Efficient Motion Field Interpolation Method for Wyner-Ziv Video Coding

Wyner-Ziv video coding has the capability to reduce video encoding complexity by shifting motion estimation procedure from encoder to decoder. Amongst many motion estimation methods, expectation maximization algorithm is the most effective one. Unfortunately, the implementation of block-based motion estimation in this algorithm causes motion field profile bounded by granularity of block size. Nearest-neighbor and bilinear interpolation methods have already applied in multiview image coding to handle similar problem. This paper aims to evaluate performance of both interpolation methods in transform-domain Wyner-Ziv video codec. Results showed that bilinear interpolation effective only for high motion video sequences. In this scenario, it has bitrate saving up to 3.29 %, 0.2 dB higher PSNR, and 12.30 % higher decoding complexity compared to nearest-neighbor. In low motion video content, bitrate saving only gained up to 0.82%, with almost the same PSNR, while decoding complexity increase up to 10.32%. 

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities. Our models are fully interpretable as well as parameter and data efficient. Our experiments demonstrate their effectiveness on a large diversity of tasks ranging from image denoising and compressed sensing for fMRI to dense stereo matching.Comment: NeurIPS 202

Advancements in multi-view processing for reconstruction, registration and visualization.

The ever-increasing diffusion of digital cameras and the advancements in computer vision, image processing and storage capabilities have lead, in the latest years, to the wide diffusion of digital image collections. A set of digital images is usually referred as a multi-view images set when the pictures cover different views of the same physical object or location. In multi-view datasets, correlations between images are exploited in many different ways to increase our capability to gather enhanced understanding and information on a scene. For example, a collection can be enhanced leveraging on the camera position and orientation, or with information about the 3D structure of the scene. The range of applications of multi-view data is really wide, encompassing diverse fields such as image-based reconstruction, image-based localization, navigation of virtual environments, collective photographic retouching, computational photography, object recognition, etc. For all these reasons, the development of new algorithms to effectively create, process, and visualize this type of data is an active research trend. The thesis will present four different advancements related to different aspects of the multi-view data processing: - Image-based 3D reconstruction: we present a pre-processing algorithm, that is a special color-to-gray conversion. This was developed with the aim to improve the accuracy of image-based reconstruction algorithms. In particular, we show how different dense stereo matching results can be enhanced by application of a domain separation approach that pre-computes a single optimized numerical value for each image location. - Image-based appearance reconstruction: we present a multi-view processing algorithm, this can enhance the quality of the color transfer from multi-view images to a geo-referenced 3D model of a location of interest. The proposed approach computes virtual shadows and allows to automatically segment shadowed regions from the input images preventing to use those pixels in subsequent texture synthesis. - 2D to 3D registration: we present an unsupervised localization and registration system. This system can recognize a site that has been framed in a multi-view data and calibrate it on a pre-existing 3D representation. The system has a very high accuracy and it can validate the result in a completely unsupervised manner. The system accuracy is enough to seamlessly view input images correctly super-imposed on the 3D location of interest. - Visualization: we present PhotoCloud, a real-time client-server system for interactive exploration of high resolution 3D models and up to several thousand photographs aligned over this 3D data. PhotoCloud supports any 3D models that can be rendered in a depth-coherent way and arbitrary multi-view image collections. Moreover, it tolerates 2D-to-2D and 2D-to-3D misalignments, and it provides scalable visualization of generic integrated 2D and 3D datasets by exploiting data duality. A set of effective 3D navigation controls, tightly integrated with innovative thumbnail bars, enhances the user navigation. These advancements have been developed in tourism and cultural heritage application contexts, but they are not limited to these

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

International audienceWe introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities. Our models are fully interpretable as well as parameter and data efficient. Our experiments demonstrate their effectiveness on a large diversity of tasks ranging from image denoising and compressed sensing for fMRI to dense stereo matching

Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016

Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

OpenPTrack: Open Source Multi-Camera Calibration and People Tracking for RGB-D Camera Networks

OpenPTrack is an open source software for multi-camera calibration and people tracking in RGB-D camera networks. It allows to track people in big volumes at sensor frame rate and currently supports a heterogeneous set of 3D sensors. In this work, we describe its user-friendly calibration procedure, which consists of simple steps with real-time feedback that allow to obtain accurate results in estimating the camera poses that are then used for tracking people. On top of a calibration based on moving a checkerboard within the tracking space and on a global optimization of cameras and checkerboards poses, a novel procedure which aligns people detections coming from all sensors in a x-y-time space is used for refining camera poses. While people detection is executed locally, in the machines connected to each sensor, tracking is performed by a single node which takes into account detections from all over the network. Here we detail how a cascade of algorithms working on depth point clouds and color, infrared and disparity images is used to perform people detection from different types of sensors and in any indoor light condition. We present experiments showing that a considerable improvement can be obtained with the proposed calibration refinement procedure that exploits people detections and we compare Kinect v1, Kinect v2 and Mesa SR4500 performance for people tracking applications. OpenPTrack is based on the Robot Operating System and the Point Cloud Library and has already been adopted in networks composed of up to ten imagers for interactive arts, education, culture and human\u2013robot interaction applications