Determining subunits for sign language recognition by evolutionary cluster-based segmentation of time series

Abstract. The paper considers partitioning time series into subsequences which form homogeneous groups. To determine the cut points an evolutionary optimization procedure based on multicriteria quality assessment of the resulting clusters is applied. The problem is motivated by automatic recognition of signed expressions, based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. In the paper the problem is formulated, its solution method is proposed and experimentally verified

Czynniki prognostyczne u chorych na pierwotnego inwazyjnego raka pochwy

Aim of the study: Aim of the study was the assessment of prognostic factors in the group of primary invasive vaginal carcinoma (PIVC) patients subjected to radical radiation therapy. Material and methods: The analysis was performed for the group of 152 PIVC patients treated with intracavitary brachytherapy alone (16.5%), the combination of brachytherapy and external radiotherapy (78.9%), or external radiotherapy alone (4.6%). The relationship was investigated between treatment outcome and the following demographic, clinical and histopathological features: age, duration of pathological symptoms, number of births given, prior hysterectomy, haemoglobin level, Karnofsky performance status score, primary tumour location in vagina, length of vagina involved, FIGO stage, gross appearance, histological type, and tumour grade. Results: Five-year disease-free survival was observed in 46.1% of the patients (70/152). Patients below 60 years of age, with Karnofsky score of 80-90, diagnosed with PIVC in stage I0 or II0, and with tumour of grade G1 or G2 had significantly higher 5-year disease-free survival. Multifactoral analysis showed that age below 60 and FIGO stage I0 and II0 are independent favourable prognostic factors. Conclusions: The independent prognostic factors in PIVC patients treated with radical radiotherapy are patient age and FIGO stage.Cel pracy: Celem pracy była ocena czynników prognostycznych w grupie chorych na pierwotnego inwazyjnego raka pochwy (PIVC) poddanych radykalnej radioterapii. Materiał i metody: Przedmiotem analizy była grupa 152 chorych na PIVC poddanych: samodzielnej brachyterapii dojamowej (16,5%), brachyterapii dojamowej skojarzonej z teleradioterapią (78,9%) lub samodzielnej teleradioterapii (4,6%). Przeprowadzono analizę zależności pomiędzy wynikami leczenia, a następującymi cechami populacyjnymi, klinicznymi i mikroskopowymi: wiek, czas trwania objawów chorobowych, liczba porodów, uprzednio wykonana histerektomia, poziom hemoglobiny, stopień sprawności wg skali Karnofskiego, punkt wyjścia raka w obrębie pochwy, długość pochwy zajętej przez raka, zaawansowanie raka wg FIGO, postać makroskopowa guza, postać mikroskopowa i zróżnicowanie raka. Wyniki: 5 lat bez objawów nowotworu przeżyło 46,1% chorych (70/152). Statystycznie znamiennie wyższe bezobjawowe przeżycie 5-letnie uzyskano u chorych poniżej 60 roku życia, w stopniu sprawności Karnofskiego 80-90, chorych na PIVC w I0 i II0 zaawansowania oraz chorych na PIVC G1 i G2. W analizie wielocechowej niezależnymi, korzystnymi czynnikami prognostycznymi były: wiek poniżej 60 lat oraz I0 i II0 zaawansowania raka wg FIGO. Wnioski: Niezależnymi czynnikami prognostycznymi u chorych na PIVC poddanych radykalnej radioterapii są wiek i stopień zaawansowania raka wg FIGO

Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor

In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed using the Point Cloud Library. Recognition is performed by two types of classifiers: (i) k-NN nearest neighbors’ classifier with Dynamic Time Warping measure, (ii) bidirectional long short-term memory (BiLSTM) deep learning networks. Reduction of classification time for the k-NN by introducing a two tier model and improvement of BiLSTM-based classification via transfer learning and combining multiple networks by fuzzy integral are discussed. Our classification results obtained on two representative datasets: University of Texas at Dallas Multimodal Human Action Dataset and Mining Software Repositories Action 3D Dataset are comparable or better than the current state of the art

Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office

The paper addresses the recognition of dynamic Polish Sign Language expressions in an experimental system supporting deaf people in an office when applying for an ID card. A method of processing a continuous stream of RGB-D data and a feature vector are proposed. The classification is carried out using the k-nearest neighbors algorithm with dynamic time warping, hidden Markov models, and bidirectional long short-term memory. The leave-one-subject-out protocol is used for the dataset containing 121 Polish Sign Language sentences performed five times by four deaf people. A data augmentation method is also proposed and tested. Preliminary observations and conclusions from the use of the system in a laboratory, as well as in real conditions with an experimental installation in the Office of Civil Affairs are given

Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images

Changes in the clinical characteristics, treatment options, and therapy outcomes in patients with phyllodes tumor of the breast during 55 years of experience

BACKGROUND: Data from the literature suggests that the clinical picture of phyllodes tumor (PT) of the breast, as well as treatment options and perhaps therapy outcomes, have significantly changed. The aim of this work was to review these changes by analysis of consecutive patients with PT over a 55-year period at a single institution. MATERIAL/METHODS: From 1952 to 2007, 280 women with PT were treated surgically at the Maria Skłodowska-Curie Memorial Institute of Oncology, Cancer Center in Cracow. Age, size of breast tumor, microscopic type, extent of surgery, and therapy outcomes were compared between 2 groups: 190 patients treated from 1952 to 1991 vs 90 patients treated from 1992 to 2007. RESULTS: The results show that the 1992–2007 group compared to the 1952–1991 included more patients <50 years of age, with tumor <5 cm in diameter, undergoing breast-conserving therapy, as well as no evidence of disease at 5-year survival had increased and this change was statistically significant. In addition, malignant PT cases had decreased in frequency. CONCLUSIONS: The results of this study show that patients with PT are increasingly younger, the breast tumors at diagnosis are smaller, malignant PT is becoming less frequent, and BCT is now the treatment of choice. Most importantly, the general treatment outcomes are significantly better