Marek R. Ogiela

Rule-based approach to recognizing human body poses and gestures in real time

In this paper we propose a classifier capable of recognizing human body static poses and body gestures in real time. The method is called the gesture description language (GDL). The proposed methodology is intuitive, easily thought and reusable for any kind of body gestures. The very heart of our approach is an automated reasoning module. It performs forward chaining reasoning (like a classic expert system) with its inference engine every time new portion of data arrives from the feature extraction library. All rules of the knowledge base are organized in GDL scripts having the form of text files that are parsed with a LALR-1 grammar. The main novelty of this paper is a complete description of our GDL script language, its validation on a large dataset (1,600 recorded movement sequences) and the presentation of its possible application. The recognition rate for examined gestures is within the range of 80.5–98.5xa0%. We have also implemented an application that uses our method: it is a three-dimensional desktop for visualizing 3D medical datasets that is controlled by gestures recognized by the GDL module.

A system for detecting and describing pathological changes using dynamic perfusion computer tomography brain maps

This paper presents a novel method of detecting and describing pathological changes that can be visualized on dynamic computer tomography brain maps (perfusion CT). The system was tested on a set of dynamic perfusion computer tomography maps. Each set consisted of two perfusion maps (CBF, CBV and TTP for testing the irregularity detection algorithm) and one CT brain scan (for the registration algorithm) from 8 different patients with suspected strokes. In 36 of the 84 brain maps, abnormal perfusion was diagnosed. The results of the algorithm were compared with the findings of a team of two radiologists. All of the CBF and CBV maps that did not show a diagnosed asymmetry were classified correctly (i.e. no asymmetry was detected). In four of the TTP maps the algorithm found asymmetries, which were not classified as irregularities in the medical diagnosis; 84.5% of the maps were diagnosed correctly (85.7% for the CBF, 85.7% for the CBV and 82.1% for the TTP); 75% of the errors in the CBF maps and 100% of the errors in the CBV and the TTP maps were caused by the excessive detection of asymmetry regions. Errors in the CBFs and the CBVs were eliminated in cases in which the symmetry axis was selected manually. Subsequently, 96.4% of the CBF maps and 100% of the CBV maps were diagnosed correctly.

CAD system for automatic analysis of CT perfusion maps

In this article, authors present novel algorithms developed for the computer-assisted diagnosis (CAD) system for analysis of dynamic brain perfusion, computer tomography (CT) maps, cerebral blood flow (CBF), and cerebral blood volume (CBV). Those methods perform both quantitative analysis [detection and measurement and description with brain anatomy atlas (AA) of potential asymmetries/lesions] and qualitative analysis (semantic interpretation of visualized symptoms). The semantic interpretation (decision about type of lesion: ischemic/hemorrhagic, is the brain tissue at risk of infraction or not) of visualized symptoms is done by, so-called, cognitive inference processes allowing for reasoning on character of pathological regions based on specialist image knowledge. The whole system is implemented in.NET platform (C# programming language) and can be used on any standard PC computer with.NET framework installed.

Automatic Detection and Lesion Description in Cerebral Blood Flow and Cerebral Blood Volume Perfusion Maps

The paper presents a new approach of description and analysis of potential lesions in cerebral blood flow and cerebral blood volume perfusion maps. To perform such a computer analysis at first the axial position in patient’s brain must be chosen. In next step the generation of brain perfusion maps connected with detection of asymmetries indicating selected pathological states, and allowing supporting diagnosis of the visible lesions are all done automatically. The constructed system uses the unified algorithm for detection of asymmetry in cerebral blood flow and cerebral blood volume perfusion maps, as well as a registration algorithm created by the authors and based on free form deformation. The tests were performed on set of dynamic perfusion computer tomography maps. Algorithms presented in this paper enable detection of pathological states like head injuries, epilepsy, brain vascular disease, ischemic and hemorrhagic stroke.

Semantic Description and Recognition of Human Body Poses and Movement Sequences with Gesture Description Language

In this article we introduce new approach for human body poses and movement sequences recognition. Our concept is based on syntactic description with so called Gesture Description Language (GDL). The implementation of GDL requires special semantic reasoning module with additional heap-like memory. In the following paragraphs we shortly describes our initial concept. We also present software and hardware architecture that we created to test our solution and very promising early experiments results.

Real-Time Recognition of Selected Karate Techniques Using GDL Approach

Summary. In this paper will be presented a new approach for recognition and interpretation of several karate techniques used specially defined Gesture description language (GDL). The novel contribution of this paper is validation of our new semantic Gesture Description Language classifier on several basic Karate techniques recorded with set of Kinect devices. We also present the calibration procedure that enables integration of skeleton data from set of tracking devices into one skeleton what eliminates many segmentation and tracking errors. The data set for our research contains 350 recorded sequences of qualifies professional sport (black belt) instructor, and master of Okinawa Shorin-ryu Karate. 83% of recordings were correctly classified. The whole solution runs in real-time and enables online and offline classification.

Segmentation and visualization of tubular structures in computed tomography angiography

The new contribution of this article is description of filtering algorithm for detecting tubular structures (veins /arteries) in three-dimensional images. An algorithm incorporate the Frangis filtration with additional neighborhood analysis filter that eliminates local noises that often remains after that algorithm. The sensitivity of the method is steered by two algorithms parameters that might be visualized in 3D plot. Changing of those parameters does not require recalculation of filtration results. Also the concepts of those parameters are more intuitive to the potential user then the three scalable eigenvalues - based Frangis parameters. The whole solution was tested on real volumetric CTA data.

Augmented Reality Interface for Visualization of Volumetric Medical Data

Most of the publications in the field of augmented reality in medicine concentrates on developing new types of systems but not on optimization of speed or portability of existing ones. The intention of authors of this article is to fill this gap by showing a possibility of creating augmented reality interface for visualization of volumetric medical data for Windows OS environment that can be run on off - the - shelf computer and test its capability. Tests of the authors application was performed on three 3D models generated from computer tomography data stored in collection of DICOM files. Authors novel and efficient solution can be easily attached to nearly all medical application system running Windows OS and give medical personnel support of a new level by presenting more informative and realistic three dimensional visualizations for low cost.

Computer Karate Trainer in Tasks of Personal and Homeland Security Defense

In this paper will be presented a new possibility of using GDL (Gesture Description Language) approach for recognition of basic combat techniques from martial arts. The GDL approach allows not only to analyze the several Shorin-Ryu Karate techniques but also to support the training and teaching activities of such arts. Moreover the GDL allow performing the human behavioral analysis, which may be important for recognition of dangerous situations while ensuring the homeland security.

The Automatic Two – Step Vessel Lumen Segmentation Algorithm for Carotid Bifurcation Analysis during Perfusion Examination

The novel path detection algorithm and the region growing based lumen detection algorithm is the main contribution of this article. The proposed lumen segmentation method is consisted of two sub-algorithms. After preprocessing step the first algorithm detects the possible path between start and end point. In the second step it performs the thinning of previously obtained path. The second algorithm is a region – growing procedure with proper homogeneity criteria. The role of this procedure is to segment the whole lumen of considered vessel. The region growing is computed in axial slices and the seed point for growing method in each slice is a voxel taken from path computed in previous step. The proposed method was tested on six carotid arteries structures obtained from CTA examination. Our researches has shown that with our method it is possible to keep the TPR (true positive rate) in 5 from 6 considered cases on the level higher than 80% with FPR (false positive rate) below 1%.