3D object tracking via boundary constrained region-based model

vertebral fracture is a very common outcome of osteoporosis, which is one of the major public health concerns in the world. Early detection of vertebral fractures is important because timely pharmacologic intervention can reduce the risk of subsequent additional fractures. Our goal seeks to develop a computerized method for detection of vertebral fractures by measuring the shape and appearance of vertebrae on cervical xray radiographs in order to assist radiologist’s image interpretation and thus allow the early diagnosis of osteoporosis. The statistical models of shape and appearance are powerful tools for interpreting medical images. This work introduces the application of correlation filter classifiers for identification and verification of the osteoporosis presence in cervical vertebrae training/ testing set. Correlation filter classifiers have been previously applied to other biometric classification tasks, but not to classification of cervical vertebrae images. We describe how the extraction of an appropriate region of interest in the cervical vertebrae surface can be used to design correlation filters that accomplish 90 % recognition on a database of 50 cervical bone shapes.Keywordsx-ray radiographsSegmentationASM modelingCorrelation filtervertebral deformityOsteoporosis

Learning to see stuff

We formulate a probabilistic framework for simultaneous region-based 2D segmentation and 2D to 3D pose tracking, using a known 3D model. Given such a model, we aim to maximise the discrimination between statistical foreground and background appearance models, via direct optimisation of the 3D pose parameters. The foreground region is delineated by the zero-level-set of a signed distance embedding function, and we define an energy over this region and its immediate background surroundings based on pixel-wise posterior membership probabilities (as opposed to likelihoods). We derive the differentials of this energy with respect to the pose parameters of the 3D object, meaning we can conduct a search for the correct pose using standard gradient-based non-linear minimisation techniques. We propose novel enhancements at the pixel level based on temporal consistency and improved online appearance model adaptation. Furthermore, straightforward extensions of our method lead to multi-camera and multi-object tracking as part of the same framework. The parallel nature of much of the processing in our algorithm means it is amenable to GPU acceleration, and we give details of our real-time implementation, which we use to generate experimental results on both real and artificial video sequences, with a number of 3D models. These experiments demonstrate the benefit of using pixel-wise posteriors rather than likelihoods, and showcase the qualities, such as robustness to occlusions and motion blur (and also some failure modes), of our tracker.

The joint effect of mesoscale and microscale roughness on perceived gloss

We present experiments to illustrate tracking of occluded objects using three-dimensional (3D) integral imaging (II). Tracking of heavily occluded objects by conventional two-dimensional (2D) image processing may be difficult owing to the superposition of occlusion noise and object details. The effects of occlusion are remedied by 3D computational II reconstruction. We use a summation of absolute difference (SAD) algorithm between pixels of consecutive frames of a moving object for 3D tracking. SAD algorithm is not robust for 2D images of occluded objects; 3D computation reconstruction of the scene allows implementation of a SAD algorithm by reducing occlusion effects. Experimental results demonstrate 3D tracking of occluded objects. To the best of our knowledge, this is the first report on 3D tracking of objects using II.

Heterogeneous intensity distribution inside the prostate gland, significant variations in prostate shape, size, inter dataset contrast variations, and imaging artifacts like shadow regions and speckle in Trans Rectal Ultrasound (TRUS) images challenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a supervised learning schema based on random forest for automatic initialization and propagation of statistical shape and appearance model. Parametric representation of the statistical model of shape and appearance is derived from principal component analysis (PCA) of the probability distribution inside the prostate and PCA of the contour landmarks obtained from the training images. Unlike traditional statistical models of shape and intensity priors, the appearance model in this paper is derived from the posterior probabilities obtained from random forest classification. This probabilistic information is then used for the initialization and propagation of the statistical model. The proposed method achieves mean Dice Similarity Coefficient (DSC) value of 0.96±0.01, with a mean segmentation time of 0.67±0.02 seconds when validated with 24 images from 6 datasets with considerable shape, size, and intensity variations, in a leave-one-patient-out validation framework. The model achieves statistically significant t-test p-value<0.0001 in mean DSC and mean mean absolute distance (MAD) values compared to traditional statistical models of shape and intensity priors.

3D tracking of objects and hands in an object manipulation scenario is a very interesting computer vision problem with a wide variety of applications ranging from consumer electronics to robotics and medicine. Recent advances in this research topic allow for 3D tracking of complex scenarios involving bi-manual manipulation of several rigid objects using commodity hardware and with high accuracy. The problem with these approaches is that they treat tracking as a search problem whose dimensionality increases with the number of objects in the scene. This fact typically limits the number of the tracked objects and/or the processing framerate. In this paper we present a method that utilizes simple low level motion cues for dynamically assigning computational resources to parts of the scene where they are actually required. In a series of experiments, we show that this simple idea improves tracking performance dramatically at a cost of only a minor degradation of tracking accuracy. The works that are most related to ours are the approaches by Kyriazis and Argyros on top-down 3D tracking of multiple active objects from RGBD input [1, 2]. The methodological part of our contribution can be briefly described as an extra processing node in the pipeline of [2] which it extends. The tracking approach in [2], the Ensemble of Collaborative Trackers (ECT), regards a set of semi-independent trackers. Each tracker is associated with a distinct object in the scene. For an object to be tracked, a separate optimization problem is solved, one for each frame and each object. Each optimization problem is numerically solved using a black box optimizer, i.e. a variant of the Particle Swarm Optimization (PSO) algorithm. PSO treats the objective function as an oracle and queries it on purposefully evolved “guesses” in the search space, in order to find the optimum. The harder the problem is, the more guesses (budget, which is the product of PSO particles and PSO generations that need to be computed) are required for adequate accuracy to be achieved. For example, tracking the pose and the articulation of the hand amounts to solving a 27-parameter optimization problem. This is much harder than solving for the 3D pose of a rigid object (6 parameters). ECT [2] assigns a fixed amount of computational resources to each tracking sub-problem which depends on this notion of complexity and which is empirically estimated. In this work, we quantify at run time, how hard the tracking of an object should be, not only based on its intrinsic complexity, but also based on its observed dynamics. An object that appears static in the recent temporal window requires less resources compared to an object whose state is more dynamic. Thus, change detection is performed on the image space of color intensities and depth measurements of the RGBD input. In more detail, for the next tracking frame, and the ith tracker, a value mi is computed, which takes the value of 0 if the corresponding tracked object appears to be relatively static, and takes the value of 1 otherwise. For mi to be 1, any of the following need to be true: • the mean value of the pixel-wise differences between the observations of object i and the back-projection of the last estimated configuration of object i is high enough, • the kinetic energy of object i, as computed from so far tracked velocities, suggests a moving object, • the amount of missing depth measurements changes substantially, from one frame to the next, which might me attributed to change in the slant of object i, or • any of the above was satisfied in the recent past (damping). Budget has the trivial minimum value of 0. Moreover, as it has been shown in [3, 4], a PSO budget of 64 particles and 64 generations suffices to track a hand, even in interaction with another hand. No more budget should be required to track simpler structures such as rigid objects. The proposed dynamic budget allocation policy assigns a minimum budget Bmin of 64 particles running for 4 generations to the objects that are static and a maximum budget Bmax that never exceeds the aforementioned (a) Pouring pancake mix (b) Waiting for the mix to cook

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

Active Appearance Models (AAMs) and Constrained Local Models (CLMs) are classical approaches for model-based image segmentation in medical image analysis. AAMs consist of global statistical models of shape and appearance, are known to be hard to fit, and often suffer from insufficient generalization capabilities in case of limited training data. CLMs model appearance only for local patches and relax or completely remove the global appearance constraint. They are, therefore, much easier to optimize but in certain cases they lack the robustness of AAMs. In this paper, we present a framework for patch-based active appearance modeling, which elegantly combines strengths of AAMs and CLMs. Our models provide global shape and appearance constraints and we make use of recent methodological advances from computer vision for efficient joint optimization of shape and appearance parameters during model fitting. Furthermore, the insufficient generalization abilities of those global models are tackled by incorporating and extending a recent approach for learning representative statistical shape models from small training populations. We evaluate our approach on publicly available chest radiographs and cardiac MRI data. The results show that the proposed framework leads to competitive results in terms of segmentation accuracy for challenging multi-object segmentation problems even when only few training samples are available.

The Active Appearance Model (AAM) algorithm has proved to be a successful method for matching statistical models of appearance to new images. Since the original algorithm was described there have been a variety of suggested modifications to the basic algorithm, each typically claiming to be in some way superior. We review these algorithms and report the results of experiments comparing their performance. We also investigate the effects of different methods of estimating the update matrix used in the algorithm. We find that careful choice of the latter has at least as much effect as the choice of updating technique.

This paper is concerned with the detection of moving objects using a pan-tilt camera and a background subtraction algorithm. Traditionally, motion compensation is performed on the current image to align its pixels with their background models in previous frames. Pixel misalignment however can occur during motion compensation. Although this problem can be alleviated by using pixel motion such as the optic flow, motion information itself can be inaccurate and, together with pixel misalignment, contributes to false positive foreground detection. In this paper, we exploit the fact that pixel misalignment and inaccurate optic flow tend not to occur simultaneously for a pixel. Consequently, we can substantially improve the performance of the background subtraction algorithm by evaluating the marginal statistical models of appearance and motion separately — rather than jointly — in classifying whether a pixel is foreground. We will use experiments to validate our approach and establish its superiority to other competing algorithms in the literature.

This paper examines the process real-world SMT manufacturers are employing to evaluate, select and implement automated optical inspection (AOI) systems for post-placement/pre-reflow inspection during the PCB assembly process. A case study from a prominent European SMT manufacturer describes the process used to evaluate a post-placement/pre-reflow AOI system. Traditional, algorithm-based AOI techniques are briefly described and evaluated, along with their strengths and limitations. A new AOI methodology, called statistical appearance modeling, and a stereo-vision inspection machine are briefly described and compared to traditional AOI systems. Examples of payback and ROI calculations and analysis are highlighted. The system requirements are specified and the actual results of the AOI performance trial are summarized. Other relevant topics include analyzing GR&amp;R, interpreting SPC data, and implementing post/placement/pre-reflow AOI.

This paper addresses the three-dimensional (3-D) tracking of pose and animation of the human face in monocular image sequences using active appearance models. The major problem of the classical appearance-based adaptation is the high computational time resulting from the inclusion of a synthesis step in the iterative optimization. Whenever the dimension of the face space is large, a real-time performance cannot be achieved. In this paper, we aim at designing a fast and stable active appearance model search for 3-D face tracking. The main contribution is a search algorithm whose CPU-time is not dependent on the dimension of the face space. Using this algorithm, we show that both the CPU-time and the likelihood of a nonaccurate tracking are reduced. Experiments evaluating the effectiveness of the proposed algorithm are reported, as well as method comparison and tracking synthetic and real image sequences.

Active Appearance Models (AAMs) are statistical models of shape and appearance widely used in computer vision to detect landmarks on objects like faces. Fitting an AAM to a new image can be formulated as a non-linear least-squares problem which is typically solved using iterative methods. Owing to its efficiency, Gauss-Newton optimization has been the standard choice over more sophisticated approaches like Newton. In this paper, we show that the AAM problem has structure which can be used to solve efficiently the original Newton problem without any approximations. We then make connections to the original Gauss-Newton algorithm and study experimentally the effect of the additional terms introduced by the Newton formulation on both fitting accuracy and convergence. Based on our derivations, we also propose a combined Newton and Gauss-Newton method which achieves promising fitting and convergence performance. Our findings are validated on two challenging in-the-wild data sets.

Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertrophy and cancer. Computer-aided accurate and computationally efficient prostate segmentation in TRUS images is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications, and heterogeneous intensity distribution in the prostate region. A multi-resolution framework using texture features in a parametric deformable statistical model of shape and appearance was developed to segment the prostate. Local phase information of log-Gabor quadrature filter extracted texture of the prostate region in TRUS images. Large bandwidth of log-Gabor filter ensures easy estimation of local orientations, and zero response for a constant signal provides invariance to gray level shift. This aids in enhanced representation of the underlying texture information of the prostate unaffected by speckle noise and imaging artifacts. The parametric model of the propagating contour is derived from principal component analysis of prior shape and texture information of the prostate from the training data. The parameters were modified using prior knowledge of the optimization space to achieve segmentation. The proposed method achieves a mean Dice similarity coefficient value of 0.95 ± 0.02 and mean absolute distance of 1.26 ± 0.51 millimeter when validated with 24 TRUS images of 6 data sets in a leave-one-patient-out validation framework. The proposed method for prostate TRUS image segmentation is computationally efficient and provides accurate prostate segmentations in the presence of intensity heterogeneities and imaging artifacts.