Quantum Multi-Model Fitting

Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single model, while leaving the question of multi-model fitting open. In response to this challenge, this paper shows that the latter case can significantly benefit from quantum hardware and proposes the first quantum approach to multi-model fitting (MMF). We formulate MMF as a problem that can be efficiently sampled by modern adiabatic quantum computers without the relaxation of the objective function. We also propose an iterative and decomposed version of our method, which supports real-world-sized problems. The experimental evaluation demonstrates promising results on a variety of datasets. The source code is available at: https://github.com/FarinaMatteo/qmmf.Comment: In Computer Vision and Pattern Recognition (CVPR) 2023; Highligh

Multiple structure recovery with maximum coverage

We present a general framework for geometric model fitting based on a set coverage formulation that caters for intersecting structures and outliers in a simple and principled manner. The multi-model fitting problem is formulated in terms of the optimization of a consensus-based global cost function, which allows to sidestep the pitfalls of preference approaches based on clustering and to avoid the difficult trade-off between data fidelity and complexity of other optimization formulations. Two especially appealing characteristics of this method are the ease with which it can be implemented and its modularity with respect to the solver and to the sampling strategy. Few intelligible parameters need to be set and tuned, namely the inlier threshold and the number of desired models. The summary of the experiments is that our method compares favourably with its competitors overall, and it is always either the best performer or almost on par with the best performer in specific scenarios

Multiple structure recovery with T-linkage

reserved2noThis work addresses the problem of robust fitting of geometric structures to noisy data corrupted by outliers. An extension of J-linkage (called T-linkage) is presented and elaborated. T-linkage improves the preference analysis implemented by J-linkage in term of performances and robustness, considering both the representation and the segmentation steps. A strategy to reject outliers and to estimate the inlier threshold is proposed, resulting in a versatile tool, suitable for multi-model fitting “in the wild”. Experiments demonstrate that our methods perform better than J-linkage on simulated data, and compare favorably with state-of-the-art methods on public domain real datasets.mixedMagri L.; Fusiello A.Magri, L.; Fusiello, A

Determination and Comparison of In Vivo Forces and Torques in Normal and Degenerative Lumbar Spines

In vivo motions of normal and degenerative lumbar spine patients performing extension/flexion were obtained using video fluoroscopy. 3-D models of each patient’s vertebrae were registered to the 2-D fluoroscopy images using a process developed at Rocky Mountain Musculoskeletal Research Laboratory. Temporal equations representing the motions were input into a math model and the forces at the contact point between vertebral levels and the body torques between the vertebrae were the output. The vertical forces in the normal and degenerative patients were similar and ranged from 0.35-0.42 times the body weight of the patient. The maximum torques were higher in the degenerative patient than in the normal patient. The maximum torques between L4 and L5 were 11.1 N*m in the degenerative patient and 9.72 N*m in the normal patient. At L3/L4, the maximum torque was 10.3 N*m in the degenerative and 9.03 N*m in the normal patient. The maximum torques in the degenerative patient were also higher than in the normal patient at the L2/L3 and L1/L2 levels. Left untreated these higher torques could cause deterioration of other levels as the spine tries to compensate for existing degenerative levels. This model will lead to a better understanding of the lumbar spine and could aid in treating lower back pain and in the design of spinal prostheses

Multiple structure recovery via robust preference analysis

2noThis paper address the extraction of multiple models from outlier-contaminated data by exploiting preference analysis and low rank approximation. First points are represented in the preference space, then Robust PCA (Principal Component Analysis) and Symmetric NMF (Non negative Matrix Factorization) are used to break the multi-model fitting problem into many single-model problems, which in turn are tackled with an approach inspired to MSAC (M-estimator SAmple Consensus) coupled with a model-specific scale estimate. Experimental validation on public, real data-sets demonstrates that our method compares favorably with the state of the art.openopenMagri, Luca; Fusiello, AndreaMagri, Luca; Fusiello, Andre

Active and Physics-Based Human Pose Reconstruction

Perceiving humans is an important and complex problem within computervision. Its significance is derived from its numerous applications, suchas human-robot interaction, virtual reality, markerless motion capture,and human tracking for autonomous driving. The difficulty lies in thevariability in human appearance, physique, and plausible body poses. Inreal-world scenes, this is further exacerbated by difficult lightingconditions, partial occlusions, and the depth ambiguity stemming fromthe loss of information during the 3d to 2d projection. Despite thesechallenges, significant progress has been made in recent years,primarily due to the expressive power of deep neural networks trained onlarge datasets. However, creating large-scale datasets with 3dannotations is expensive, and capturing the vast diversity of the realworld is demanding. Traditionally, 3d ground truth is captured usingmotion capture laboratories that require large investments. Furthermore,many laboratories cannot easily accommodate athletic and dynamicmotions. This thesis studies three approaches to improving visualperception, with emphasis on human pose estimation, that can complementimprovements to the underlying predictor or training data.The first two papers present active human pose estimation, where areinforcement learning agent is tasked with selecting informativeviewpoints to reconstruct subjects efficiently. The papers discard thecommon assumption that the input is given and instead allow the agent tomove to observe subjects from desirable viewpoints, e.g., those whichavoid occlusions and for which the underlying pose estimator has a lowprediction error.The third paper introduces the task of embodied visual active learning,which goes further and assumes that the perceptual model is notpre-trained. Instead, the agent is tasked with exploring its environmentand requesting annotations to refine its visual model. Learning toexplore novel scenarios and efficiently request annotation for new datais a step towards life-long learning, where models can evolve beyondwhat they learned during the initial training phase. We study theproblem for segmentation, though the idea is applicable to otherperception tasks.Lastly, the final two papers propose improving human pose estimation byintegrating physical constraints. These regularize the reconstructedmotions to be physically plausible and serve as a complement to currentkinematic approaches. Whether a motion has been observed in the trainingdata or not, the predictions should obey the laws of physics. Throughintegration with a physical simulator, we demonstrate that we can reducereconstruction artifacts and enforce, e.g., contact constraints

Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj