Three‐dimensional (3D) local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local feature detection and description methods have already been published, these are constrained in a single dimensional scheme, i.e. either 3D or 2D methods that are applied onto multiple projections of the 3D data. However, cross‐dimensional (mixed 2D and 3D) feature detection and description are yet to be investigated. Here, the authors evaluated the performance of both single and cross‐dimensional feature detection and description methods on several 3D data sets and demonstrated the superiority of cross‐dimensional over single‐dimensional schemes.

In Low-power and Lossy Networks (LLNs), multicast communications accommodate many interesting applications ranging from firmware installation and upgrades to resource discovery and utilization. However, performing multicast in LLNs has shown to be less efficient and lacks reliability when compared with unicast. This is especially the case under radio duty cycling mechanisms employed by LLNs in order to save energy and hence extend the network lifetime. In this paper, we present a technique to enhance the performance of multicast burst forwarding in duty-cycled LLNs. The proposed mechanism has been implemented in Contiki OS and evaluated in both testbed experiments and cycle-accurate simulations. Results show a considerable increase of multicast burst throughput (more than 8 times) accompanied with an important decrease in energy consumption (about three times) and transmission latency (at least 35%). Finally, the proposed mechanism was integrated with a multicast-based service discovery protocol and demonstrated noticeable improvements.

The emerging trend towards all-IP networks provides network-layer interoperability for the Internet of Things (IoT). Service oriented architecture is expected to provide the application-layer interoperability and hence achieve IoT objectives. The first component into realizing this vision is efficient discovery of things and their services. In this paper, we present a fully distributed opportunistic approach to optimize the discovery of constrained-node services. We also discuss the impact of radio duty cycling on the performance of such a multicast-based approach. Simulation and testbed results have shown the capacity of such an approach to cope with network dynamics and provide high discovery rates, fast response times and low energy consumption.

In this paper, we propose a novel image representation approach to tackle illumination variations in stereo matching problems. Images are mapped using their Fourier transforms which are convolved with a set of monogenic filters. Frequency analysis is carried out at different scales to account for most image content. The phase congruency and the local weighted mean phase angle are then computed over all the scales. The original image is transformed into a new representation using these two mappings. This representation is invariant to illumination and contrast variations. More importantly, it is generic and can be used with most sparse as well as dense stereo matching algorithms. In addition, sequential feature matching or tracking can also benefit from our approach in varying radiometric conditions. We demonstrate the improvements introduced with our image mappings on well-established data sets in the literature as well as on our own experimental scenarios that include high dynamic range imagery. The experiments include both dense and sparse stereo and sequential matching algorithms where the latter is considered in the very challenging visual odometry framework.

The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference $p\< 0.005$ and a large practical discrimination (d 1/4 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances.