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- Brandmaier, Stefan, et al.
(författare)
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The QSPR-THESAURUS : The Online Platform of the CADASTER Project
- 2014
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Ingår i: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 42:1, s. 13-24
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Tidskriftsartikel (refereegranskat)abstract
- The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.
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- Cassani, Stefano, et al.
(författare)
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Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction
- 2013
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Ingår i: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 41:1, s. 49-64
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Tidskriftsartikel (refereegranskat)abstract
- QSAR regression models of the toxicity of triazoles and benzotriazoles ([B] TAZs) to an alga (Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by five partners in the FP7-EU Project, CADASTER. The models were developed by different methods - Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative Neural Network (ASNN) - by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPR-THESAURUS web). In addition, different procedures were used for variable selection, validation and applicability domain inspection. The predictions of the models developed, as well as those obtained in a consensus approach by averaging the data predicted from each model, were compared with the results of experimental tests that were performed by two CADASTER partners. The individual and consensus models were able to correctly predict the toxicity classes of the chemicals tested in the CADASTER project, confirming the utility of the QSAR approach. The models were also used for the prediction of aquatic toxicity of over 300 (B)TAZs, many of which are included in the REACH pre-registration list, and were without experimental data. This highlights the importance of QSAR models for the screening and prioritisation of untested chemicals, in order to reduce and focus experimental testing.
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| 9. |
- Keserwani, Prateek, et al.
(författare)
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Quadbox : Quadrilateral bounding box based scene text detection using vector regression
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 36802-36818
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Tidskriftsartikel (refereegranskat)abstract
- Scene text appears with a wide range of sizes and arbitrary orientations. For detecting such text in the scene image, the quadrilateral bounding boxes provide a much tight bounding box compared to the rotated rectangle. In this work, a vector regression method has been proposed for text detection in the wild to generate a quadrilateral bounding box. The bounding box prediction using direct regression requires predicting the vectors from each position inside the quadrilateral. It needs to predict four-vectors, and each varies drastically in its length and orientation. It makes the vector prediction a difficult problem. To overcome this, we have proposed a centroid-centric vector regression by utilizing the geometry of quadrilateral. In this work, we have added the philosophy of indirect regression to direct regression by shifting all points within the quadrilateral to the centroid and afterward performed vector regression from shifted points. The experimental results show the improvement of the quadrilateral approach over the existing direct regression approach. The proposed method shows good performance on many existing public datasets. The proposed method also demonstrates good results on the unseen dataset without getting trained on it, which validates the approach’s generalization ability.
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| 10. |
- Keserwani, Prateek, et al.
(författare)
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Robust Scene Text Detection for Partially Annotated Training Data
- 2022
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Ingår i: IEEE transactions on circuits and systems for video technology (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1051-8215 .- 1558-2205. ; 32:12, s. 8635-8645
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Tidskriftsartikel (refereegranskat)abstract
- This article analyzed the impact of training data containing un-annotated text instances, i.e., partial annotation in scene text detection, and proposed a text region refinement approach to address it. Scene text detection is a problem that has attracted the attention of the research community for decades. Impressive results have been obtained for fully supervised scene text detection with recent deep learning approaches. These approaches, however, need a vast amount of completely labeled datasets, and the creation of such datasets is a challenging and time-consuming task. Research literature lacks the analysis of the partial annotation of training data for scene text detection. We have found that the performance of the generic scene text detection method drops significantly due to the partial annotation of training data. We have proposed a text region refinement method that provides robustness against the partially annotated training data in scene text detection. The proposed method works as a two-tier scheme. Text-probable regions are obtained in the first tier by applying hybrid loss that generates pseudo-labels to refine text regions in the second-tier during training. Extensive experiments have been conducted on a dataset generated from ICDAR 2015 by dropping the annotations with various drop rates and on a publicly available SVT dataset. The proposed method exhibits a significant improvement over the baseline and existing approaches for the partially annotated training data.
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- Roy, Partha Pratim, et al.
(författare)
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3D word spotting using leap motion sensor
- 2021
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Ingår i: Multimedia tools and applications. - : Springer. - 1380-7501 .- 1573-7721. ; 80:8, s. 11671-11689
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Tidskriftsartikel (refereegranskat)abstract
- Leap motion sensor provides a new way of interaction with computers or mobile devices. With this sensor, users can write in air by moving palm or finger, thus, avoiding traditional pen and paper for writing. The strokes of air-writing or 3D writing is different from conventional way of writing. In 3D writing, the words are connected by continuous lines instead of space between them. Also, the arbitrary size of characters and presence of frequent jitters in strokes make the recognition tasks of such words and sentences difficult. To understand the semantics of a word without recognizing each character of words, the alternative process called “word-spotting” is being used. Word-spotting is often useful than conventional recognition systems to understand complex handwriting. Hence, we propose a novel word spotting methodology for 3D text using Leap motion sensor data. Spotting/detection of a keyword in 3D sentences is carried out using Hidden Markov Model (HMM) framework. From experimental study, an average of 41.7 is recorded in terms of Mean-Average-Precision (MAP). The efficiency of the system is demonstrated by comparing traditional segmentation based system. The improved performance shows that the system could be used in developing novel applications in Human-Computer-Interaction (HCI) domain.
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