Efficient Localization of Discontinuities in Complex Computational Simulations

Surrogate models for computational simulations are input-output approximations that allow computationally intensive analyses, such as uncertainty propagation and inference, to be performed efficiently. When a simulation output does not depend smoothly on its inputs, the error and convergence rate of many approximation methods deteriorate substantially. This paper details a method for efficiently localizing discontinuities in the input parameter domain, so that the model output can be approximated as a piecewise smooth function. The approach comprises an initialization phase, which uses polynomial annihilation to assign function values to different regions and thus seed an automated labeling procedure, followed by a refinement phase that adaptively updates a kernel support vector machine representation of the separating surface via active learning. The overall approach avoids structured grids and exploits any available simplicity in the geometry of the separating surface, thus reducing the number of model evaluations required to localize the discontinuity. The method is illustrated on examples of up to eleven dimensions, including algebraic models and ODE/PDE systems, and demonstrates improved scaling and efficiency over other discontinuity localization approaches

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images

Käden konfiguraatioiden estimointi viittomakielisistä videoista

A computer vision system is presented that can locate and classify the handshape from an individual sign-language video frame, using a synthetic 3D model. The system requires no training data; only phonetically-motivated descriptions of sign-language hand configuration classes are required. Experiments were conducted with realistically low-quality sign-language video dictionary footage to test various features and metrics to fix the camera parameters of a fixed synthetic hand model to find the best match of the model to the input frame. Histogram of Oriented Gradients (HOG) features with Euclidean distance turned out to be suitable for this purpose. A novel approach, called Trimmed HOGs, with Earth Mover's Distance, as well as simplistic contours and Canny edges with the chamfer distance, also performed favorably. Minimizing the cost function built from these measures with gradient descent optimization further improved the camera parameter fitting results. Classification of images of handshapes into hand configuration classes with nearest-neighbor classifiers built around the chamfer distance between contours and Canny edges, and chi^2 distance between Pyramidal HOG descriptors turned out to yield reasonable accuracy. Although the system displayed only moderate success rates in a full 26-class scenario, the system was able to reach nearly perfect discriminatory accuracy in a binary classification case, and up to 40 % accuracy when images from a restricted set of 12 classes were classified into six hand configuration groups. Considering that the footage used to evaluate the system was of very poor quality, with future improvements, the methods evaluated may be used as basis for a practical system for automatic annotation of sign language video corpora.Työssä esitetään tietokonenäköjärjestelmä, joka pystyy löytämään ja luokittelemaan käsimuotoja yksittäisistä viittomakielisten videoiden ruuduista synteettistä 3D-mallia käyttäen. Järjestelmä ei vaadi opetusdataa; pelkät foneettisesti motivoidut kuvaukset käden konfiguraatioluokista riittävät. Kokeissa testattiin erilaisia piirteitä ja metriikoita staattisen käsimallin kameraparametrien kiinnittämiseksi, jotta löydettäisiin paras vastaavuus mallin ja syötekuvan välillä. Kokeet ajettiin realistisen heikkolaatuisella videoaineistolla. Gradienttihistogrammit euklidisella etäisyydellä osoittautuivat sopiviksi tähän tarkoitukseen. Uusi työssä esitetty lähestymistapa, jota kutsutaan trimmatuksi gradienttihistogrammiksi, maansiirtäjän etäisyyden (Earth Mover's Distance) kanssa toimi myös hyvin, kuten myös yksinkertaiset ääriviivat ja Canny-reunat chamfer-etäisyyden kanssa. Gradienttilaskeumaoptimointi (gradient descent optimization) paransi kameraparametrien sovitustuloksia. Syötekuvia luokiteltiin lähimmän naapurin luokittimilla, ja ääriviiva- ja Canny-reunapiirteiden chamfer-etäisyyteen sekä pyramidisten gradienttihistogrammien chi^2-etäisyyteen pohjautuvat luokittimet osoittautuivat toimiviksi. Vaikka järjestelmän luokittelutarkkuus jäi vaatimattomaksi täydessä 26 luokan tapauksessa, järjestelmä saavutti liki täydellisen luokittelutarkkuuden binääriluokittelutapauksessa, ja saavutti jopa 40 % tarkkuuden, kun 12 luokan osajoukosta poimittuja kuvia luokiteltiin kuuteen eri ryhmään. Ottaen huomioon aineiston heikosta laadusta johtuvan vaativuuden, voidaan pitää uskottavana, että esitettyjä menetelmiä voidaan käyttää käytännöllisen korpusaineiston automaattiseen annotointiin soveltuvan järjestelmän pohjana

Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images