Transfer function design based on user selected samples for intuitive multivariate volume exploration

pre-printMultivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets

Handwriting Difficulty Screening Tool based on Dynamic Data from Drawing Process

Children with handwriting difficulty are advised to join an intervention program to rectify the problem at an early stage. However, the available screening tools suffer from subjectivity judgement while lack of expertise reduces the chance for every student to be screened. Yet, digitalized screening tools that use dynamic data from writing activities are only applicable to those who know the language. These limitations had led this study to develop an objective handwriting difficulty screening tool based on dynamic data of drawings. Three attributes extracted from 120 sets of dynamic data from drawing process were found to be significant in differentiating below-average writers from average writers. The attributes were then used to train Support Vector Machine prediction model. To test the validity and reliability of the prediction model, additional sets of data were acquired from 36 pupils. The performance of the tool was compared with the results from the Handwriting Proficiency Screening Questionnaire (HPSQ) that employs teachers’ observations on pupils’ handwriting ability. With 78% reliability, 69% of the predictions made by the developed tool was in accordance with the teachers’ observation. Most importantly, 53% of the average writers were screened as having handwriting problems. This denotes the objectivity of the developed tool in identifying below-average writers who failed to be recognized through teacher’s observation

Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition

A human-computer interface is developed to provide services of computer assisted machine translation (CAT) and computer assisted transcription of handwritten text images (CATTI). The back-end machine translation (MT) and handwritten text recognition (HTR) systems are provided by the Pattern Recognition and Human Language Technology (PRHLT) research group. The idea is to provide users with easy to use tools to convert interactive translation and transcription feasible tasks. The assisted service is provided by remote servers with CAT or CATTI capabilities. The interface supplies the user with tools for efficient local edition: deletion, insertion and substitution.Ocampo Sepúlveda, JC. (2009). Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition. http://hdl.handle.net/10251/14318Archivo delegad

Review of automated systems for upper limbs functional assessment in neurorehabilitation

Traditionally, the assessment of upper limb (UL) motor function in neurorehabilitation is carried out by clinicians using standard clinical tests for objective evaluation, but which could be influenced by the clinician's subjectivity or expertise. The automation of such traditional outcome measures (tests) is an interesting and emerging field in neurorehabilitation. In this paper, a systematic review of systems focused on automation of traditional tests for assessment of UL motor function used in neurological rehabilitation is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the automation level, the data acquisition systems, the outcome generation method, and the focus of assessment. Finally, a series of technical requirements, guidelines, and challenges that must be considered when designing and implementing fully-automated systems for upper extremity functional assessment are summarized. This paper advocates the use of automated assessment systems (AAS) to build a rehabilitation framework that is more autonomous and objective.This work was supported in part by the Spanish Ministry of Economy and Competitiveness via the ROBOHEALTH (DPI2013-47944-C4-1-R) and ROBOESPAS (DPI2017-87562-C2-1-R) Projects, and in part by the RoboCity2030-III-CM project (S2013/MIT-2748) which is funded by the Programas de Actividades I+D Comunidad de Madrid and cofunded by the Structural Funds of the EU

Off-line Thai handwriting recognition in legal amount

Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

Automating graphology using computer vision

Graphology is the science of studying an individual\u27s personality traits through handwriting analysis. In this thesis, we have automated the graphology process, particularly automating the pattern analysis of the handwriting and inference of the personality traits. The thesis is based off computer vision techniques to build a pipeline for automated graphology using handwritten text, camera and a microcomputing device. In this work, we consider the intricate details of a handwriting sample, like the size and slant variations, the various patterns formed in the writing of the text as visual features for computer vision training and processing. Our experimental analysis on ~100 users resulted in 90\% overall accuracy of the system in personality trait mapping using the user’s feedback as a baseline for evaluation