Wave patterns generated by an axisymmetric obstacle in a two-layer flow

Gravity waves generated by a moving obstacle in a two-layer stratified fluid are investigated. The experimental configuration is three-dimensional with an axisymmetric obstacle which is towed in one of the two layers. The experimental method used in the present study is based on a stereoscopic technique allowing the 3D reconstruction of the interface between the two layers. Investigation into the wave pattern as a function of the Froude number, Fr, based on the relative density of the fluid layers and the velocity of the towed obstacle is presented. Specific attention is paid to the transcritical regime for which Fr is close to one. Potential energy trapped in the wave field patterns is also extracted from the experimental results and is analyzed as a function of both the Froude number, Fr, and the transcritical similarity parameter Γ. In particular, a remarkable increase in the potential energy around Fr = 1 is observed and a scaling allowing to assemble data resulting from different experimental parameters is proposed

Inimeste tuvastamine ning kauguse hindamine kasutades kaamerat ning YOLOv3 tehisnärvivõrku

Inimestega vähemalt samal tasemel keskkonnast aru saamine masinate poolt oleks kasulik paljudes domeenides. Mitmed erinevad sensored aitavad selle ülesande juures, enim on kasutatud kaameraid. Objektide tuvastamine on tähtis osa keskkonnast aru saamisel. Selle täpsus on viimasel ajal palju paranenud tänu arenenud masinõppe meetoditele nimega konvolutsioonilised närvivõrgud (CNN), mida treenitakse kasutades märgendatud kaamerapilte. Monokulaarkaamerapilt sisaldab 2D infot, kuid ei sisalda sügavusinfot. Teisalt, sügavusinfo on tähtis näiteks isesõitvate autode domeenis. Inimeste ohutus tuleb tagada näiteks töötades autonoomsete masinate läheduses või kui jalakäija ületab teed autonoomse sõiduki eest. Antud töös uuritakse võimalust, kuidas tuvastada inimesi ning hinnata nende kaugusi samaaegselt, kasutades RGB kaamerat, eesmärgiga kasutada seda autonoomseks sõitmiseks maastikul. Selleks täiustatakse hetkel parimat objektide tuvastamise konvolutsioonilist närvivõrku YOLOv3 (ingl k. You Only Look Once). Selle töö väliselt on simulatsioonitarkvaradega AirSim ning Unreal Engine loodud lumine metsamaastik koos inimestega erinevates kehapoosides. YOLOv3 närvivõrgu treenimiseks võeti simulatsioonist välja vajalikud andmed, kasutades skripte. Lisaks muudeti närvivõrku, et lisaks inimese asukohta tuvastavale piirikastile väljastataks ka inimese kauguse ennustus. Antud töö tulemuseks on mudel, mille ruutkesmine viga RMSE (ingl k. Root Mean Square Error) on 2.99m objektidele kuni 50m kaugusel, säilitades samaaegselt originaalse närvivõrgu inimeste tuvastamise täpsuse. Võrreldavate meetodite RMSE veaks leiti 4.26m (teist andmestikku kasutades) ja 4.79m (selles töös kasutatud andmestikul), mis vastavalt kasutavad kahte eraldiseisvat närvivõrku ning LASSO meetodit. See näitab suurt parenemist võrreldes teiste meetoditega. Edasisteks eesmärkideks on meetodi treenimine ning testimine päris maailmast kogutud andmetega, et näha, kas see üldistub ka sellistele keskkondadele.Making machines perceive environment better or at least as well as humans would be beneficial in lots of domains. Different sensors aid in this, most widely used of which is monocular camera. Object detection is a major part of environment perception and its accuracy has greatly improved in the last few years thanks to advanced machine learning methods called convolutional neural networks (CNN) that are trained on many labelled images. Monocular camera image contains two dimensional information, but contains no depth information of the scene. On the other hand, depth information of objects is important in a lot of areas related to autonomous driving, e.g. working next to an automated machine, pedestrian crossing a road in front of an autonomous vehicle, etc. This thesis presents an approach to detect humans and to predict their distance from RGB camera for off-road autonomous driving. This is done by improving YOLO (You Only Look Once) v3[1], a state-of-the-art object detection CNN. Outside of this thesis, an off-road scene depicting a snowy forest with humans in different body poses was simulated using AirSim and Unreal Engine. Data for training YOLOv3 neural network was extracted from there using custom scripts. Also, network was modified to not only predict humans and their bounding boxes, but also their distance from camera. RMSE of 2.99m for objects with distances up to 50m was achieved, while maintaining similar detection accuracy to the original network. Comparable methods using two neural networks and a LASSO model gave 4.26m (in an alternative dataset) and 4.79m (with dataset used is this work) RMSE respectively, showing a huge improvement over the baselines. Future work includes experiments with real-world data to see if the proposed approach generalizes to other environments

Complementary geometric and optical information for match-propagation-based 3D reconstruction

International audienceIn this work, we consider the problem of propagation-based matching for 3D reconstruction, which deals with expanding a limited set of correspondences towards a quasi-dense map across two views. In general, propagation based methods capture well the scene structure. However, the recovered geometry often presents an overall choppy na-ture which can be attributed to matching errors and abrupt variations in the estimated local affine transformations. We propose to control the reconstructed geometry by means of a local patch fitting which corrects both the matching locations and affine transformations throughout the propagation process. In this way, matchings that propagate from geo-metrically consolidated locations bring coherence to both positions and affine transformations. Results of our approach are not only more visu-ally appealing but also more accurate and complete as substantiated by results on standard benchmarks

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

Scene Flow Estimation by Growing Correspondence Seeds

International audienceA simple seed growing algorithm for estimating scene flow in a stereo setup is presented. Two calibrated and synchronized cameras observe a scene and output a sequence of image pairs. The algorithm simultaneously computes a disparity map between the image pairs and optical flow maps between consecutive images. This, together with calibration data, is an equivalent representation of the 3D scene flow, i.e. a 3D velocity vector is associated with each reconstructed point. The proposed method starts from correspondence seeds and propagates these correspondences to their neighborhood. It is accurate for complex scenes with large motions and produces temporallycoherent stereo disparity and optical flow results. The algorithm is fast due to inherent search space reduction. An explicit comparison with recent methods of spatiotemporal stereo and variational optical and scene flow is provided

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions.  A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique.  Based on this, the closest obstacles are detected through the use of k-means segmentation.  The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset