Microsoft Technology Licensing, LLC

Siemens Medical Solutions USA, Inc.

Nuctech Company Limited

Siemens Corporation

Zhuowen Tu

Dorin Comaniciu

Xiang Sean Zhou

Adrian Barbu

Baining Guo

Luca Bogoni

Eric I-Chao Chang

Junyan Zhu

Ke Deng

Zhiqiang Chen

Ziran Zhao

Liang Li

Yan Xu

Tommer Leyvand

Szymon Piotr Stachniak

Yichen Wei

Jin Cui

Baoyuan Wang

Dong Lin

Wei Shen

Lu-yong Wang

Duokun Zhang

Christian P Schultz

Daniel Fasulo

Zhi Peng

Jiajun Wu

Low-latency gesture detection is described, for example, to compute a gesture class from a live stream of image frames of a user making a gesture, for example, as part of a natural user interface controlling a game system or other system. In examples, machine learning components are trained to learn gesture primitives and at test time, are able to detect gestures using the learned primitives, in a fast, accurate manner. For example, a gesture primitive is a latent (unobserved) variable features of a subset of frames from a sequence of frames depicting a gesture. For example, the subset of frames has many fewer frames than a sequence of frames depicting a complete gesture. In various examples gesture primitives are learnt from instance level features computed by aggregating frame level features to capture temporal structure. In examples frame level features comprise body position and body part articulation state features.

Low-latency gesture detection

A stereoscopic imaging system and method is disclosed. In one aspect the system includes an X-ray source that emits a plurality of X-ray fanbeams. The system includes columns of detectors. Each column of detectors is arranged at a preset angle with respect to the X-ray source. The detectors detect a strength of an X-ray fanbeam penetrating an object under inspection. The detectors form transmission images when the object intersects, or moves along a direction intersecting with, the X-ray fanbeams. The system includes a reconstruction apparatus that uses any two of the formed transmission images as a binocular image. The reconstruction apparatus calculates depth information of the object on the transmission images. The reconstruction apparatus superposes and fuses the calculated depth information to obtain 3D information. The reconstruction apparatus performs 3D reconstruction so that the detected object can be presented in a stereoscopic manner from different view angles.

Stereoscopic imaging systems and methods

The techniques and systems described herein create and train a multiple clustered instance learning (MCIL) model based on image features and patterns extracted from training images. The techniques and systems separate each of the training images into a plurality of instances (or patches), and then learn multiple instance-level classifiers based on the extracted image features. The instance-level classifiers are then integrated into the MCIL model so that the MCIL model, when applied to unclassified images, can perform image-level classification, patch-level clustering, and pixel-level segmentation.

Multiple cluster instance learning for image classification

Techniques for unsupervised object class discovery via bottom-up multiple class learning are described. These techniques may include receiving multiple images containing one or more object classes. The multiple images may be analyzed to extract top saliency instances and least saliency instances. These saliency instances may be clustered to generate and/or update statistical models. The statistical models may be used to discover the one or more object classes. In some instances, the statistical models may be used to discover object classes of novel images.

Unsupervised object class discovery via bottom up multiple class learning

Embodiments are disclosed that relate to the correction of an estimated pose determined from depth image data. One disclosed embodiment provides, on a computing system, a method of obtaining a representation of a pose of articulated object from image data capturing the articulated object. The method comprises receiving the depth image data, obtaining an initial estimated skeleton of the articulated object from the depth image data, applying a random forest subspace regression function to the initial estimated skeleton, and determining the representation of the pose based upon a result of applying the random forest subspace regression to the initial estimated skeleton.

Estimated pose correction

A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier. Each set is then used to train the left and right sub-trees recursively.

Probabilistic boosting tree framework for learning discriminative models

A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

Joint classification and subtype discovery in tumor diagnosis by gene expression profiling

A system and method for using learned discriminative models to segment a border of an anatomical structure in a three dimensional (3D) image is disclosed. A discriminative probability model is computed for each voxel in the 3D image. Thresholding is performed on each discriminative probability model. One or more two dimensional (2D) slices of the thresholded 3D image along X-Y planes are obtained. Seed regions are selected in the 2D slices. Morphological region growing is performed on the selected seed regions. An initial 3D segmentation is obtained. Boundary evolution is performed on the initial 3D segmentation. The segmented anatomical structure is removed. in the original 3D image.

System and method for using learned discriminative models to segment three dimensional colon image data

A method for detecting target objects in a three dimensional (3D) image volume of an anatomical structure is disclosed. A set of candidate locations in the image volume are obtained. For each candidate location, sub-volumes of at least two different scales are cropped out. Each sub-volume comprises a plurality of voxels. For each of the sub-volumes, each sub-volume is rotated in at least two different orientations. A shape classifier is applied to each sub-volume. If the voxels in the sub-volume pass the shape classifier, a gradient direction is computed for the voxels. If the gradient direction for the voxels is one of a predefined orientation, a probability classifier is applied to the voxels. A probability measure computed by the probability classifier as a confidence measure is used for the sub-volume. If the confidence measure is above a predetermined threshold value, the sub-volume is determined to contain the target object.

Method for detecting polyps in a three dimensional image volume

A method for segmenting a digitized image includes providing a digitized image comprising a plurality of intensities defined on a lattice of pixels, initializing a Bayesian prior probability and likelihood function for segmenting said image, decomposing said image into a union of a plurality of subspaces wherein each subspace is a product of k-partitions, and distributing a plurality Bayesian posterior probability functions over said subspaces, updating said Bayesian posterior probability functions via a data-driven Markov-chain, and selecting a subset of said Bayesian posterior probability functions that approximate a true Bayesian posterior probability function.

System and method for anatomical structure parsing and detection

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San Diego, CA, United States of America

Zhuowen Tu