Relation between vibrotactile perception thresholds and reductions in finger blood flow induced by vibration of the hand at frequencies in the range 8–250 Hz

Purpose: this study investigated how the vasoconstriction induced by vibration depends on the frequency of vibration when the vibration magnitude is defined by individual thresholds for perceiving vibration [i.e. sensation levels (SL)].Methods: fourteen healthy subjects attended the laboratory on seven occasions: for six vibration frequencies (8, 16, 31.5, 63, 125, or 250 Hz) and a static control condition. Finger blood flow (FBF) was measured in the middle fingers of both hands at 30-second intervals during five successive periods: (i) no force or vibration, (ii) 2-N force, no vibration, (iii) 2-N force, vibration, (iv) 2-N force, no vibration, (v) no force or vibration. During period (iii), vibration was applied to the right thenar eminence via a 6-mm diameter probe during ten successive 3-min periods as the vibration magnitude increased in ten steps (?10 to +40 dB SL).Results: with vibration at 63, 125, and 250 Hz, there was vasoconstriction on both hands when the vibration magnitude reached 10 dB SL. With vibration at 8, 16, and 31.5 Hz, there was no significant vasoconstriction until the vibration reached 25 dB SL. At all frequencies, there was greater vasoconstriction with greater magnitudes of vibration.Conclusions: it is concluded that at the higher frequencies (63, 125, and 250 Hz), the Pacinian channel mediates vibrotactile sensations near threshold and vasoconstriction occurs when vibration is perceptible. At lower frequencies (8, 16, and 31.5 Hz), the Pacinian channel does not mediate sensations near threshold and vasoconstriction commences at greater magnitudes when the Pacinian channel is activate

Real-Time RGB-D based Template Matching Pedestrian Detection

Pedestrian detection is one of the most popular topics in computer vision and robotics. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. In this paper, we propose different approaches for training the depth-based template. We train multiple templates for handling issues due to various upper-body orientations of the pedestrians and different levels of detail in depth-map of the pedestrians with various distances from the camera. And, we take into account the degree of reliability for different regions of sliding window by proposing the weighted template approach. Furthermore, we combine the depth-detector with an appearance based detector as a verifier to take advantage of the appearance cues for dealing with the limitations of depth data. We evaluate our method on the challenging ETH dataset sequence. We show that our method outperforms the state-of-the-art approaches.Comment: published in ICRA 201

Extension of incompressible surfaces on the boundary of 3-manifolds

An incompressible surface $F$ on the boundary of a compact orientable 3-manifold $M$ is arc-extendible if there is an arc $\gamma$ on $\partial M -$ Int $F$ such that $F \cup N(\gamma)$ is incompressible, where $N(\gamma)$ is a regular neighborhood of $\gamma$ in $\partial M$. Suppose for simplicity that $M$ is irreducible, and $F$ has no disk components. If $M$ is a product $F\times I$, or if $\partial M - F$ is a set of annuli, then clearly $F$ is not arc-extendible. The main theorem of this paper shows that these are the only obstructions for $F$ to be arc-extendible

Triplet-based Deep Similarity Learning for Person Re-Identification

In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.Comment: ICCV Workshops 201

Object Recognition from very few Training Examples for Enhancing Bicycle Maps

In recent years, data-driven methods have shown great success for extracting information about the infrastructure in urban areas. These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples. While large datasets have been published regarding cars, for cyclists very few labeled data is available although appearance, point of view, and positioning of even relevant objects differ. Unfortunately, labeling data is costly and requires a huge amount of work. In this paper, we thus address the problem of learning with very few labels. The aim is to recognize particular traffic signs in crowdsourced data to collect information which is of interest to cyclists. We propose a system for object recognition that is trained with only 15 examples per class on average. To achieve this, we combine the advantages of convolutional neural networks and random forests to learn a patch-wise classifier. In the next step, we map the random forest to a neural network and transform the classifier to a fully convolutional network. Thereby, the processing of full images is significantly accelerated and bounding boxes can be predicted. Finally, we integrate data of the Global Positioning System (GPS) to localize the predictions on the map. In comparison to Faster R-CNN and other networks for object recognition or algorithms for transfer learning, we considerably reduce the required amount of labeled data. We demonstrate good performance on the recognition of traffic signs for cyclists as well as their localization in maps.Comment: Submitted to IV 2018. This research was supported by German Research Foundation DFG within Priority Research Programme 1894 "Volunteered Geographic Information: Interpretation, Visualization and Social Computing