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See detailARTHuS: Adaptive Real-Time Human Segmentation in Sports through Online Distillation
Cioppa, Anthony ULiege; Deliège, Adrien ULiege; Istasse, Maxime et al

in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Proceedings (in press)

Semantic segmentation can be regarded as a useful tool for global scene understanding in many areas, including sports, but has inherent difficulties, such as the need for pixel-wise annotated training ... [more ▼]

Semantic segmentation can be regarded as a useful tool for global scene understanding in many areas, including sports, but has inherent difficulties, such as the need for pixel-wise annotated training data and the absence of well-performing real-time universal algorithms. To alleviate these issues, we sacrifice universality by developing a general method, named ARTHuS, that produces adaptive real-time match-specific networks for human segmentation in sports videos, without requiring any manual annotation. This is done by an online knowledge distillation process, in which a fast student network is trained to mimic the output of an existing slow but effective universal teacher network, while being periodically updated to adjust to the latest play conditions. As a result, ARTHuS allows to build highly effective real-time human segmentation networks that evolve through the match and that sometimes outperform their teacher. The usefulness of producing adaptive match-specific networks and their excellent performances are demonstrated quantitatively and qualitatively for soccer and basketball matches. [less ▲]

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See detailImage classification using neural networks
Van Droogenbroeck, Marc ULiege; Deliège, Adrien ULiege; Cioppa, Anthony ULiege

Patent (in press)

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See detailMid-Air: A multi-modal dataset for extremely low altitude drone flights
Fonder, Michaël ULiege; Van Droogenbroeck, Marc ULiege

in IEEE Conference on Computer Vision and Pattern Recognition Workshops Proceedings (2019, June)

Flying a drone in unstructured environments with varying conditions is challenging. To help producing better algorithms, we present Mid-Air, a multi-purpose synthetic dataset for low altitude drone ... [more ▼]

Flying a drone in unstructured environments with varying conditions is challenging. To help producing better algorithms, we present Mid-Air, a multi-purpose synthetic dataset for low altitude drone flights in unstructured environments. It contains synchronized data of multiple sensors for a total of 54 trajectories and more than 420k video frames simulated in various climate conditions. In this work, we motivate design choices, explain how the data was simulated, and present the content of the dataset. Finally, a benchmark for positioning and a benchmark for image generation tasks show how Mid-Air can be used to set up a standard evaluation method for assessing computer vision algorithms in terms of robustness and generalization. We illustrate this by providing a baseline for depth estimation and by comparing it with results obtained on an existing dataset. The Mid-Air is publicly downloadable, with additional details on the data format and organization, at http://midair.ulg.ac.be [less ▲]

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See detailDual Approaches for Elliptic Hough Transform: Eccentricity/Orientation vs Center based
Latour, Philippe ULiege; Van Droogenbroeck, Marc ULiege

in Discrete Geometry for Computer Imagery (2019, March)

Ellipse matching is the process of extracting (detecting and fitting) elliptic shapes from digital images. This process typically requires the determination of 5 parameters, which can be obtained by using ... [more ▼]

Ellipse matching is the process of extracting (detecting and fitting) elliptic shapes from digital images. This process typically requires the determination of 5 parameters, which can be obtained by using an Elliptic Hough Transform (EHT) algorithm. In this paper, we focus on Elliptic Hough Transform (EHT) algorithms based on two edge points and their associated image gradients. For this set-up, it is common to first reduce the dimension of the 5D EHT by means of some geometrical observations, and then apply a simpler HT. We present an alternative approach, with its corresponding algebraic framework, based on the pencil of bi-tangent conics, expressed in two dual forms: the point or the tangential forms. We show that, for both forms, the locus of the ellipse parameters is a line in a 5D space. With our framework, we can split the EHT into two steps. The first step accumulates 2D lines, which are computed from planar projections of the parameter locus (5D line). The second part back-projects the peak of the 2D accumulator into the 5D space, to obtain the three remaining parameters that we then accumulate in a 3D histogram, possibly represented as three separated 1D histograms. For the point equation, the first step extracts parameters related to the ellipse orientation and eccentricity, while the remaining parameters are related to the center and a sizing parameter of the ellipse. For the tangential equation, the first step is the known center extraction algorithm, while the remaining parameters are related to the ellipse half-axes and orientation. [less ▲]

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See detailForeground and background detection method
Van Droogenbroeck, Marc ULiege; Braham, Marc ULiege; Pierard, Sébastien ULiege

Patent (2019)

The present invention concerns a method for assigning a pixel to one of a foreground pixel set and a background pixel set. In this method, if a first condition is met the pixel is assigned to the ... [more ▼]

The present invention concerns a method for assigning a pixel to one of a foreground pixel set and a background pixel set. In this method, if a first condition is met the pixel is assigned to the background pixel set , and if the first condition is not met and a second condition is met, the pixel is assigned to the foreground pixel set. The method comprises a step ( S100 ) of calculating a probability that the pixel belongs to a foreground-relevant object according to a semantic segmentation algorithm, the first condition is that this probability that the pixel belongs to a foreground relevant object does not exceed a first predetermined threshold, and the second condition is that a difference between this probability that the pixel belongs to a foreground relevant object and a baseline probability for the pixel equals or exceeds a second predetermined threshold. [less ▲]

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See detailForeground and background detection method
Van Droogenbroeck, Marc ULiege; Braham, Marc ULiege; Pierard, Sébastien ULiege

Patent (2019)

The present invention concerns a method for assigning a pixel to one of a foreground pixel set and a background pixel set. In this method, if a first condition is met the pixel is assigned to the ... [more ▼]

The present invention concerns a method for assigning a pixel to one of a foreground pixel set and a background pixel set. In this method, if a first condition is met the pixel is assigned to the background pixel set, and if the first condition is not met and a second condition is met, the pixel is assigned to the foreground pixel set. The method comprises a step (S100) of calculating a probability that the pixel belongs to a foreground-relevant object according to a semantic segmentation algorithm, the first condition is that this probability that the pixel belongs to a foreground-relevant object does not exceed a first predetermined threshold, and the second condition is that a difference between this probability that the pixel belongs to a foreground-relevant object and a baseline probability for the pixel equals or exceeds a second predetermined threshold. [less ▲]

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See detailAn Effective Hit-or-Miss Layer Favoring Feature Interpretation as Learned Prototypes Deformations
Deliège, Adrien ULiege; Cioppa, Anthony ULiege; Van Droogenbroeck, Marc ULiege

in Thirty-Third AAAI Conference on Artificial Intelligence (2019, February)

Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of more effective networks, which results in a complexification of their ... [more ▼]

Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of more effective networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers, but this often comes at the expense of producing uninterpretable black boxes. In this paper, we redesign a simple capsule network to enable it to synthesize class-representative samples, called prototypes, by replacing the last layer with a novel Hit-or-Miss layer. This layer contains activated vectors, called capsules, that we train to hit or miss a fixed target capsule by tailoring a specific centripetal loss function. This possibility allows to develop a data augmentation step combining information from the data space and the feature space, resulting in a hybrid data augmentation process. We show that our network, named HitNet, is able to reach better performances than those reproduced with the initial CapsNet on several datasets, while allowing to visualize the nature of the features extracted as deformations of the prototypes, which provides a direct insight into the feature representation learned by the network. [less ▲]

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See detailPrincipes des télécommunications analogiques et numériques: manuel des répétitions
Van Droogenbroeck, Marc ULiege; Latour, Philippe ULiege; Wagner, Jean-Marc et al

Learning material (2019)

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See detailProbabilistic Framework for the Characterization of Surfaces and Edges in Range Images, with Application to Edge Detection
Lejeune, Antoine ULiege; Verly, Jacques ULiege; Van Droogenbroeck, Marc ULiege

in IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), 40(9), 2209-2222

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge ... [more ▼]

We develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images, which is useful in many applications of computer vision, such as filtering, edge detection, feature extraction, and classification. We use the geometrical nature of the data to derive an analytic expression for the joint probability density function (pdf) for the random variables used to model the ranges of a set of pixels in a local neighborhood of an image. We decompose this joint pdf by considering independently the cases where two real world points corresponding to two neighboring pixels are locally on the same real world surface or not. In particular, we show that this joint pdf is linked to the Voigt pdf and not to the Gaussian pdf as it is assumed in some applications. We apply our framework to edge detection and develop a locally adaptive algorithm that is based on a probabilistic decision rule. We show in an objective evaluation that this new edge detector performs better than prior art edge detectors. This proves the benefits of the probabilistic characterization of the local neighborhood as a tool to improve applications that involve range images. [less ▲]

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See detailMulti-timescale drowsiness characterization based on a video of a driver’s face
Massoz, Quentin ULiege; Verly, Jacques ULiege; Van Droogenbroeck, Marc ULiege

in Sensors (2018), 18(9), 2801

Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and ... [more ▼]

Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and timely warnings of drowsiness to the driver. In practice, the least intrusive, physiology-based approach is to remotely monitor, via cameras, facial expressions indicative of drowsiness such as slow and long eye closures. Since the system’s decisions are based upon facial expressions in a given time window, there exists a trade-off between accuracy (best achieved with long windows, i.e., at long timescales) and responsiveness (best achieved with short windows, i.e., at short timescales). To deal with this trade-off, we develop a multi-timescale drowsiness characterization system composed of four binary drowsiness classifiers operating at four distinct timescales (5 s, 15 s, 30 s, and 60 s) and trained jointly. We introduce a multi-timescale ground truth of drowsiness, based on the reaction times (RTs) performed during standard Psychomotor Vigilance Tasks (PVTs), that strategically enables our system to characterize drowsiness with diverse trade-offs between accuracy and responsiveness. We evaluated our system on 29 subjects via leave-one-subject-out cross-validation and obtained strong results, i.e., global accuracies of 70%, 85%, 89%, and 94% for the four classifiers operating at increasing timescales, respectively. [less ▲]

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See detailLaBGen-P-Semantic: A First Step for Leveraging Semantic Segmentation in Background Generation
Laugraud, Benjamin ULiege; Pierard, Sébastien ULiege; Van Droogenbroeck, Marc ULiege

in Journal of Imaging (2018), 4(7), 86

Given a video sequence acquired from a fixed camera, the stationary background generation problem consists of generating a unique image estimating the stationary background of the sequence. During the ... [more ▼]

Given a video sequence acquired from a fixed camera, the stationary background generation problem consists of generating a unique image estimating the stationary background of the sequence. During the IEEE Scene Background Modeling Contest (SBMC) organized in 2016, we presented the LaBGen-P method. In short, this method relies on a motion detection algorithm for selecting, for each pixel location, a given amount of pixel intensities that are most likely static by keeping the ones with the smallest quantities of motion. These quantities are estimated by aggregating the motion scores returned by the motion detection algorithm in the spatial neighborhood of the pixel. After this selection process, the background image is then generated by blending the selected intensities with a median filter. In our previous works, we showed that using a temporally-memoryless motion detection, detecting motion between two frames without relying on additional temporal information, leads our method to achieve the best performance. In this work, we go one step further by developing LaBGen-P-Semantic, a variant of LaBGen-P, the motion detection step of which is built on the current frame only by using semantic segmentation. For this purpose, two intra-frame motion detection algorithms, detecting motion from a unique frame, are presented and compared. Our experiments, carried out on the Scene Background Initialization (SBI) and SceneBackgroundModeling.NET (SBMnet) datasets, show that leveraging semantic segmentation improves the robustness against intermittent motions, background motions and very short video sequences, which are among the main challenges in the background generation field. Moreover, our results confirm that using an intra-frame motion detection is an appropriate choice for our method and paves the way for more techniques based on semantic segmentation. [less ▲]

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See detailHitNet: a neural network with capsules embedded in a Hit-or-Miss layer, extended with hybrid data augmentation and ghost capsules
Deliège, Adrien ULiege; Cioppa, Anthony ULiege; Van Droogenbroeck, Marc ULiege

in arXiv (2018), 1806.06519

Abstract Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of better networks, which results in a complexification of their ... [more ▼]

Abstract Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of better networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers. In this paper, we show how to redesign a simple network to reach excellent performances, which are better than the results reproduced with CapsNet on several datasets, by replacing a layer with a Hit-or-Miss layer. This layer contains activated vectors, called capsules, that we train to hit or miss a central capsule by tailoring a specific centripetal loss function. We also show how our network, named HitNet, is capable of synthesizing a representative sample of the images of a given class by including a reconstruction network. This possibility allows to develop a data augmentation step combining information from the data space and the feature space, resulting in a hybrid data augmentation process. In addition, we introduce the possibility for HitNet, to adopt an alternative to the true target when needed by using the new concept of ghost capsules, which is used here to detect potentially mislabeled images in the training data. [less ▲]

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See detailSupervised detection of exoplanets in high-contrast imaging sequences
Gómez González, Carlos ULiege; Absil, Olivier ULiege; Van Droogenbroeck, Marc ULiege

in Astronomy and Astrophysics (2018), 613

Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production ... [more ▼]

Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims. In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods. We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results. This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from ∼2 to ∼10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions. The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets. [less ▲]

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See detailA bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games
Cioppa, Anthony ULiege; Deliège, Adrien ULiege; Van Droogenbroeck, Marc ULiege

in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018, June)

Automatic interpretation of sports games is a major challenge, especially when these sports feature complex players organizations and game phases. This paper describes a bottom-up approach based on the ... [more ▼]

Automatic interpretation of sports games is a major challenge, especially when these sports feature complex players organizations and game phases. This paper describes a bottom-up approach based on the extraction of semantic features from the video stream of the main camera in the particular case of soccer using scene-specific techniques. In our approach, all the features, ranging from the pixel level to the game event level, have a semantic meaning. First, we design our own scene-specific deep learning semantic segmentation network and hue histogram analysis to extract pixel-level semantics for the field, players, and lines. These pixel-level semantics are then processed to compute interpretative semantic features which represent characteristics of the game in the video stream that are exploited to interpret soccer. For example, they correspond to how players are distributed in the image or the part of the field that is filmed. Finally, we show how these interpretative semantic features can be used to set up and train a semantic-based decision tree classifier for major game events with a restricted amount of training data. The main advantages of our semantic approach are that it only requires the video feed of the main camera to extract the semantic features, with no need for camera calibration, field homography, player tracking, or ball position estimation. While the automatic interpretation of sports games remains challenging, our approach allows us to achieve promising results for the semantic feature extraction and for the classification between major soccer game events such as attack, goal or goal opportunity, defense, and middle game. [less ▲]

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See detailThe LNQ25 and ELN PVT Metrics Exhibit a Good Sensitivity to Sleep Deprivation and are Independent from the Subject
Latour, Philippe ULiege; Van Droogenbroeck, Marc ULiege

in Sleep Medicine (2017), 40

Introduction Performance of people undergoing critical tasks (like driving) may be impaired completely by the lowering of their vigilance level, due to sleep deprivation for instance. This reduction of ... [more ▼]

Introduction Performance of people undergoing critical tasks (like driving) may be impaired completely by the lowering of their vigilance level, due to sleep deprivation for instance. This reduction of performance may be measured by metrics computed from the reaction times (RT) recorded during a 10min Psychomotor Vigilance Test (PVT). Here, we analyze and compare the sensitivity to sleep deprivation and the subject dependent variability of the PVT metrics performance, with a special emphasis on the time interval sizes. Materials and Methods Individuals (22 volunteers; 11 males, 11 females, mean 22.2y., range 19-34 years) follow an uninterrupted 28h sleep deprivation standard PVT protocol during which they achieved two groups of three PVT sessions (in different conditions). The first PVT of each group is in Non-SDP condition (9h30 and 10h30 Day 1) and the second and third PVT of each group are in SDP condition (2h30, 3h30, 10h30 and 11h30 day 2). The subjects fill a sleep journal during the week before the experiment. We checked that they had a normal sleep-wake cycle with no sleep deprivation, jet-lag or shift work and no medication. During the PVT of the first group, the subjects were wearing the glasses of the Phasya’s Drowsimeter. We compute the usual PVT metrics; meanRT, meanRS (Reaction Speed) and LN500 (500ms lapses number). We also compute LNQ25 (adaptive lapses number computed with a subject dependent threshold) and ELN (Expected Lapse Number, computed from a subject-dependent estimation of the lapse probability). Results We use the “Effect Size” (ES, ratio of the mean by the standard deviation of the difference of metrics in the SDP and Non-SDP conditions) to assess the sensitivity to sleep deprivation. For the 10min (resp. 1min, 3min) interval, the ES of LNQ25 and ELN are respectively 1.38 (resp. 0.95, 1.22) and 1.35 (resp. 0.85, 1.14), the ES of meanRS, meanRT and LN500 are 1.23 (resp. 0.91, 1.09), 0.81 (resp. 0.54, 0.68) and 0.85 (resp. 0.63, 0.77). We classify the intervals on which metrics are computed as SDP or non-SDP. We use a fixed threshold for the metrics, independent of the subject. In the ROC curves, the TPR (for a FPR of 10%) assesses the quality of the classification, and then also the subject independence. For the 10min (resp. 1min, 3min) interval, the TPR of LNQ25 and ELN are respectively 0.86 (resp. 0.56, 0.75) and 0.83 (resp. 0.58, 0.75), the TPR of meanRS, meanRT and LN500 are 0.42 (resp. 0.38, 0.41), 0.40 (resp. 0.39, 0.40) and 0.42 (resp. 0.24, 0.30). Conclusions We demonstrate that LNQ25 and ELN enable a quite good classification of the SDP condition for time intervals greater than or equal to 3min, independently of the subject. On the other hand, these metrics provide also a good sensitivity to sleep deprivation. They outperform the usual metrics for both criteria. For time intervals below 3min, the performances degrade first progressively and then much more quickly below 1min. [less ▲]

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See detailSemantic Background Subtraction
Braham, Marc ULiege; Pierard, Sébastien ULiege; Van Droogenbroeck, Marc ULiege

in IEEE International Conference on Image Processing (ICIP), Beijing 17-20 September 2017 (2017, September)

We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety ... [more ▼]

We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety of challenging scenarios for background subtraction. Our framework combines the information of a semantic segmentation algorithm, expressed by a probability for each pixel, with the output of any background subtraction algorithm to reduce false positive detections produced by illumination changes, dynamic backgrounds, strong shadows, and ghosts. In addition, it maintains a fully semantic background model to improve the detection of camouflaged foreground objects. Experiments led on the CDNet dataset show that we managed to improve, significantly, almost all background subtraction algorithms of the CDNet leaderboard, and reduce the mean overall error rate of all the 34 algorithms (resp. of the best 5 algorithms) by roughly 50% (resp. 20%). Note that a C++ implementation of the framework is available at http://www.telecom.ulg.ac.be/semantic. [less ▲]

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See detailA two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Geurts, Pierre ULiege et al

in Advanced Concepts for Intelligent Vision Systems (2017, September)

In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we ... [more ▼]

In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we first estimate the orientation of the standing person seen by the camera and then use this information to dynamically select a pose estimation model suited for this particular orientation. We evaluated our method on a public dataset of realistic depth images with precise ground truth joints location. Our experiments show that our method decreases the error of a state-of-the-art pose estimation method by 30%, or reduces the size of the needed learning set by a factor larger than 10. [less ▲]

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See detailIs a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?
Laugraud, Benjamin ULiege; Van Droogenbroeck, Marc ULiege

in Advanced Concepts for Intelligent Vision Systems (2017, September)

The stationary background generation problem consists in generating a unique image representing the stationary background of a given video sequence. The LaBGen background generation method combines a ... [more ▼]

The stationary background generation problem consists in generating a unique image representing the stationary background of a given video sequence. The LaBGen background generation method combines a pixel-wise median filter and a patch selection mechanism based on a motion detection performed by a background subtraction algorithm. In our previous works related to LaBGen, we have shown that, surprisingly, the frame difference algorithm provides the most effective motion detection on average. Compared to other background subtraction algorithms, it detects motion between two frames without relying on additional past frames, and is therefore memoryless. In this paper, we experimentally check whether the memoryless property is truly relevant for LaBGen, and whether the effective motion detection provided by the frame difference is not an isolated case. For this purpose, we introduce LaBGen-OF, a variant of LaBGen leverages memoryless dense optical flow algorithms for motion detection. Our experiments show that using a memoryless motion detector is an adequate choice for our background generation framework, and that LaBGen-OF outperforms LaBGen on the SBMnet dataset. We further provide an open-source C++ implementation of both methods at http://www.telecom.ulg.ac.be/labgen. [less ▲]

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See detailVIP: Vortex Image Processing package for high-contrast direct imaging
Gómez González, Carlos ULiege; Wertz, Olivier; Absil, Olivier ULiege et al

in Astronomical Journal (2017), 154

We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to ... [more ▼]

We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular di↵erential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompass- ing pre- and post-processing algorithms, potential sources position and flux estimation, and sensitivity curves generation. Among the reference point-spread function subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithm capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization (NMF), which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP we investigated the presence of additional companions around HR8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github.com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library. [less ▲]

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