1,287 research outputs found
The Conditional Lucas & Kanade Algorithm
The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense
image and object alignment. The approach is efficient as it attempts to model
the connection between appearance and geometric displacement through a linear
relationship that assumes independence across pixel coordinates. A drawback of
the approach, however, is its generative nature. Specifically, its performance
is tightly coupled with how well the linear model can synthesize appearance
from geometric displacement, even though the alignment task itself is
associated with the inverse problem. In this paper, we present a new approach,
referred to as the Conditional LK algorithm, which: (i) directly learns linear
models that predict geometric displacement as a function of appearance, and
(ii) employs a novel strategy for ensuring that the generative pixel
independence assumption can still be taken advantage of. We demonstrate that
our approach exhibits superior performance to classical generative forms of the
LK algorithm. Furthermore, we demonstrate its comparable performance to
state-of-the-art methods such as the Supervised Descent Method with
substantially less training examples, as well as the unique ability to "swap"
geometric warp functions without having to retrain from scratch. Finally, from
a theoretical perspective, our approach hints at possible redundancies that
exist in current state-of-the-art methods for alignment that could be leveraged
in vision systems of the future.Comment: 17 pages, 11 figure
MinMax Radon Barcodes for Medical Image Retrieval
Content-based medical image retrieval can support diagnostic decisions by
clinical experts. Examining similar images may provide clues to the expert to
remove uncertainties in his/her final diagnosis. Beyond conventional feature
descriptors, binary features in different ways have been recently proposed to
encode the image content. A recent proposal is "Radon barcodes" that employ
binarized Radon projections to tag/annotate medical images with content-based
binary vectors, called barcodes. In this paper, MinMax Radon barcodes are
introduced which are superior to "local thresholding" scheme suggested in the
literature. Using IRMA dataset with 14,410 x-ray images from 193 different
classes, the advantage of using MinMax Radon barcodes over \emph{thresholded}
Radon barcodes are demonstrated. The retrieval error for direct search drops by
more than 15\%. As well, SURF, as a well-established non-binary approach, and
BRISK, as a recent binary method are examined to compare their results with
MinMax Radon barcodes when retrieving images from IRMA dataset. The results
demonstrate that MinMax Radon barcodes are faster and more accurate when
applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on
Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US
Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern
International audienceUltrasound in regional anesthesia (RA) has increased in pop-ularity over the last years. The nerve localization presents a key step for RA practice, it is therefore valuable to develop a tool able to facilitate this practice. The nerve detection in the ultrasound images is a challeng-ing task, since the noise and other artifacts corrupt the visual properties of such kind of tissue. In this paper we propose a new method to address this problem. The proposed technique operates in two steps. As the me-dian nerve belongs to a hyperechoic region, the first step consists in the segmentation of this type of region using the k-means algorithm. The second step is more critical; it deals with nerve structure detection in noisy data. For that purpose, a new descriptor is developed. It combines tow methods median binary pattern (MBP) and Gabor filter to obtain the median Gabor binary pattern (MGBP). The method was tested on 173 ultrasound images of the median nerve obtained from three patients. The results showed that the proposed approach achieves better accuracy than the original MBP, Gabor descriptor and other popular descriptors
Multimodal Fake News Detection with Textual, Visual and Semantic Information
[EN] Recent years have seen a rapid growth in the number of fake
news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news.Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3S3038Boididou, C., et al.: Verifying multimedia use at MediaEval 2015. In: MediaEval 2015 Workshop, pp. 235–237 (2015)Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: WWW 2011, pp. 675–684 (2011)Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR 2017, pp. 1251–1258 (2017)Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: ICWSM 2017 (2017)Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248–255 (2009)Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020)Giachanou, A., Gonzalo, J., Mele, I., Crestani, F.: Sentiment propagation for predicting reputation polarity. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 226–238. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_18Giachanou, A., RĂssola, E.A., Ghanem, B., Crestani, F., Rosso, P.: The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: MĂ©tais, E., Meziane, F., Horacek, H., Cimiano, P. (eds.) NLDB 2020. LNCS, vol. 12089, pp. 181–192. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51310-8_17Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: SIGIR 2019, pp. 877–880 (2019)He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770–778 (2016)Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C 41(6), 765–781 (2011)Khattar, D., Goud, J.S., Gupta, M., Varma, V.: MVAE: multimodal variational autoencoder for fake news detection. In: WWW 2019, pp. 2915–2921 (2019)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Popat, K., Mukherjee, S., Yates, A., Weikum, G.: DeClarE: debunking fake news and false claims using evidence-aware deep learning. In: EMNLP 2018, pp. 22–32 (2018)Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: EMNLP 2017, pp. 2931–2937 (2017)Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: MIPR 2018, pp. 430–435 (2018)Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv:1809.01286 (2018)Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR 2016, pp. 2818–2826 (2016)Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: KDD 2018, pp. 849–857 (2018)Zhao, Z., et al.: An image-text consistency driven multimodal sentiment analysis approach for social media. Inf. Process. Manag. 56(6), 102097 (2019)Zlatkova, D., Nakov, P., Koychev, I.: Fact-checking meets fauxtography: verifying claims about images. In: EMNLP-IJCNLP 2019, pp. 2099–2108 (2019
A study of 15N14N isotopic exchange over cobalt molybdenum nitrides
The 14N/15N isotopic exchange pathways over Co3Mo3N, a material of interest as an ammonia synthesis catalyst and for the development of nitrogen transfer reactions, have been investigated. Both the homomolecular and heterolytic exchange processes have been studied, and it has been shown that lattice nitrogen species are exchangeable. The exchange behavior was found to be a strong function of pretreatment with ca. 25% of lattice N atoms being exchanged after 40 min at 600 °C after N2 pretreatment at 700 °C compared to only 6% following similar Ar pretreatment. This observation, for which the potential contribution of adsorbed N species can be discounted, is significant in terms of the application of this material. In the case of the Co6Mo6N phase, regeneration to Co3Mo3N under 15N2 at 600 °C occurs concurrently with 14N15N formation. These observations demonstrate the reactivity of nitrogen in the Co–Mo–N system to be a strong function of pretreatment and worthy of further consideration
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Proxemic Flow: Dynamic Peripheral Floor Visualizations for Revealing and Mediating Large Surface Interactions
Interactive large surfaces have recently become commonplace for interactions in public settings. The fact that people can engage with them and the spectrum of possible interactions, however, often remain invisible and can be confusing or ambiguous to passersby. In this paper, we explore the design of dynamic peripheral floor visualizations for revealing and mediating large surface interactions. Extending earlier work on interactive illuminated floors, we introduce a novel approach for leveraging floor displays in a secondary, assisting role to aid users in interacting with the primary display. We illustrate a series of visualizations with the illuminated floor of the Proxemic Flow system. In particular, we contribute a design space for peripheral floor visualizations that (a) provides peripheral information about tracking fidelity with personal halos, (b) makes interaction zones and borders explicit for easy opt-in and opt-out, and (c) gives cues inviting for spatial movement or possible next interaction steps through wave, trail, and footstep animations. We demonstrate our proposed techniques in the context of a large surface application and discuss important design considerations for assistive floor visualizations
Virtual Reality Sickness Reduces Attention During Immersive Experiences
In this paper, we show that Virtual Reality (VR) sickness is associated with
a reduction in attention, which was detected with the P3b Event-Related
Potential (ERP) component from electroencephalography (EEG) measurements
collected in a dual-task paradigm. We hypothesized that sickness symptoms such
as nausea, eyestrain, and fatigue would reduce the users' capacity to pay
attention to tasks completed in a virtual environment, and that this reduction
in attention would be dynamically reflected in a decrease of the P3b amplitude
while VR sickness was experienced. In a user study, participants were taken on
a tour through a museum in VR along paths with varying amounts of rotation,
shown previously to cause different levels of VR sickness. While paying
attention to the virtual museum (the primary task), participants were asked to
silently count tones of a different frequency (the secondary task). Control
measurements for comparison against the VR sickness conditions were taken when
the users were not wearing the Head-Mounted Display (HMD) and while they were
immersed in VR but not moving through the environment. This exploratory study
shows, across multiple analyses, that the effect mean amplitude of the P3b
collected during the task is associated with both sickness severity measured
after the task with a questionnaire (SSQ) and with the number of counting
errors on the secondary task. Thus, VR sickness may impair attention and task
performance, and these changes in attention can be tracked with ERP measures as
they happen, without asking participants to assess their sickness symptoms in
the moment
How to stop disproportionation of a hydrochloride salt of a very weakly basic compound in a non-clinical suspension formulation
Our objectives were to stabilize a non-clinical suspension for use in toxicological studies and to develop methods to investigate the stability of the formulation in terms of salt disproportionation. The compound under research was a hydrochloride salt of a practically insoluble discovery compound ODM-203. The first of the three formulation approaches was a suspension prepared and stored at room temperature. The second formulation was stabilized by pH adjustment. In the third approach cooling was used to prevent salt disproportionation. 5 mg/mL aqueous suspension consisting of 20 mg/mL PVP/VA and 5 mg/mL Tween 80 was prepared for each of the approaches. The polymer was used as precipitation inhibitor to provide prolonged supersaturation while Tween 80 was used to enhance dissolution and homogeneity of the suspension. The consequences of salt disproportionation were studied by a small-scale in vitro dissolution method and by an in vivo pharmacokinetic study in rats. Our results show that disproportionation was successfully suppressed by applying cooling of the suspension in an ice bath at 2-8 degrees C. This procedure enabled us to proceed to the toxicological studies in rats. The in vivo study results obtained for the practically insoluble compound showed adequate exposures with acceptable variation at each dose level.Peer reviewe
In Search of a Cure: The Development of Therapeutics to Alter the Progression of Spinal Muscular Atrophy
Until the recent development of disease-modifying therapeutics, spinal muscular atrophy (SMA) was considered a devastating neuromuscular disease with a poor prognosis for most affected individuals. Symptoms generally present during early childhood and manifest as muscle weakness and progressive paralysis, severely compromising the affected individual’s quality of life, independence, and lifespan. SMA is most commonly caused by the inheritance of homozygously deleted SMN1 alleles with retention of one or more copies of a paralog gene, SMN2, which inversely correlates with disease severity. The recent advent and use of genetically targeted therapies have transformed SMA into a prototype for monogenic disease treatment in the era of genetic medicine. Many SMA-affected individuals receiving these therapies achieve traditionally unobtainable motor milestones and survival rates as medicines drastically alter the natural progression of this disease. This review discusses historical SMA progression and underlying disease mechanisms, highlights advances made in therapeutic research, clinical trials, and FDA-approved medicines, and discusses possible second-generation and complementary medicines as well as optimal temporal intervention windows in order to optimize motor function and improve quality of life for all SMA-affected individuals
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