excellent for BM synovitis (0.94/0.51) and PD activity (0.91/0.60).ConclusionAn ultrasonographic image acquisition protocol and joint-specific scoring system with reference atlas were developed and demonstrated moderate to excellent reliability for scoring of synovitis in patients with JIA. This can be a valuable tool in clinical practice and future research.

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Brandt, H., and K. Sigmund. "Indirect reciprocity, image scoring, and moral hazard." Proceedings of the National Academy of Sciences 102, no. 7 (2005): 2666–70. http://dx.doi.org/10.1073/pnas.0407370102.

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Nowak, Martin A., and Karl Sigmund. "Evolution of indirect reciprocity by image scoring." Nature 393, no. 6685 (1998): 573–77. http://dx.doi.org/10.1038/31225.

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Braitman, Keli A., and Nerella V. Ramanaiah. "Sex Roles and Body Image." Psychological Reports 84, no. 3 (1999): 1055–58. http://dx.doi.org/10.2466/pr0.1999.84.3.1055.

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The hypothesis chat women scoring as Sex-typed have less favorable body image than women scoring as Androgynous or Cross-sexed was tested using the Personal Attributes Questionnaire of Spence and Helmreich and the Body Esteem Scale of Franzoi and Shields. These were completed by 121 Euro-American female undergraduates in psychology. Analysis indicated that women classified as Sex-typed and Undifferentiated scored significantly lower than women classified as Androgynous and Cross-sexed on Sexual Attractiveness, Weight Concern, and Physical Condition subscales of the Body Esteem Scale, and the two groups had a significantly different mean profile. In addition, the Sex-typed group had a significantly different mean profile based on the three body-image subscales from that of women scoring as Androgynous but not those classed as Cross-sexed. As expected, there were no differences on body image between women classed as Sex-typed and Undifferentiated.

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CHEN, KEE-HSIN, SUSANNA CHANG, TZU-CHIEN HSIAO, YUEH-CHIH CHEN, and CHII-WANN LIN. "A NEONATAL FACIAL IMAGE SCORING SYSTEM (NFISS) FOR PAIN RESPONSE STUDIES." Biomedical Engineering: Applications, Basis and Communications 17, no. 02 (2005): 79–85. http://dx.doi.org/10.4015/s1016237205000123.

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The aim of this study was to establish a scoring system, Neonatal Facial Image Scoring System (NFISS), for investigating the pain responses in neonates during routine venipuncture. We collected 1998 facial images from 19 infants during the first phase of this study and identified twelve features of the facial image: normalized scale for NFISS (scale from 0-15), including brow bulge (0-2), vertical brow furrowing (0-1) and short distance (0-1), brow lowering (0-1), eyes close (0-1), bulging eyes (0-2), eye-eye furrowing (0-1), cheek bulge (0-1), nasal-labial furrowing (0-2), mouth open and stretch (0-1), lip purse (0-1) and taut tongue (0-1). During the second evaluation phase, with 2770 images from 31 newborn infants (average age: 2.85°”.027 days), the pain responses were graded on the NFISS scale. Two peak score values during a time course of baseline-venipuncture-recovery (3-3-10 min) were found with needle piercing and needle withdrawal. The reliability of the score analysis was evaluated using 492 randomly selected images out of the 2770 images. The results for intra-scorer (author, one week interval) and inter-scorer (author and one experienced nurse) has correlation coefficients of 0.916 and 0.826, respectively.

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Dissertations / Theses on the topic "Image scoring"

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Seal, Amy. "Scoring sentences developmentally : an analog of developmental sentence scoring /." Diss., CLICK HERE for online access:, 2001. http://contentdm.lib.byu.edu/ETD/image/etd12.pdf.

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Berger, Ulrich, and Ansgar Grüne. "Evolutionary Stability of Indirect Reciprocity by Image Scoring." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4087/1/wp168.pdf.

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Indirect reciprocity describes a class of reputation-based mechanisms which may explain the prevalence of cooperation in groups where partners meet only once. The first model for which this has analytically been shown was the binary image scoring mechanism, where one's reputation is only based on one's last action. But this mechanism is known to fail if errors in implementation occur. It has thus been claimed that for indirect reciprocity to stabilize cooperation, reputation assessments must be of higher order, i.e. contingent not only on past actions, but also on the reputations of the targets of these actions. We show here that this need not be the case. A simple image scoring mechanism where more than just one past action is observed provides ample possibilities for stable cooperation to emerge even under substantial rates of implementation errors. (authors' abstract)<br>Series: Department of Economics Working Paper Series

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Judson, Carrie Ann. "Accuracy of Automated Developmental Sentence Scoring Software." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1448.pdf.

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Conser, Erik Timothy. "Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/3879.

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Image retrieval via a structured query is explored in Johnson, et al. [7]. The query is structured as a scene graph and a graphical model is generated from the scene graph's object, attribute, and relationship structure. Inference is performed on the graphical model with candidate images and the energy results are used to rank the best matches. In [7], scene graph objects that are not in the set of recognized objects are not represented in the graphical model. This work proposes and tests two approaches for modeling the unrecognized objects in order to leverage the attribute and relationship models to improve image retrieval performance.

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Millett, Ronald P. "Automatic holistic scoring of ESL essays using linguistic maturity attributes /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1507.pdf.

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SEIBERT, BRENT BENJAMIN. "EFFECTS OF SUB-PART SCORING IN AUTOMATIC TARGET RECOGNITION." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1006203207.

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Contreras, Anthony D. "Historical GeoCollaboration : the implementation of a scoring system to account for uncertainty in Geographic data created in a collaborative environment /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3555.pdf.

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Sarris, Ippokratis. "Creation of a new fetal biometry image quality scoring tool to improve the accuracy of fetal biometric measurements." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595667.

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The hypothesis of this work is that through establishing the background variation of ultrasonographic fetal biometry measurements and elucidating the parameters that influence these measurements, a new Fetal Ultrasound Biometry Quality (FUB-Q) image-scoring tool can be created which will be reproducible and able to quantify the accuracy of fetal measurements. Six studies are included, each answering a specific research question. The aim of the first study was to ascertain whether pre-existing image quality scoring methods reflect measurement accuracy and reproducibility. It demonstrated that during the course of an exercise where there was demonstrable improvement in the consistency of measurements performed - by a group of sonographers, this was not mirrored by the pre-existing image scoring system. The aim of the second study was to establish the intra- and inter- observer variability of fetal biometry measurements throughout pregnancy by expert sonographers. This study demonstrated that ultrasound variability of fetal biometry increases with advancing gestation when expressed in measurement values, but is constant as a percentage of the fetal dimensions or when reported as a z score. Calliper placement was the major component of the overall variability. The values from this study served as the background variability, "reference standard", for the FUB-Q tool. The third study had two aims. The first was to establish how 3D scanning performs compared to conventional, real-time, 20. The second aim was to assess whether off-line 3D volume manipulation can be used as a tool to substitute real-time 20 ultrasound for the subsequent studies. It demonstrated that measurements using 3D volume acquisitions exhibit good agreement with real-time 20 scanning, with no systematic error but with a higher random error. However, it also demonstrated that 3D scanning is slower to perform and, similar to real-time 2D, it is not always possible to acquire a 3D volume from a desired orientation. Furthermore, not all 3D volume acquisitions were amenable to reconstruction. However, this study showed that saved 3D volumes can be used as a mean to store large volumes of data for later detailed analysis. The aim of the fourth study was to create the FUB-Q scoring tool. This was done by establishing the difference in measurement resulting from optimal and different forms of suboptimal images in a systematic fashion. For any . t " given image, and its derived measurement, the observer inserts in the model the various image scoring point parameters. The model then gives a prediction about the confidence interval within which the optimal, "gold standard", measurement should be. The aim of the fifth study was to validate on an independent test set the predictive ability of the newly developed FUB-Q scoring tool. It demonstrated that the FUB-Q tool can correctly predict the confidence interval within which measurements recorded from correctly acquired images should be in relation to measurements acquired from incorrectly acquired ones. The aim of the sixth, and final , study was to evaluate the reproducibility of obtaining the relevant scores for the FUB-Q tool. It demonstrated that the FUB-Q tool has good intra- and inter- observer reproducibility and is a reliable system for assessing the quality of fetal biometry based on ultrasound images. In conclusion, the FUB-Q tool could be a useful system used for audit of clinical practice and quality control as well as for training purposes .

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Ghaedi, Leila. "AN AUTOMATED DENTAL CARIES DETECTION AND SCORING SYSTEM FOR OPTIC IMAGES OF TOOTH OCCLUSAL SURFACE." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3548.

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Dental caries are one of the most prevalent chronic diseases. Worldwide 60 to 90 percent of school children and nearly 100 percent of adults experienced dental caries. The management of dental caries demands detection of carious lesions at early stages. The research of designing diagnostic tools in caries has been at peak for the last decade. This research aims to design an automated system to detect and score dental caries according to the International Caries Detection and Assessment System (ICDAS) guidelines using the optical images of the occlusal tooth surface. There have been numerous works that address the problem of caries detection by using new imaging technologies or advanced measurements. However, no such study has been done to detect and score caries with the use of optical images of the tooth surface. The aim of this dissertation is to develop image processing and machine learning algorithms to address the problem of detection and scoring the caries by the use of optical image of the tooth surface.

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Forbes, Jessica LeeAnn. "Development and verification of medical image analysis tools within the 3D slicer environment." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/3085.

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Rapid development of domain specialized medical imaging tools is essential for deploying medical imaging technologies to advance clinical research and clinical practice. This work describes the development process, deployment method, and evaluation of modules constructed within the 3D Slicer environment. These tools address critical problems encountered in four different clinical domains: quality control review of large repositories of medical images, rule-based automated label map cleaning, quantification of calcification in the heart using low-dose radiation scanning, and waist circumference measurement from abdominal scans. Each of these modules enables and accelerates clinical research by incorporating medical imaging technologies that minimize manual human effort. They are distributed within the multi-platform 3D Slicer Extension Manager environment for use in the computational environment most convenient to the clinician scientist.

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Book chapters on the topic "Image scoring"

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Sharma, Ram Prakash, and Somnath Dey. "Fingerprint Image Quality Assessment and Scoring." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71928-3_16.

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Diez, Mireia, Mikel Penagarikano, Amparo Varona, Luis Javier Rodriguez-Fuentes, and German Bordel. "On the Use of Dot Scoring for Speaker Diarization." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_76.

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Ma, Kai, Banghui Li, Zhipeng Yan, and Qiaoling Liu. "Vehicle Seat Comfort Scoring Method." In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3867-4_44.

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Denzinger, Felix, Michael Wels, Katharina Breininger, et al. "Automatic CAD-RADS Scoring Using Deep Learning." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59725-2_5.

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de Bruijne, Marleen. "A Pattern Classification Approach to Aorta Calcium Scoring in Radiographs." In Computer Vision for Biomedical Image Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11569541_18.

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Klikowski, Jakub, and Robert Burduk. "Distance Metrics in Clustering and Weighted Scoring Algorithm." In Progress in Image Processing, Pattern Recognition and Communication Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81523-3_3.

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Humphries, Stephen M., Aleena M. Notary, Juan Pablo Centeno, and David A. Lynch. "Automatic Classification of Centrilobular Emphysema on CT Using Deep Learning: Comparison with Visual Scoring." In Image Analysis for Moving Organ, Breast, and Thoracic Images. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00946-5_32.

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Bergamini, Luca, Abigail Rose Trachtman, Andrea Palazzi, et al. "Segmentation Guided Scoring of Pathological Lesions in Swine Through CNNs." In New Trends in Image Analysis and Processing – ICIAP 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30754-7_35.

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Zapaishchykova, Anna, David Dreizin, Zhaoshuo Li, Jie Ying Wu, Shahrooz Faghihroohi, and Mathias Unberath. "An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87199-4_40.

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Xue, Wufeng, Gary Brahm, Stephanie Leung, Ogla Shmuilovich, and Shuo Li. "Cardiac Motion Scoring with Segment- and Subject-Level Non-local Modeling." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_49.

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Conference papers on the topic "Image scoring"

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Van Velzen, Sanne, Bob D. de Vos, Helena Verkooijen, Tim Leiner, Max Viergever, and Ivana Išgum. "Coronary artery calcium scoring: can we do better?" In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2020. http://dx.doi.org/10.1117/12.2549557.

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Liu, Yaqi, Qiang Cai, Xiaobin Zhu, Jian Cao, and Haisheng Li. "Saliency detection using two-stage scoring." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351569.

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Guo, Yuchen, Guiguang Ding, Jungong Han, Sicheng Zhao, and Bin Wang. "Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/680.

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Recognizing unseen classes is an important task for real-world applications, due to: 1) it is common that some classes in reality have no labeled image exemplar for training; and 2) novel classes emerge rapidly. Recently, to address this task many zero-shot learning (ZSL) approaches have been proposed where explicit linear scores, like inner product score, are employed to measure the similarity between a class and an image. We argue that explicit linear scoring (ELS) seems too weak to capture complicated image-class correspondence. We propose a simple yet effective framework, called Implicit Non-linear Similarity Scoring (ICINESS). In particular, we train a scoring network which uses image and class features as input, fuses them by hidden layers, and outputs the similarity. Based on the universal approximation theorem, it can approximate the true similarity function between images and classes if a proper structure is used in an implicit non-linear way, which is more flexible and powerful. With ICINESS framework, we implement ZSL algorithms by shallow and deep networks, which yield consistently superior results.

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Chen, Zhao, Bin Wang, Yubin Niu, Wei Xia, Jian Qiu Zhang, and Bo Hu. "Semisupervised hyperspectral image classification based on affinity scoring." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326947.

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Isgum, I., B. van Ginneken, A. Rutten, and M. Prokop. "Automated coronary calcification detection and scoring." In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. IEEE, 2005. http://dx.doi.org/10.1109/ispa.2005.195396.

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Ghoniemy, Tarek, Julien Valognes, and Maria A. Amer. "Robust Scoring and Ranking of Object Tracking Techniques." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451757.

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Subong, Ryan A., Arnel C. Fajardo, and Yoon Joong Kim. "LSB Rotation and Inversion Scoring Approach to Image Steganography." In 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2018. http://dx.doi.org/10.1109/jcsse.2018.8457333.

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Spyromitros-Xioufis, Eleftherios, Symeon Papadopoulos, Alexandru Lucian Ginsca, Adrian Popescu, Yiannis Kompatsiaris, and Ioannis Vlahavas. "Improving Diversity in Image Search via Supervised Relevance Scoring." In ICMR '15: International Conference on Multimedia Retrieval. ACM, 2015. http://dx.doi.org/10.1145/2671188.2749334.

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Laddha, Prashant, Om Ji Omer, Gurpreet Singh Kalsi, Dipan Kumar Mandal, and Sreenivas Subramoney. "Descriptor Scoring for Feature Selection in Real-Time Visual Slam." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190889.

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Phienthrakul, Tanasanee, Tewan Santitewagun, and Narit Hnoohom. "A Linear Scoring Algorithm for Shredded Paper Reconstruction." In 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2015. http://dx.doi.org/10.1109/sitis.2015.13.

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Reports on the topic "Image scoring"

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Conser, Erik. Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.5767.

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