Spatiotemporal oriented energies for spacetime stereo

This paper presents a novel approach to recovering tem-porally coherent estimates of 3D structure of a dynamic scene from a sequence of binocular stereo images. The approach is based on matching spatiotemporal orientation distributions between left and right temporal image streams, which encapsulates both local spatial and temporal struc-ture for disparity estimation. By capturing spatial and tem-poral structure in this unified fashion, both sources of in-formation combine to yield disparity estimates that are nat-urally temporal coherent, while helping to resolve matches that might be ambiguous when either source is considered alone. Further, by allowing subsets of the orientation mea-surements to support different disparity estimates, an ap-proach to recovering multilayer disparity from spacetime stereo is realized. The approach has been implemented with real-time performance on commodity GPUs. Empir-ical evaluation shows that the approach yields qualitatively and quantitatively superior disparity estimates in compari-son to various alternative approaches, including the ability to provide accurate multilayer estimates in the presence of (semi)transparent and specular surfaces. 1

Enhancing low-level features with mid-level cues

Local features have become an essential tool in visual recognition. Much of the progress in computer vision over the past decade has built on simple, local representations such as SIFT or HOG. SIFT in particular shifted the paradigm in feature representation. Subsequent works have often focused on improving either computational efficiency, or invariance properties. This thesis belongs to the latter group. Invariance is a particularly relevant aspect if we intend to work with dense features. The traditional approach to sparse matching is to rely on stable interest points, such as corners, where scale and orientation can be reliably estimated, enforcing invariance; dense features need to be computed on arbitrary points. Dense features have been shown to outperform sparse matching techniques in many recognition problems, and form the bulk of our work. In this thesis we present strategies to enhance low-level, local features with mid-level, global cues. We devise techniques to construct better features, and use them to handle complex ambiguities, occlusions and background changes. To deal with ambiguities, we explore the use of motion to enforce temporal consistency with optical flow priors. We also introduce a novel technique to exploit segmentation cues, and use it to extract features invariant to background variability. For this, we downplay image measurements most likely to belong to a region different from that where the descriptor is computed. In both cases we follow the same strategy: we incorporate mid-level, "big picture" information into the construction of local features, and proceed to use them in the same manner as we would the baseline features. We apply these techniques to different feature representations, including SIFT and HOG, and use them to address canonical vision problems such as stereo and object detection, demonstrating that the introduction of global cues yields consistent improvements. We prioritize solutions that are simple, general, and efficient. Our main contributions are as follows: (a) An approach to dense stereo reconstruction with spatiotemporal features, which unlike existing works remains applicable to wide baselines. (b) A technique to exploit segmentation cues to construct dense descriptors invariant to background variability, such as occlusions or background motion. (c) A technique to integrate bottom-up segmentation with recognition efficiently, amenable to sliding window detectors.Les "features" locals s'han convertit en una eina fonamental en el camp del reconeixement visual. Gran part del progrés experimentat en el camp de la visió per computador al llarg de l'última decada es basa en representacions locals de baixa complexitat, com SIFT o HOG. SIFT, en concret, ha canviat el paradigma en representació de característiques visuals. Els treballs que l'han succeït s'acostumen a centrar o bé a millorar la seva eficiencia computacional, o bé propietats d'invariança. El treball presentat en aquesta tesi pertany al segon grup. L'invariança es un aspecte especialment rellevant quan volem treballab amb "features" denses, és a dir per a cada pixel. La manera tradicional d'atacar el problema amb "features" de baixa densitat consisteix en seleccionar punts d'interés estables, com per exemple cantonades, on l'escala i l'orientació poden ser estimades de manera robusta. Les "features" denses, per definició, han de ser calculades en punts arbitraris de la imatge. S'ha demostrat que les "features" denses obtenen millors resultats en tècniques de correspondència per a molts problemes en reconeixement, i formen la major part del nostre treball. En aquesta tesi presentem estratègies per a enriquir "features" locals de baix nivell amb "cues" o dades globals, de mitja complexitat. Dissenyem tècniques per a construïr millors "features", que usem per a atacar problemes tals com correspondències amb un grau elevat d'ambigüetat, oclusions, i canvis del fons de la imatge. Per a atacar ambigüetats, explorem l'ús del moviment per a imposar consistència espai-temporal mitjançant informació d'"optical flow". També presentem una tècnica per explotar dades de segmentació que fem servir per a extreure "features" invariants a canvis en el fons de la imatge. Aquest mètode consisteix en atenuar els components de la imatge (i per tant les "features") que probablement corresponguin a regions diferents a la del descriptor que estem calculant. En ambdós casos seguim la mateixa estratègia: la nostra voluntat és incorporar dades globals d'un nivell de complexitat mitja a la construcció de "features" locals, que procedim a utilitzar de la mateixa manera que les "features" originals. Aquestes tècniques són aplicades a diferents tipus de representacions, incloent SIFT i HOG, i mostrem com utilitzar-les per a atacar problemes fonamentals en visió per computador tals com l'estèreo i la detecció d'objectes. En aquest treball demostrem que introduïnt informació global en la construcció de "features" locals podem obtenir millores consistentment. Donem prioritat a solucions senzilles, generals i eficients. Aquestes són les principals contribucions de la tesi: (a) Una tècnica per a reconstrucció estèreo densa mitjançant "features" espai-temporals, amb l'avantatge respecte a treballs existents que podem aplicar-la a càmeres en qualsevol configuració geomètrica ("wide-baseline"). (b) Una tècnica per a explotar dades de segmentació dins la construcció de descriptors densos, fent-los invariants a canvis al fons de la imatge, i per tant a problemes com les oclusions en estèreo o objectes en moviment. (c) Una tècnica per a integrar segmentació de manera ascendent ("bottom-up") en problemes de reconeixement d'una manera eficient, dissenyada per a detectors de tipus "sliding window"

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

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Egomotion estimation using binocular spatiotemporal oriented energy

Camera egomotion estimation is concerned with the recovery of a camera's motion (e.g., instantaneous translation and rotation) as it moves through its environment. It has been demonstrated to be of both theoretical and practical interest. This thesis documents a novel algorithm for egomotion estimation based on binocularly matched spatiotemporal oriented energy distributions. Basing the estimation on oriented energy measurements makes it possible to recover egomotion without the need to establish temporal correspondences or convert disparity into 3D world coordinates. There sulting algorithm has been realized in software and evaluated quantitatively on a novel laboratory dataset with ground truth as well as qualitatively on both indoor and outdoor real-world datasets. Performance is evaluated relative to comparable alternative algorithms and shown to exhibit best overall performance

Segmentation-based Method of Increasing The Depth Maps Temporal Consistency

In this paper, a modification of the graph-based depth estimation is presented. The purpose of proposed modification is to increase the quality of estimated depth maps, reduce the time of the estimation, and increase the temporal consistency of depth maps. The modification is based on the image segmentation using superpixels, therefore in the first step of the proposed modification a segmentation of previous frames is used in the currently processed frame in order to reduce the overall time of the depth estimation. In the next step, a depth map from the previous frame is used in the depth map optimization as the initial values of a depth map estimated for the current frame. It results in the better representation of silhouettes of objects in depth maps and in the reduced computational complexity of the depth estimation process. In order to evaluate the performance of the proposed modification the authors performed the experiment for a set of multiview test sequences that varied in their content and an arrangement of cameras. The results of the experiments confirmed the increase of the depth maps quality — the quality of depth maps calculated with the proposed modification is higher than for the unmodified depth estimation method, apart from the number of the performed optimization cycles. Therefore, use of the proposed modification allows to estimate a depth of the better quality with almost 40% reduction of the estimation time. Moreover, the temporal consistency, measured through the reduction of the bitrate of encoded virtual views, was also considerably increased

Schätzung dichter Korrespondenzfelder unter Verwendung mehrerer Bilder

Most optical flow algorithms assume pairs of images that are acquired with an ideal, short exposure time. We present two approaches, that use additional images of a scene to estimate highly accurate, dense correspondence fields. In our first approach we consider video sequences that are acquired with alternating exposure times so that a short-exposure image is followed by a long-exposure image that exhibits motion-blur. With the help of the two enframing short-exposure images, we can decipher not only the motion information encoded in the long-exposure image, but also estimate occlusion timings, which are a basis for artifact-free frame interpolation. In our second approach we consider the data modality of multi-view video sequences, as it commonly occurs, e.g., in stereoscopic video. As several images capture nearly the same data of a scene, this redundancy can be used to establish more robust and consistent correspondence fields than the consideration of two images permits.Die meisten Verfahren zur Schätzung des optischen Flusses verwenden zwei Bilder, die mit einer optimalen, kurzen Belichtungszeit aufgenommen wurden. Wir präsentieren zwei Methoden, die zusätzliche Bilder zur Schätzung von hochgenauen, dichten Korrespondenzfeldern verwenden. Die erste Methode betrachtet Videosequenzen, die mit alternierender Belichtungsdauer aufgenommen werden, so dass auf eine Kurzzeitbelichtung eine Langzeitbelichtung folgt, die Bewegungsunschärfe enthält. Mit der Hilfe von zwei benachbarten Kurzzeitbelichtungen können wir nicht nur die Bewegung schätzen, die in der Bewegungsunschärfe der Langzeitbelichtung verschlüsselt ist, sondern zusätzlich auch Verdeckungszeiten schätzen, die sich bei der Interpolation von Zwischenbildern als große Hilfe erweisen. Die zweite Methode betrachtet Videos, die eine Szene aus mehreren Ansichten aufzeichnen, wie z.B. Stereovideos. Dabei enthalten mehrere Bilder fast dieselbe Information über die Szene. Wir nutzen diese Redundanz aus, um konsistentere und robustere Bewegungsfelder zu bestimmen, als es mit zwei Bildern möglich ist