Depth-based descriptor for matching keypoints in 3D scenes

Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification

NAV-VIR: an audio-tactile virtual environment to assist visually impaired people

International audienceThis paper introduces the NAV-VIR system, a multimodal virtual environment to assist visually impaired people in virtually discovering and exploring unknown areas from the safety of their home. The originality of NAV-VIR resides in (1) an optimized representation of the surrounding topography, the spatial gist, based on human spatial cognition models and the sensorimotor supplementation framework, and (2) a multimodal orientation-aware immersive virtual environment relying on two synergetic interfaces: an interactive force feedback tablet, the F2T, and an immersive HRTF-based 3D audio simulation relying on binaural recordings of real environments. This paper presents NAV-VIR functionalities and its preliminary evaluation through a simple shape and movement perception task

Neurodynamic modelling of the human heartbeat

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN056378 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Advances in Sound Localization

Sound source localization is an important research field that has attracted researchers' efforts from many technical and biomedical sciences. Sound source localization (SSL) is defined as the determination of the direction from a receiver, but also includes the distance from it. Because of the wave nature of sound propagation, phenomena such as refraction, diffraction, diffusion, reflection, reverberation and interference occur. The wide spectrum of sound frequencies that range from infrasounds through acoustic sounds to ultrasounds, also introduces difficulties, as different spectrum components have different penetration properties through the medium. Consequently, SSL is a complex computation problem and development of robust sound localization techniques calls for different approaches, including multisensor schemes, null-steering beamforming and time-difference arrival techniques. The book offers a rich source of valuable material on advances on SSL techniques and their applications that should appeal to researches representing diverse engineering and scientific disciplines

Low Order Modeling Of Head Related Transfer Functions Based On Spectral Smoothing And Principal Component Analysis

Presented at the 19th International Conference on Auditory Display (ICAD2013) on July 6-9, 2013 in Lodz, Poland.The presented paper discusses methods of reducing the order of HRTF filters. Two alternative methods of HRTF preprocessing were proposed: wavelet based approximation of HRTF magnitude spectrum and dimensionality reduction by means of Principal Component Analysis. Frequency components of HRTFs were weighted according to the non-linear frequency resolution of human hearing via transformation to the bark scale. The preprocessed HRTFs were modeled with either recursive or non-recursive filters in linear and non-linear frequency domains

Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures

Data glove devices, apart from being widely used in industry and entertainment, can also serve as a means for communication with the environment. This is possible thanks to the advancement in electronic technology and machine learning algorithms. In this paper, the results of the study using a designed data glove equipped with 10 piezoelectric sensors are reported, and the designed glove is validated on a recognition task of hand gestures based on 16 static signs of the Polish Sign Language (PSL) alphabet. The main result of the study is that recognition of 16 PSL static gestures is possible with a reduced number of piezoelectric sensors. This result has been achieved by applying the decision tree classifier that can rank the importance of the sensors for the recognition performance. Other machine learning algorithms were also tested, and it was showed that for the Support Vector Machines, k-NN and Bagged Trees classifiers, a recognition rate of the signs exceeding 90% can be achieved just for three preselected sensors. Such a result is important for a reduction in design complexity and costs of such a data glove with sustained reliability of the device

Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation

The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian’s steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS

Person Independent Recognition of Head Gestures from Parametrised and Raw Signals Recorded from Inertial Measurement Unit

Numerous applications of human–machine interfaces, e.g., dedicated to persons with disabilities, require contactless handling of devices or systems. The purpose of this research is to develop a hands-free head-gesture-controlled interface that can support persons with disabilities to communicate with other people and devices, e.g., the paralyzed to signal messages or the visually impaired to handle travel aids. The hardware of the interface consists of a small stereovision rig with a built-in inertial measurement unit (IMU). The device is to be positioned on a user’s forehead. Two approaches to recognize head movements were considered. In the first approach, for various time window sizes of the signals recorded from a three-axis accelerometer and a three-axis gyroscope, statistical parameters were calculated such as: average, minimum and maximum amplitude, standard deviation, kurtosis, correlation coefficient, and signal energy. For the second approach, the focus was put onto direct analysis of signal samples recorded from the IMU. In both approaches, the accuracies of 16 different data classifiers for distinguishing the head movements: pitch, roll, yaw, and immobility were evaluated. The recordings of head gestures were collected from 65 individuals. The best results for the testing data were obtained for the non-parametric approach, i.e., direct classification of unprocessed samples of IMU signals for Support Vector Machine (SVM) classifier (95% correct recognitions). Slightly worse results, in this approach, were obtained for the random forests classifier (93%). The achieved high recognition rates of the head gestures suggest that a person with physical or sensory disability can efficiently communicate with other people or manage applications using simple head gesture sequences