Thesis (Ph. D.)--University of Chicago, Department of Psychology, December 1999. / Includes bibliographical references. Also available on the Internet.

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This thesis presents a novel Dynamic Gesture Language Recognition (DGLR) system for human-robot and inter-robot communication. We developed and implemented an experimental setup consisting of a humanoid robot/android able to recognize and execute in real time all the arm gestures of the Dynamic Gesture Language (DGL) in similar way as humans do. Our DGLR system comprises two main subsystems: an image processing (IP) module and a linguistic recognition system (LRS) module. The IP module enables recognizing individual DGL gestures. In this module, we use the bag-of-features (BOFs) and a local part model approach for dynamic gesture recognition from images. Dynamic gesture classification is conducted using the BOFs and nonlinear support-vector-machine (SVM) methods. The multiscale local part model preserves the temporal context. The IP module was tested using two databases, one consisting of images of a human performing a series of dynamic arm gestures under different environmental conditions and a second database consisting of images of an android performing the same series of arm gestures. The linguistic recognition system (LRS) module uses a novel formal grammar approach to accept DGL-wise valid sequences of dynamic gestures and reject invalid ones. LRS consists of two subsystems: one using a Linear Formal Grammar (LFG) to derive the valid sequence of dynamic gestures and another using a Stochastic Linear Formal Grammar (SLFG) to occasionally recover gestures that were unrecognized by the IP module. Experimental results have shown that the DGLR system had a slightly better overall performance when recognizing gestures made by a human subject (98.92% recognition rate) than those made by the android (97.42% recognition rate).

The object of the research is ML-systems for key point extraction for video recordings. The aim of the research is to analyze ML-systems and find the best model for extracting key points from video recordings. The aim of the paper is to analyze the existing ML-systems for extracting key gesture language from video recordings. The research methods are based on data analysis, theory of extracting key points from image, conducting experiments, measuring and comparing the obtained values to evaluate the models. The scientific novelty of the study lies in solving the actual problem of evaluating ML-based keypoint extraction systems for the task of sign language, using modern technologies. The result of the work is a comparative analysis of ML-systems of keypoint extraction from video recordings of sign language, which allowed to identify the best models in terms of metrics and efficiency, and a gloss gluing algorithm is developed, which allows to combine them into one single gesture. / Объект исследования — МЛ-системы извлечения ключевых точек для видеозаписей. Цель исследования – проанализировать мл-системы и найти наилучшую модель для извлечения ключевых точек из видеозаписей. Целью работы – анализ существующих МЛ-систем для извлечение ключевых жестового языка из видеозаписей. Методы исследования основываются на анализе данных, теории извлечения ключевых точек из изображения, проведение экспериментов, измерении и сравнении полученных значений для оценки моделей. Научная новизна исследования заключается в решение актуальной задачи оценки МЛ-систем извлечения ключевых точек для задачи жестового языка, с применением современных технологий. Результатом работы является сравнительный анализ МЛ-систем извлечения ключевых точек из видеозаписей жестового языка, который позволил определить лучшие по метрикам и по эффективности модели, а также разработан алгоритм склейки глоссов, который позволяет объединить их в один единый жест.