Carnegie Mellon University

Siemens Medical Solutions Usa, Inc.

Instituto Superior Tecnico

José M F Moura

Marie-Pierre Jolly

Ying Sun

Evgeny Toropov

Aleksandar Kavcic

João Paulo Costeira

Shanghang Zhang

Yuanwei Jin

Bernardo Pires

Joel B Harley

Methods and software utilizing artificial neural networks (ANNs) to estimate density and/or flow (speed) of objects in one or more scenes each captured in one or more images. In some embodiments, the ANNs and their training configured to provide reliable estimates despite one or more challenges that include but are not limited to, low-resolution images, low framerate image acquisition, high rates of object occlusions, large camera perspective, widely varying lighting conditions, and widely varying weather conditions. In some embodiments, fully convolutional networks (FCNs) are used in the ANNs. In some embodiments, a long short-term memory network (LSTM) is used with an FCN. In such embodiments, the LSTM can be connected to the FCN in a residual learning manner or in a direct connected manner. Also disclosed are methods of generating training images for training an ANN-based estimating algorithm that make training of the estimating algorithm less costly.

Deep learning methods for estimating density and/or flow of objects, and related methods and software

A method performed by a processing device, the method comprising: obtaining first waveform data indicative of traversal of a first signal through a structure at a first time; applying a scale transform to the first waveform data and the second waveform data; computing, by the processing device and based on applying the scale transform, a scale-cross correlation function that promotes identification of scaling behavior between the first waveform data and the second waveform data; performing one or more of: computing, by the processing device and based on the scale-cross correlation function, a scale factor for the first waveform data and the second waveform data; and computing, by the processing device and based on the scale-cross correlation function, a scale invariant correlation coefficient between the first waveform data and the second waveform data.

Temperature compensation in wave-based damage detection systems

Feature-matching methods for attempting to match visual features in one image with visual features in another image. Feature-matching methods disclosed progressively sample the affine spaces of the images for visual features, starting with a course sampling and iteratively increasing the density of sampling. Once a predetermined threshold number of unambiguous matches has been satisfied, the iterative sampling and matching can be stopped. The iterative sampling and matching methodology is especially, but not exclusively, suited for use in fully affine invariant feature matching applicants and can be particularly computationally efficient for comparing images that have large differences in observational parameters, such as scale, tilt, object-plane rotation, and image-plane rotation. The feature-matching methods disclosed can be useful in object/scene recognition applications. The disclosed methods can be implemented in software and various object/scene recognition systems.

Systems, methods, and software implementing affine-invariant feature detection implementing iterative searching of an affine space

A method and apparatus for target focusing and ghost image removal in synthetic aperture radar (SAR) is disclosed. Conventional SAR is not designed for imaging targets in a rich scattering environment. In this case, ghost images due to secondary reflections appear in the SAR images. We demonstrate, how, from a rough estimate of the target location obtained from a conventional SAR image and using time reversal, time reversal techniques can be applied to SAR to focus on the target with improved resolution, and reduce or remove ghost images.

Application of time reversal to synthetic aperture imaging

A system and method are provided for contrast-invariant registration of images, the system including a processor, an imaging adapter or a communications adapter for receiving an image data sequence, a user interface adapter for selecting a reference frame from the image sequence or cropping a region of interest (ROI) from the reference frame, a tracking unit for tracking the ROI across the image sequence, and an estimation unit for segmenting the ROI in the reference frame or performing an affine registration for the ROI; and the method including receiving an image sequence, selecting a reference frame from the image sequence, cropping a region of interest (ROI) from the reference frame, tracking the ROI across the image sequence, segmenting the ROI in the reference frame, and performing an affine registration for the ROI.

Contrast-invariant registration of cardiac and renal magnetic resonance perfusion images

A system and method are provided for integrated registration of images, the system including a processor, a first registration portion for performing rough registration of an image, a first segmentation portion for performing segmentation of an object of interest in the image, a second registration portion for performing fine registration of the image, and a second segmentation portion for performing segmentation of structures of the object of interest in the image; and the method including receiving a sequence of images, selecting an image from the sequence, cropping a region of interest (ROI) from the selected image, performing rough registration of the cropped ROI, performing segmentation of an object of interest from the rough registered ROI, performing fine registration of the ROI, and performing segmentation of structures of the object of interest from the fine registered ROI.

Integrated registration of dynamic renal perfusion magnetic resonance images

A method of determining branch metric values in a detector. The method includes receiving a plurality of time variant signal samples, the signal samples having one of signal-dependent noise, correlated noise, and both signal dependent and correlated noise associated therewith. The method also includes selecting a branch metric function at a certain time index and applying the selected function to the signal samples to determine the metric values.

Soft and hard sequence detection in ISI memory channels

The present invention is directed to a method of determining branch metric values for branches of a trellis for a Viterbi-like detector. The method includes the step of selecting a branch metric function for each of the branches at a certain time index. The method also includes the step of applying the selected function to a plurality of time variant signal samples to determine the metric values.

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Pittsburgh, PA, United States of America

José M F Moura