We introduce a technique to synthetically increase the framerate of semi-repetitive videos (i.e., videos of motion that repeats but not in an identical fashion) to aid in visualization. By reordering and combining fra...
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We introduce a technique to synthetically increase the framerate of semi-repetitive videos (i.e., videos of motion that repeats but not in an identical fashion) to aid in visualization. By reordering and combining frames from all repetitions, we produce a single non-repetitive sequence with much higher temporal resolution. Then, we use a novel frame warping technique based on a dense corrective flow to counteract differences between repetitions. The resulting video maintains smoothness of motion and additionally allows for seamless, infinite looping. We demonstrate the effectiveness of the proposed solution both quantitatively, by measuring the improvement over existing methods, and qualitatively, by performing a user evaluation and providing several examples in the article and accompanying video.
In this paper, we present a fusion approach to solve the nonrigid shape recovery problem, which takes advantage of both the appearance information and the local features. We have two major contributions. First, we pro...
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In this paper, we present a fusion approach to solve the nonrigid shape recovery problem, which takes advantage of both the appearance information and the local features. We have two major contributions. First, we propose a novel progressive finite Newton optimization scheme for the feature-based nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem that has a closed-form solution for a given set of observations. Second, we propose a deformable Lucas-Kanade algorithm that triangulates the template image into small patches and constrains the deformation through the second-order derivatives of the mesh vertices. We formulate it into a sparse regularized least squares problem, which is able to reduce the computational cost and the memory requirement. The inverse compositional algorithm is applied to efficiently solve the optimization problem. We have conducted extensive experiments for performance evaluation on various environments, whose promising results show that the proposed algorithm is both efficient and effective.
We present the VROnSite platform that supports immersive training of first responder units' on-site squad leaders. Our training platform is fully immersive, entirely untethered to ease use and provides two means o...
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We present the VROnSite platform that supports immersive training of first responder units' on-site squad leaders. Our training platform is fully immersive, entirely untethered to ease use and provides two means of navigation-abstract and natural walking-to simulate stress and exhaustion, two important factors for decision making. With the platform's capabilities, we close a gap in prior art for first responder training. Our research is closely interlocked with stakeholders from multiple fire brigades to gather early feedback in an iterative design process. In this paper, we present the system's design rationale, provide insight into the process of training scenario development and present results of a user study with 41 squad leaders from the firefighting domain. Virtual disaster environments with two different navigation types were evaluated using quantitative and qualitative measures. Participants considered our platform highly suitable for training of decision making in complex first responder scenarios and results show the importance of the provided navigation technologies in this context.
This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extracti...
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This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape serves as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.
We develop a mathematical framework for quantifying and understanding multidimensional frequency modulations in digital images. We begin with the widely accepted definition of the instantaneous frequency vector ( IF) ...
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We develop a mathematical framework for quantifying and understanding multidimensional frequency modulations in digital images. We begin with the widely accepted definition of the instantaneous frequency vector ( IF) as the gradient of the phase and define the instantaneous frequency gradient tensor ( IFGT) as the tensor of component derivatives of the IF vector. Frequency modulation bounds are derived and interpreted in terms of the eigendecomposition of the IFGT. Using the IFGT, we derive the ordinary differential equations ( ODEs) that describe image flowlines. We study the diagonalization of the ODEs of multidimensional frequency modulation on the IFGT eigenvector coordinate system and suggest that separable transforms can be computed along these coordinates. We illustrate these new methods of image pattern analysis on textured and fingerprint images. We envision that this work will find value in applications involving the analysis of image textures that are nonstationary yet exhibit local regularity. Examples of such textures abound in nature.
Background The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from ...
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Background The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.
Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as heart rate and heart rate variability (Wang et al. in FBB 6:33, ...
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Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as heart rate and heart rate variability (Wang et al. in FBB 6:33, 2018). rPPG signals are typically extracted from facial videos by performing spatial averaging to obtain temporal RGB traces. Although this spatial averaging simplifies computation, it is accompanied by loss of essential spatial information which might reveal interesting relationships between signals from different spatial regions. In this article, we present a novel algorithm adapted from generalized eigenvalue decomposition (GEVD) to estimate this spatial rPPG distribution. GEVD is an extremely versatile algorithm that finds uses in signal and imageprocessing and analytical problems such as principal component analysis and Fisher discriminant analysis (Ghojogh et al. in Tutorial 2: 1-8, 2019)(Han and Clemmensen in PR 49:43-54, 2016). It is performed using the QZ algorithm (Moler and Stewart in JNA 10(2):241-256, 2010), which in turn uses Householder transformations (Householder in JACM 5(4):339-342, 1958) to extract generalized eigenvectors of a pair of matrices. We adapt the QZ algorithm for the domain of spatio-temporal biomedical signals such as remote photoplethysmography (rPPG), electrocardiography and electroencephalography signals. We call this algorithm Temporal-QZ, which employs vectorization techniques to extract generalized eigenvectors over spatial data points simultaneously. We validate this extension in the domain of remote photoplethysmography (rPPG) measurement, for the estimation of spatial rPPG distribution of skin.
Precise localization of Wireless Capsule Endoscopy (WCE) inside the curly, long, and compact small intestine remains a challenging problem facing researchers for more than a decade. Conventional Radio Frequency (RF) l...
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Precise localization of Wireless Capsule Endoscopy (WCE) inside the curly, long, and compact small intestine remains a challenging problem facing researchers for more than a decade. Conventional Radio Frequency (RF) localization techniques, commonly used in outdoor and indoor area, have demonstrated a few centimeters accuracy when applying to the inside of human body. In this paper, using 3D Posterior Cramer-Rao Lower Bound (PCRLB) as a framework for performance evaluation, we demonstrated that millimetric accuracy can be achieved using hybrid RF and imageprocessing localization technique. This level of accuracy enables precise simultaneous localization and mapping of the WCE movement path inside the small intestine. Using the PCRLB framework, we provided in-depth analysis on hybrid localization performance regarding the effects of WCE movement estimation, the effects of system bandwidth as well as the effects of on-body sensor numbers and placements.
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