ALFO: Adaptive light field over-segmentation

Automatic image over-segmentation into superpixels has attracted increasing attention from researchers to apply it as a pre-processing step for several computer vision applications. In 4D Light Field (LF) imaging, image over-segmentation aims at achieving not only superpixel compactness and accuracy but also cross-view consistency. Due to the high dimensionality of 4D LF images, depth information can be estimated and exploited during the over-segmentation along with spatial and visual appearance features. However, balancing between several hybrid features to generate robust superpixels for different 4D LF images is challenging and not adequately solved in existing solutions. In this paper, an automatic, adaptive, and view-consistent LF over-segmentation method based on normalized LF cues and K-means clustering is proposed. Initially, disparity maps for all LF views are estimated entirely to improve superpixel accuracy and consistency. Afterwards, by using K-means clustering, a 4D LF image is iteratively divided into regular superpixels that adhere to object boundaries and ensure cross-view consistency. Our proposed method can automatically adjust the clustering weights of the various features that characterize each superpixel based on the image content. Quantitative and qualitative results on several 4D LF datasets demonstrate outperforming performance of the proposed method in terms of superpixel accuracy, shape regularity and view consistency when using adaptive clustering weights, compared to the state-of-the-art 4D LF over-segmentation methods.info:eu-repo/semantics/publishedVersio

Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features

SLFS: Semi-supervised light-field foreground-background segmentation

Efficient segmentation is a fundamental problem in computer vision and image processing. Achieving accurate segmentation for 4D light field images is a challenging task due to the huge amount of data involved and the intrinsic redundancy in this type of images. While automatic image segmentation is usually challenging, and because regions of interest are different for different users or tasks, this paper proposes an improved semi-supervised segmentation approach for 4D light field images based on an efficient graph structure and user's scribbles. The recent view-consistent 4D light field superpixels algorithm proposed by Khan et al. is used as an automatic pre-processing step to ensure spatio-angular consistency and to represent the image graph efficiently. Then, segmentation is achieved via graph-cut optimization. Experimental results for synthetic and real light field images indicate that the proposed approach can extract objects consistently across views, and thus it can be used in applications such as augmented reality applications or object-based coding with few user interactions.info:eu-repo/semantics/acceptedVersio

Hyperpixels: Flexible 4D over-segmentation for dense and sparse light fields

4D Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks and generate immersive experiences for end-users. A key challenge in 4D LF imaging is to flexibly and adaptively represent the included spatio-angular information to facilitate subsequent computer vision applications. Recently, image over-segmentation into homogenous regions with perceptually meaningful information has been exploited to represent 4D LFs. However, existing methods assume densely sampled LFs and do not adequately deal with sparse LFs with large occlusions. Furthermore, the spatio-angular LF cues are not fully exploited in the existing methods. In this paper, the concept of hyperpixels is defined and a flexible, automatic, and adaptive representation for both dense and sparse 4D LFs is proposed. Initially, disparity maps are estimated for all views to enhance over-segmentation accuracy and consistency. Afterwards, a modified weighted K-means clustering using robust spatio-angular features is performed in 4D Euclidean space. Experimental results on several dense and sparse 4D LF datasets show competitive and outperforming performance in terms of over-segmentation accuracy, shape regularity and view consistency against state-of-the-art methods.info:eu-repo/semantics/publishedVersio

View-consistent 4D Light Field style transfer using neural networks and over-segmentation

Deep learning has shown promising results in several computer vision applications, such as style transfer applications. Style transfer aims at generating a new image by combining the content of one image with the style and color palette of another image. When applying style transfer to a 4D Light Field (LF) that represents the same scene from different angular perspectives, new challenges and requirements are involved. While the visually appealing quality of the stylized image is an important criterion in 2D images, cross-view consistency is essential in 4D LFs. Moreover, the need for large datasets to train new robust models arises as another challenge due to the limited LF datasets that are currently available. In this paper, a neural style transfer approach is used, along with a robust propagation based on over-segmentation, to stylize 4D LFs. Experimental results show that the proposed solution outperforms the state-of-the-art without any need for training or fine-tuning existing ones while maintaining consistency across LF views.info:eu-repo/semantics/acceptedVersio

Non-disruptive use of light fields in image and video processing

In the age of computational imaging, cameras capture not only an image but also data. This captured additional data can be best used for photo-realistic renderings facilitating numerous post-processing possibilities such as perspective shift, depth scaling, digital refocus, 3D reconstruction, and much more. In computational photography, the light field imaging technology captures the complete volumetric information of a scene. This technology has the highest potential to accelerate immersive experiences towards close-toreality. It has gained significance in both commercial and research domains. However, due to lack of coding and storage formats and also the incompatibility of the tools to process and enable the data, light fields are not exploited to its full potential. This dissertation approaches the integration of light field data to image and video processing. Towards this goal, the representation of light fields using advanced file formats designed for 2D image assemblies to facilitate asset re-usability and interoperability between applications and devices is addressed. The novel 5D light field acquisition and the on-going research on coding frameworks are presented. Multiple techniques for optimised sequencing of light field data are also proposed. As light fields contain complete 3D information of a scene, large amounts of data is captured and is highly redundant in nature. Hence, by pre-processing the data using the proposed approaches, excellent coding performance can be achieved.Im Zeitalter der computergestützten Bildgebung erfassen Kameras nicht mehr nur ein Bild, sondern vielmehr auch Daten. Diese erfassten Zusatzdaten lassen sich optimal für fotorealistische Renderings nutzen und erlauben zahlreiche Nachbearbeitungsmöglichkeiten, wie Perspektivwechsel, Tiefenskalierung, digitale Nachfokussierung, 3D-Rekonstruktion und vieles mehr. In der computergestützten Fotografie erfasst die Lichtfeld-Abbildungstechnologie die vollständige volumetrische Information einer Szene. Diese Technologie bietet dabei das größte Potenzial, immersive Erlebnisse zu mehr Realitätsnähe zu beschleunigen. Deshalb gewinnt sie sowohl im kommerziellen Sektor als auch im Forschungsbereich zunehmend an Bedeutung. Aufgrund fehlender Kompressions- und Speicherformate sowie der Inkompatibilität derWerkzeuge zur Verarbeitung und Freigabe der Daten, wird das Potenzial der Lichtfelder nicht voll ausgeschöpft. Diese Dissertation ermöglicht die Integration von Lichtfelddaten in die Bild- und Videoverarbeitung. Hierzu wird die Darstellung von Lichtfeldern mit Hilfe von fortschrittlichen für 2D-Bilder entwickelten Dateiformaten erarbeitet, um die Wiederverwendbarkeit von Assets- Dateien und die Kompatibilität zwischen Anwendungen und Geräten zu erleichtern. Die neuartige 5D-Lichtfeldaufnahme und die aktuelle Forschung an Kompressions-Rahmenbedingungen werden vorgestellt. Es werden zudem verschiedene Techniken für eine optimierte Sequenzierung von Lichtfelddaten vorgeschlagen. Da Lichtfelder die vollständige 3D-Information einer Szene beinhalten, wird eine große Menge an Daten, die in hohem Maße redundant sind, erfasst. Die hier vorgeschlagenen Ansätze zur Datenvorverarbeitung erreichen dabei eine ausgezeichnete Komprimierleistung

Fast and Accurate Depth Estimation from Sparse Light Fields

We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping compact superpixels that are used as basic data units for depth estimation and refinement. Superpixel representation provides a desirable reduction in the computational cost while preserving the image geometry with respect to the object contours. Each superpixel is modeled as a plane in the image space, allowing depth values to vary smoothly within the superpixel area. Initial depth maps, which are obtained by plane sweeping, are iteratively refined by propagating good correspondences within an image. To ensure the fast convergence of the iterative optimization process, we employ a highly parallel propagation scheme that operates on all the superpixels of all the images at once, making full use of the parallel graphics hardware. A few optimization iterations of the energy function incorporating superpixel-wise smoothness and geometric consistency constraints allows to recover depth with high accuracy in textured and textureless regions as well as areas with occlusions, producing dense globally consistent depth maps. We demonstrate that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results.Comment: 15 pages, 15 figure