Hichem Sahli

Multiscale gradient watersheds of color images

We present a new framework for the hierarchical segmentation of color images. The proposed scheme comprises a nonlinear scale-space with vector-valued gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer, namely strong edges, smooth segments and detailed segments. The scale-space is based on a vector-valued diffusion that uses the Additive Operator Splitting numerical scheme. Furthermore, we introduce the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes.

A model-based approach to the automatic extraction of linear features from airborne images

The authors describe a model-based method for the automatic extraction of linear features, like roads and paths, from aerial images. The paper combines and extends two earlier approaches for road detection in SAR satellite images and presents the modifications needed for the application domain of airborne image analysis together with representative results.

A Stochastic Framework for the Identification of Building Rooftops Using a Single Remote Sensing Image

The identification of building rooftops from a single image, without the use of auxiliary 3-D information like stereo pairs or digital elevation models, is a very challenging and difficult task in the area of remote sensing. The existing methodologies rarely tackle the problem of 3-D object identification, like buildings, from a purely stochastic viewpoint. Our approach is based on a stochastic image interpretation model, which combines both 2-D and 3-D contextual information of the imaged scene. Building rooftop hypotheses are extracted using a contour-based grouping hierarchy that emanates from the principles of perceptual organization. We use a Markov random field model to describe the dependencies between all available hypotheses with regard to a globally consistent interpretation. The hypothesis verification step is treated as a stochastic optimization process that operates on the whole grouping hierarchy to find the globally optimal configuration for the locally interacting grouping hypotheses, providing also an estimate of the height of each extracted rooftop. This paper describes the main principles of our method and presents building detection results on a set of synthetic and airborne images.

Investigation of Time–Frequency Features for GPR Landmine Discrimination

Ground-penetrating radar (GPR) is capable to detect plastic antipersonnel landmines as well as other subsurface targets. In order to reduce false alarms, an option of automatic landmine discrimination from neutral minelike targets would be very useful. This paper presents a possibility for such discrimination and analyzes it experimentally. The authors investigate time-frequency features of an ultrawideband (UWB) target response for the discrimination between buried landmines and other objects. The discrimination method includes the extraction of an early-time target impulse response, its time-frequency transformation, and the extraction of time-frequency features based on a singular value decomposition of the transformed image. In order to take into account the changes in the UWB target signals, the experimental conditions are completely controlled to focus on the behavior of the targets response with respect to its depth and the horizontal position of the GPR above it. The dependence of the features on the GPR bandwidth is analyzed as well. The Mahalanobis distance is used as a criterion for optimal discrimination. The obtained results define the best features and conditions when the landmine discrimination is successful. For comparison, the discriminant power of the proposed features has been tested on a dataset, acquired during a field campaign in Angola

Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks

This paper presents our system design for the Audio-Visual Emotion Challenge (

Improved thermal analysis of buried landmines

AV^{+}EC

A hierarchical Markovian model for multiscale region-based classification of vector-valued images

2015). Besides the baseline features, we extract from audio the functionals on low-level descriptors (LLDs) obtained via the YAAFE toolbox, and from video the Local Phase Quantization from Three Orthogonal Planes (LPQ-TOP) features. From the physiological signals, we extract 52 electro-cardiogram (ECG) features and 22 electro-dermal activity (EDA) features from various analysis domains. The extracted features along with the

Infrared Thermography for Buried Landmine Detection: Inverse Problem Setting

AV^{+}EC

CoRoBa, a multi mobile robot control and simulation framework

2015 baseline features of audio, ECG or EDA are concatenated for a further feature selection step, in which the concordance correlation coefficient (CCC), instead of the usual Pearson correlation coefficient (CC), has been used as objective function. In addition, offsets between the features and the arousal/valence labels are considered in both feature selection and modeling of the affective dimensions. For the fusion of multimodal features, we propose a Deep Bidirectional Long Short-Term Memory Recurrent Neural Network (DBLSTM-RNN) based multimodal affect prediction framework, in which the initial predictions from the single modalities via the DBLSTM-RNNs are firstly smoothed with Gaussian smoothing, then input into a second layer of DBLSTM-RNN for the final prediction of affective state. Experimental results show that our proposed features and the DBLSTM-RNN based fusion framework obtain very promising results. On the development set, the obtained CCC is up to 0.824 for arousal and 0.688 for valence, and on the test set, the CCC is 0.747 for arousal and 0.609 for valence.

Robust shape-based head tracking

In this paper, we address the problem of the detection and identification of surface-laid and shallowly buried landmines from measured infrared images. A three-dimensional thermal model has been developed to study the effect of the presence of landmines in the thermal signature of the bare soil. Based on this model, a target identification procedure is proposed aiming at detecting and classifying the anomalies found on the soil thermal signature. In our approach, landmines are thought of as a thermal barrier in the natural flow of the heat inside the soil, which produces a perturbation of the expected thermal pattern on the surface. The detection of these perturbations will put into evidence the presence of potential mine targets. We propose an iterative procedure to classify the detected perturbations as mines or nonmines and to estimate their depth of burial. This paper describes the main principles of our method and illustrates classification results on a set of acquired images. Qualitative and quantitative comparisons with independent component analysis are also given.