In this paper we propose a novel boosting-based sliding window solution for object detection which can keep up with the precision of the state-of-the art deep learning approaches, while being 10 to 100 times faster. The solution takes advantage of multisensorial perception and exploits information from color, motion and depth. We introduce multimodal multiresolution filtering of signal intensity, gradient magnitude and orientation channels, in order to capture structure at multiple scales and orientations. To achieve scale invariant classification features, we analyze the effect of scale change on features for different filter types and propose a correction scheme. To improve recognition we incorporate 2D and 3D context by generating spatial, geometric and symmetrical channels. Finally, we evaluate the proposed solution on multiple benchmarks for the detection of pedestrians, cars and bicyclists. We achieve competitive results at over 25 frames per second.

This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.

This paper describes a novel Software-Defined Networking (SDN) Controller architecture that is built on Model-Driven Software Engineering (MDSE) principles. It supports both the “classic” OpenFlow-based approach to SDN and emerging model-driven network management/programmability technologies, such as NETCONF/YANG. The architecture was first implemented in the OpenDaylight Project Hydrogen release, and it is being further evolved in subsequent OpenDaylight releases.

This paper presents a new automatic image annotation algorithm. First, we introduce a new similarity measure between images: compactness. This uses low level visual descriptors for determining the similarity between two images. Compactness shows how close test image features lie to training image feature cluster centers. The measure provides the core for a k-nearest neighbor type image annotation method. Afterward, a formalism for defining different transfer techniques is devised and several label transfer techniques are provided. The method as whole is evaluated on four image annotation benchmarks. The results on these sets validate the accuracy of the approach, which outperforms many state-of-the-art annotation methods. The method presented here requires a simple training process, efficiently combines different feature types and performs better than complex learning algorithms, even in this incipient form. The main contributions of this paper are the usage of compactness as a similarity measure that enables efficient low level feature comparison and an annotation algorithm based on label transfer.