Graphical Model MAP Inference with Continuous Label Space in Computer Vision

2018 ◽  
Author(s):  
Oliver Müller
Keyword(s):  
Graphical Model ◽  
Belief Propagation ◽  
Fundamental Problem ◽  
Parameter Tuning ◽  
3D Models ◽  
Target Object ◽  
State Variables ◽  
Slice Sampling ◽  
Global Consistency ◽  
Continuous State

This thesis deals with monocular object tracking from video sequences. The goal is to improve tracking of previously unseen non-rigid objects under severe articulations without relying on prior information such as detailed 3D models and without expensive offline training with manual annotations. The proposed framework tracks highly articulated objects by decomposing the target object into small parts and apply online tracking. Drift, which is a fundamental problem of online trackers, is reduced by incorporating image segmentation cues and by using a novel global consistency prior. Joint tracking and segmentation is formulated as a high-order probabilistic graphical model over continuous state variables. A novel inference method is proposed, called S-PBP, combining slice sampling and particle belief propagation. It is shown that slice sampling leads to fast convergence and does not rely on hyper-parameter tuning as opposed to competing approaches based on Metropolis-Hastings or heuristi...

scholarly journals Investigating the utility of VR for spatial understanding in surgical planning: evaluation of head-mounted to desktop display

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Georges Hattab ◽  
Adamantini Hatzipanayioti ◽  
Anna Klimova ◽  
Micha Pfeiffer ◽  
Peter Klausing ◽  
...  
Keyword(s):  
Surgical Planning ◽  
Spatial Information ◽  
Scene Understanding ◽  
3D Models ◽  
Target Object ◽  
Learning Condition ◽  
Estimation Task ◽  
Head Mounted Display ◽  
Direction Estimation ◽  
Spatial Understanding

AbstractRecent technological advances have made Virtual Reality (VR) attractive in both research and real world applications such as training, rehabilitation, and gaming. Although these other fields benefited from VR technology, it remains unclear whether VR contributes to better spatial understanding and training in the context of surgical planning. In this study, we evaluated the use of VR by comparing the recall of spatial information in two learning conditions: a head-mounted display (HMD) and a desktop screen (DT). Specifically, we explored (a) a scene understanding and then (b) a direction estimation task using two 3D models (i.e., a liver and a pyramid). In the scene understanding task, participants had to navigate the rendered the 3D models by means of rotation, zoom and transparency in order to substantially identify the spatial relationships among its internal objects. In the subsequent direction estimation task, participants had to point at a previously identified target object, i.e., internal sphere, on a materialized 3D-printed version of the model using a tracked pointing tool. Results showed that the learning condition (HMD or DT) did not influence participants’ memory and confidence ratings of the models. In contrast, the model type, that is, whether the model to be recalled was a liver or a pyramid significantly affected participants’ memory about the internal structure of the model. Furthermore, localizing the internal position of the target sphere was also unaffected by participants’ previous experience of the model via HMD or DT. Overall, results provide novel insights on the use of VR in a surgical planning scenario and have paramount implications in medical learning by shedding light on the mental model we make to recall spatial structures.

scholarly journals Image-Based Modeling from a High -Resolution Route Panorama

2010 ◽  
Vol 9 (1) ◽  
pp. 27-35
Author(s):  
Ryuji Shibata ◽  
Hajime Nagahara
Keyword(s):  
Belief Propagation ◽  
3D Models ◽  
Single Image ◽  
Multiple Viewpoints ◽  
Modeling Methods ◽  
Image Based Modeling ◽  
Spatio Temporal ◽  
Effective Use ◽  
Scene Depth ◽  
Route Panorama

Image-based modeling methods for generating 3D models from an image sequence have been widely studied. Most of these methods, however, require huge redundant spatio-temporal images to estimate scene depth. This is not an effective use of capturing higher resolution texture. On the other hand, a route panorama, which is a continuous panoramic image along a path, is an efficient way of consolidating information from multiple viewpoints into a single image. A route panorama captured by a line camera also has the advantage of capturing higher resolution easily. In this paper, we propose a method for estimating the depth of an image from a route panorama using color drifts. The proposed method detects color drift by deformable window matching of the color channels. It also uses a hierarchical belief propagation to estimate the depth stably and decrease the computation cost thereof.

scholarly journals Distributed Weighted Least-Squares and Gaussian Belief Propagation: An Integrated Approach

2021 ◽  
Author(s):  
Dino Zivojevic ◽  
Muhamed Delalic ◽  
Darijo Raca ◽  
Dejan Vukobratovic ◽  
Mirsad Cosovic
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Belief Propagation ◽  
Weighted Least Squares ◽  
Measurement Data ◽  
Integrated Approach ◽  
Factor Graph ◽  
State Variables ◽  
Large Scale Systems ◽  
Distributed Approach

The purpose of a state estimation (SE) algorithm is to estimate the values of the state variables considering the available set of measurements. The centralised SE becomes impractical for large-scale systems, particularly if the measurements are spatially distributed across wide geographical areas. Dividing the large-scale systems into clusters (\ie subsystems) and distributing the computation across clusters, solves the constraints of centralised based approach. In such scenarios, using distributed SE methods brings numerous advantages over the centralised ones. In this paper, we propose a novel distributed approach to solve the linear SE model by combining local solutions obtained by applying weighted least-squares (WLS) of the given subsystems with the Gaussian belief propagation (GBP) algorithm. The proposed algorithm is based on the factor graph operating without a central coordinator, where subsystems exchange only ``beliefs", thus preserving privacy of the measurement data and state variables. Further, we propose an approach to speed-up evaluation of the local solution upon arrival of a new information to the subsystem. Finally, the proposed algorithm provides results that reach accuracy of the centralised WLS solution in a few iterations, and outperforms vanilla GBP algorithm with respect to its convergence properties.

Discrete-Time Mapping for an Impulsive Goodwin Oscillator with Three Delays

2017 ◽  
Vol 27 (12) ◽  
pp. 1750182 ◽  
Author(s):  
Alexander N. Churilov ◽  
Alexander Medvedev ◽  
Zhanybai T. Zhusubaliyev
Keyword(s):  
Time Delays ◽  
Hormone Secretion ◽  
Nonlinear Phenomena ◽  
Multiple Time ◽  
State Variables ◽  
Nonlinear Feedback ◽  
Pulse Modulation ◽  
Continuous State ◽  
Pulsatile Hormone Secretion ◽  
Goodwin Oscillator

A popular biomathematics model of the Goodwin oscillator has been previously generalized to a more biologically plausible construct by introducing three time delays to portray the transport phenomena arising due to the spatial distribution of the model states. The present paper addresses a similar conversion of an impulsive version of the Goodwin oscillator that has found application in mathematical modeling, e.g. in endocrine systems with pulsatile hormone secretion. While the cascade structure of the linear continuous part pertinent to the Goodwin oscillator is preserved in the impulsive Goodwin oscillator, the static nonlinear feedback of the former is substituted with a pulse modulation mechanism thus resulting in hybrid dynamics of the closed-loop system. To facilitate the analysis of the mathematical model under investigation, a discrete mapping propagating the continuous state variables through the firing times of the impulsive feedback is derived. Due to the presence of multiple time delays in the considered model, previously developed mapping derivation approaches are not applicable here and a novel technique is proposed and applied. The mapping captures the dynamics of the original hybrid system and is instrumental in studying complex nonlinear phenomena arising in the impulsive Goodwin oscillator. A simulation example is presented to demonstrate the utility of the proposed approach in bifurcation analysis.

scholarly journals Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

2019 ◽  
Vol 11 (13) ◽  
pp. 1550 ◽  
Author(s):  
Tobias Koch ◽  
Marco Körner ◽  
Friedrich Fraundorfer
Keyword(s):  
Path Planning ◽  
3D Reconstruction ◽  
Planning Process ◽  
Expert Knowledge ◽  
3D Models ◽  
Target Object ◽  
Planning Problem ◽  
Hazardous Environments ◽  
Planning Framework ◽  
Reconstruction Quality

Small-scaled unmanned aerial vehicles (UAVs) emerge as ideal image acquisition platforms due to their high maneuverability even in complex and tightly built environments. The acquired images can be utilized to generate high-quality 3D models using current multi-view stereo approaches. However, the quality of the resulting 3D model highly depends on the preceding flight plan which still requires human expert knowledge, especially in complex urban and hazardous environments. In terms of safe flight plans, practical considerations often define prohibited and restricted airspaces to be accessed with the vehicle. We propose a 3D UAV path planning framework designed for detailed and complete small-scaled 3D reconstructions considering the semantic properties of the environment allowing for user-specified restrictions on the airspace. The generated trajectories account for the desired model resolution and the demands on a successful photogrammetric reconstruction. We exploit semantics from an initial flight to extract the target object and to define restricted and prohibited airspaces which have to be avoided during the path planning process to ensure a safe and short UAV path, while still aiming to maximize the object reconstruction quality. The path planning problem is formulated as an orienteering problem and solved via discrete optimization exploiting submodularity and photogrammetrical relevant heuristics. An evaluation of our method on a customized synthetic scene and on outdoor experiments suggests the real-world capability of our methodology by providing feasible, short and safe flight plans for the generation of detailed 3D reconstruction models.

scholarly journals A New Framework For Interactive Segmentation of Point Clouds

2014 ◽  
Vol XL-5 ◽  
pp. 357-362 ◽  
Author(s):  
K. Liu ◽  
J. Boehm
Keyword(s):  
Point Cloud ◽  
Fundamental Problem ◽  
Weighted Graph ◽  
Point Clouds ◽  
Target Object ◽  
General Point ◽  
Interactive Segmentation ◽  
Mobile Mapping ◽  
Point Cloud Segmentation ◽  
New Framework

Point cloud segmentation is a fundamental problem in point processing. Segmenting a point cloud fully automatically is very challenging due to the property of point cloud as well as different requirements of distinct users. In this paper, an interactive segmentation method for point clouds is proposed. Only two strokes need to be drawn intuitively to indicate the target object and the background respectively. The draw strokes are sparse and don't necessarily cover the whole object. Given the strokes, a weighted graph is built and the segmentation is formulated as a minimization problem. The problem is solved efficiently by using the Max Flow Min Cut algorithm. In the experiments, the mobile mapping data of a city area is utilized. The resulting segmentations demonstrate the efficiency of the method that can be potentially applied for general point clouds.

scholarly journals Planar Prior Assisted PatchMatch Multi-View Stereo

2020 ◽  
Vol 34 (07) ◽  
pp. 12516-12523
Author(s):  
Qingshan Xu ◽  
Wenbing Tao
Keyword(s):  
Graphical Model ◽  
State Of The Art ◽  
Depth Estimation ◽  
3D Models ◽  
The Other ◽  
Depth Information ◽  
Probabilistic Graphical Model ◽  
Challenging Problem ◽  
Matching Cost ◽  
Planar Regions

The completeness of 3D models is still a challenging problem in multi-view stereo (MVS) due to the unreliable photometric consistency in low-textured areas. Since low-textured areas usually exhibit strong planarity, planar models are advantageous to the depth estimation of low-textured areas. On the other hand, PatchMatch multi-view stereo is very efficient for its sampling and propagation scheme. By taking advantage of planar models and PatchMatch multi-view stereo, we propose a planar prior assisted PatchMatch multi-view stereo framework in this paper. In detail, we utilize a probabilistic graphical model to embed planar models into PatchMatch multi-view stereo and contribute a novel multi-view aggregated matching cost. This novel cost takes both photometric consistency and planar compatibility into consideration, making it suited for the depth estimation of both non-planar and planar regions. Experimental results demonstrate that our method can efficiently recover the depth information of extremely low-textured areas, thus obtaining high complete 3D models and achieving state-of-the-art performance.

Disappointment Aversion, Term Structure, and Predictability Puzzles in Bond Markets

2020 ◽  
Author(s):  
Patrick Augustin ◽  
Roméo Tédongap
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Dynamic Equilibrium ◽  
Time Varying ◽  
State Variables ◽  
Expectations Hypothesis ◽  
Macroeconomic Uncertainty ◽  
Disappointment Aversion ◽  
Nominal Interest Rates ◽  
Continuous State

We solve a dynamic equilibrium model with generalized disappointment-aversion preferences and continuous state-endowment dynamics. We apply the framework to the term structure of interest rates and show that the model generates an upward-sloping term structure of nominal interest rates and a downward-sloping term structure of real interest rates and that it accounts for the failure of the expectations hypothesis. The key ingredients are preferences with disappointment aversion, preference for early resolution of uncertainty, and an endowment economy with three state variables: time-varying macroeconomic uncertainty, time-varying expected inflation, and inflation uncertainty. This paper was accepted by Karl Diether, finance.

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