Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag

Multipass SAR interferometry. A tool for geologic analysis

This paper investigates how the information content of repeat pass satellite SAR interferometric (INSAR) data can be used to provide the geologist with a tool which can improve his ability and efficacy in the geologic analysis of SAR imagery. INSAR processing produces interferometric fringes, coherence and amplitude images. To produce an interferometric DEM phase unwrapping is a critical step. For phase unwrapping, we propose the WLMS (Weighted Least Mean Square) estimation of the phase, which is a generalization of the least-mean square method. The crucial step in WLMS approach is the weighting procedure. We propose a weighting algorithm based on the fusion of a priori information extracted from different interferometric products. These different information channels—DEM, amplitude and coherence—can be effectively fused to convey information to the geologic interpreter using 3D stereoscopic visualization;SAR stereo pairs were artificially generated using the interferometric DEM and the intensity image or the coherence image of the area overlaid. In order to ascertain the performance of the procedure a number of tests were carried out over various sites in Matese (Southern Italy), which has a fairly demanding topography, using ERS SAR tandem data. The results demonstrate that WLMS unwrapping method is sufficiently robust in capturing the morphology of the area and that stereoscopic visualization greatly facilitates geologic interpretation and the observation of detailed features of the terrain

Adaptation of Person Re-identification Models for On-boarding New Camera(s)

Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re- identification problem, where one or multiple new cameras may be temporarily on-boarded into an ex- isting system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dy- namic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute

A decision support system for disaster prevention in Urban Areas

This paper presents the use of Human Behavior Modeling for Disaster Relief and Emergency Management. The authors propose an innovative MS2G (Modeling, Interoperable Simulation and Serious Game) using Intelligent Agents to reproduce a complex scenario used for Verification, Validation and Accreditation of the approach. The case study is inspired to South Sudan situation and to the necessity to provide accommodations, food, health care services, security and administrative support to a large number of IDPs (Internally Displaced Persons) over a wide area. The simulator includes camp preparation and installation, air dr ops, logistics network creation while the model includes populations, entities and units as well as different equipment (e.g. cargo planes, helicopters, ground units, etc.

Seeing the sound: a new multimodal imaging device for computer vision

Audio imaging can play a fundamental role in computer vision, in particular in automated surveillance, boosting the accuracy of current systems based on standard optical cameras. We present here a new hybrid device for acousticoptic imaging, whose characteristics are tailored to automated surveillance. In particular, the device allows realtime, high frame rate generation of an acoustic map, overlaid over a standard optical image using a geometric calibration of audio and video streams. We demonstrate the potentialities of the device for target tracking on three challenging setup showing the advantages of using acoustic images against baseline algorithms on image tracking. In particular, the proposed approach is able to overcome, often dramatically, visual tracking with state-of-art algorithms, dealing efficiently with occlusions, abrupt variations in visual appearence and camouflage. These results pave the way to a widespread use of acoustic imaging in application scenarios such as in surveillance and security

A Novel Dictionary Learning based Multiple Instance Learning Approach to Action Recognition from Videos

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Auditory dialog analysis and understanding by generative modelling of interactional dynamics

In the last few years, the interest in the analysis of human behavioral schemes has dramatically grown, in particular for the interpretation of the communication modalities called social signals. They represent well defined interaction patterns, possibly unconscious, characterizing different conversational situations and behaviors in general. In this paper, we illustrate an automatic system based on a generative structure able to analyze conversational scenarios. The generative model is composed by integrating a Gaussian mixture model and the (observed) influence model, and it is fed with a novel kind of simple low-level auditory social signals, which are termed steady conversational periods (SCPs). These are built on duration of continuous slots of silence or speech, taking also into account conversational turn-taking. The interactional dynamics built upon the transitions among SCPs provide a behavioral blueprint of conversational settings without relying on segmental or continuous phonetic features. Our contribution here is to show the effectiveness of our model when applied on dialogs classification and clustering tasks, considering dialogs between adults and between children and adults, in both flat and arguing discussions, and showing excellent performances also in comparison with state-of-the-art frameworks

Combining free energy score spaces with information theoretic kernels: Application to scene classification

Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embed-ding is a mapping from the object space into a fixed dimensional score space, induced by a generative model, usually learned from data. The fixed dimensionality of these generative score spaces makes them adequate for discriminative learning of classifiers, thus bringing together the best of the discriminative and generative paradigms. In particular, it was recently shown that this hybrid ap-proach outperforms a classifier obtained directly for the generative model upon which the score space was built. Using a generative embedding involves two steps: (i) defining and learning the generative model and using it to build the embed-ding; (ii) discriminatively learning a (maybe kernel) classifier on the adopted score space. The literature on generative embeddings is es-sentially focused on step (i), usually using some standard off-the-shelf tool for step (ii). In this paper, we adopt a different approach, by focusing also on the discriminative learning step. In particular, we combine two very recent and top performing tools in each of the steps: (i) the free energy score space; (ii) non-extensive information theoretic kernels. In this paper, we apply this methodology in scene recognition. Experimental results on two benchmark datasets shows that our approach yields state-of-the-art performance. Index Terms — Scene categorization, generative embeddings, score spaces, information theoretic kernels