Patient adherence is pivotal in clinical trials for new pharmaceuticals. Ensuring adherence is essential for robust safety and efficacy analyses. Intentional non-adherence, marked by the patient’s deceptive actions during dosing, complicates the accuracy of measurement. This paper proposes a novel learning-based system combining vision and metadata for detecting potentially deceptive dosing videos. Exploiting neural networks’ image understanding, it integrates visual and contextual data through ensemble learning. The system, efficient and adaptable, pioneers real-world deception capture, boosting adherence precision in trials. Our experiments show its remarkable real-world performance1.

This paper presents a novel speckle reduction algorithm based on sparse representation and structure characteristics of PolSAR image. First, each pixel in original image is classified into bright point or line targets, dark point or line targets and others to form a classification map. Second, sparse decomposition and reconstruction is performed on PolSAR image by OMP and K-SVD methods to filter speckle. Finally, the blurred point and line targets in filtered image are enhanced with the classification map. Experimental results with the data of Hayward area from AIRSAR system show that the proposed method is effective both on speckle reduction and scattering characteristics preservation.

Learning-based methods have been widely used in detecting landmarks or anatomical structures in various medical imaging applications. The performance of discriminative learning techniques has been demonstrated superior to traditional low-level filtering in robustness and scalability. Nevertheless, some structures and patterns are more difficult to be defined by such methods and complicated and ad-hoc methods still need to be used, e.g. a non-rigid and highly deformable wire structure. In this paper, we propose a novel scheme to train classifiers to detect the markers and guide wire segment anchored by markers. The classifier utilizes the markers as the end point and parameterizes the wire in-between them. The probabilities of the markers and the wire are integrated in a Bayesian framework. As a result, both the marker and the wire detection are improved by such a unified approach. Promising results are demonstrated by quantitative evaluation on 263 fluoroscopic sequences with 12495 frames. Our training scheme can further be generalized to localize longer guidewire with higher degrees of parameterization.

Magnetic resonance imaging (MRI) is currently the gold standard for left ventricle (LV) quantification. Detection of the LV in an MRI image is a prerequisite for functional measurement. However, due to the large variations in the orientation, size, shape, and image intensity of the LV, automatic LV detection is challenging. In this paper, we propose to use marginal space learning (MSL) to exploit the recent advances in learning discriminative classifiers. Unlike full space learning (FSL) where a monolithic classifier is trained directly in the five dimensional object pose space (two for position, one for rotation, and two for anisotropic scaling), we train three detectors, namely, the position detector, the position-orientation detector, and the position-orientation-scale detector. As a contribution of this paper, we perform thorough comparison between MSL and FSL. Experiments show MSL significantly outperforms FSL on both the training and test sets. Additionally, we also detect several LV landmarks, such as the LV apex and two annulus points. If we combine the detected candidates from both the whole-object detector and landmark detectors, we can further improve the system robustness even when one detector fails. A novel ranking-based strategy is proposed to combine the detected candidates from all detectors. Experiments show our ranking-based aggregation approach can significantly reduce the detection outliers.

Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the nonrigid facial movement such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both nonrigid deformations and pose changes (multiview) to a stored 3D face model with neutral expression. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built for each subject in the gallery with respect to the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and pose changes simultaneously. A fully automatic and prototypic deformable model based 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy in comparison to matching with 3D neutral models by 7 and 10 percentage points, respectively, on a subset of the FRGC v2.0 3D benchmark and the MSU multiview 3D face database with expression variations.

Human face recognition based on 3D surface matching is promising for overcoming the limitations of current 2D image-based face recognition systems. The 3D shape is invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates for each subject in a large database (associated with various expressions) is not practical. We present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously. Experimental results demonstrate that the proposed matching scheme based on deformation modeling improves the matching accuracy.

Crackles are discontinuous adventitious lung sounds with an explosive and transient character, and occur frequently in pulmonary diseases. They are the most important parameter for the diagnosis. This paper presents a new filter for automatic separation of the crackles from vesicular sounds. The proposed filter is based on the wavelet packet transform and applies two thresholds, which are defined in time-domain and frequency-domain respectively, to wavelet coefficients to achieve the separation task. This filter is more accurate and efficient compared to its rivals. Experimental results are given in detail and demonstrate its excellent performance

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.

An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.

To support real-time tracking of objects in video sequences, there has been considerable effort directed at developing optical flow and general motion-based image segmentation algorithms. The goal is to segment multiple moving objects in the image based on their relative motion. This task can be complicated by the presence of lighting variations. Furthermore, a combination of multiple motions and complex lighting effects can lead to dramatic image variations that may not be adequately accounted for by a single motion-based segmentation algorithm. We propose to fuse the results of multiple motion segmentation algorithms to improve the system robustness. Our approach uses the expectation maximization (EM) algorithm as a fusion engine. It also uses principal components analysis (PCA) to perform dimensionality reduction to improve the performance of the EM algorithm and reduce the processing burden. The performance of the proposed fusion algorithm has been demonstrated in the "smart airbag" application of monitoring occupants in a moving automobile to determine if they are too close to the instrument panel (airbag). Through fusion of the outputs of multiple algorithms we are able to reduce the percentage of pixels missed on the target by 35.

Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of the input face images have different sensitivity to these variations. Therefore, a combination of different face classifiers which can integrate the complementary information should lead to improved classification accuracy. We use the sum rule and RBF-based integration strategies to combine three commonly used face classifiers based on PCA, ICA and LDA representations. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed classifier combination approaches outperform individual classifiers.