Abstract: A local connectivity feature transform (LCFT) is applied to binary document images containing text characters, to generate transformed document images which are then input into a bi-directional Long Short Term Memory (LSTM) neural network to perform character/word recognition. The LCFT transformed image is a gray scale image where the pixel values encode local pixel connectivity information of corresponding pixels in the original binary image. The transform is one that provides a unique transform score for every possible shape represented as a 3×3 block. In one example, the transform is computed using a 3×3 weight matrix that combines bit coding with a zigzag pattern to assign weights to each element of the 3×3 block, and by summing up the weights for the non-zero elements of the 3×3 block shape.

Abstract: An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification.

Abstract: A method, computer readable medium, and system are disclosed of enhancing cell images for analysis. The method includes performing a multi-thresholding process on a cell image to generate a plurality of images of the cell image; smoothing each component within each of the plurality of images; merging the smoothed components into a merger layer; classifying each of the components of the merged layer into convex cell regions and concave cell regions; combining the concave cell regions with a cell boundary for each of the corresponding concave cell regions to generate a smoothed shape profile for each of the concave cell regions; and generating an output image by combining the convex cell regions with the concave cell regions with smoothed shape profiles.

Abstract: A method, system and non-transitory computer readable medium are disclosed for recognizing gestures, the method includes capturing at least one three-dimensional (3D) video stream of data on a subject; extracting a time-series of skeletal data from the at least one 3D video stream of data; isolating a plurality of points of abrupt content change called temporal cuts, the plurality of temporal cuts defining a set of non-overlapping adjacent segments partitioning the time-series of skeletal data; identifying among the plurality of temporal cuts, temporal cuts of the time-series of skeletal data having a positive acceleration; and classifying each of the one or more pair of consecutive cuts with the positive acceleration as a gesture boundary.

Abstract: A method, a system, and a non-transitory computer readable medium for recognizing an object. The method includes emitting an array of infrared rays from an infrared emitter towards a projection region, the projection region including a first object; generating a reference infrared image by recording an intensity of ray reflection from the projection region without the first object; generating a target infrared image by recording the intensity of ray reflection from the projection region with the first object; comparing the target infrared image to the reference infrared image to generate a predetermined intensity threshold; and extracting the first object from the target infrared image, if the intensity of ray reflection of the target infrared image of the first object exceeds the predetermined intensity threshold.

Abstract: A method, computer readable storage medium, and system are disclosed for improving communication productivity in a conference between two or more subjects, wherein at least one of the two or more subjects participates in the conference from a first location and one or more of the two or more subjects participate in the meeting from a second location. The method includes capturing, at least one first three-dimensional (3D) stream of data and at least one second three-dimensional (3D) stream of data on each of the two or more subjects participating in the conference; generating a synchrony score for the two or more subjects, wherein the synchrony score is calculated by comparing time series of skeletal data of each of the two or more subjects to one another for a defined period of time; and using the synchrony score to generate an engagement index between the two or more subjects.

Abstract: A method and system are disclosed for recognizing an object, the method including emitting one or more arranged patterns of infrared rays (IR) from an infrared emitter towards a projection region, the one or more arranged patterns of infrared rays forming unique dot patterns; mapping the one or more arranged patterns of infrared rays on the operation region to generate a reference image; capturing an IR image and a RGB image of an object with a wearable device, the wearable device including an infrared (IR) camera and a RGB camera; extracting IR dots from the IR image and determining a match between the extracted IR dots and the reference image; determining a position of the RGB image on the reference image; and mapping the position of the RGB image to a coordinate on the projection region.

Abstract: A method, system and non-transitory computer readable medium are disclosed for recognizing gestures, the method includes capturing at least one three-dimensional (3D) video stream of data on a subject; extracting a time-series of skeletal data from the at least one 3D video stream of data; isolating a plurality of points of abrupt content change called temporal cuts, the plurality of temporal cuts defining a set of non-overlapping adjacent segments partitioning the time-series of skeletal data; identifying among the plurality of temporal cuts, temporal cuts of the time-series of skeletal data having a positive acceleration; and classifying each of the one or more pair of consecutive cuts with the positive acceleration as a gesture boundary.

Abstract: A method, computer readable storage medium, and system are disclosed for improving communication productivity in a conference between two or more subjects, wherein at least one of the two or more subjects participates in the conference from a first location and one or more of the two or more subjects participate in the meeting from a second location. The method includes capturing, at least one first three-dimensional (3D) stream of data and at least one second three-dimensional (3D) stream of data on each of the two or more subjects participating in the conference; generating a synchrony score for the two or more subjects, wherein the synchrony score is calculated by comparing time series of skeletal data of each of the two or more subjects to one another for a defined period of time; and using the synchrony score to generate an engagement index between the two or more subjects.

Abstract: A method, computer readable storage medium, and system are disclosed for improving communication productivity, comprising: capturing at least one three-dimensional (3D) stream of data on two or more subjects; extracting a time-series of skeletal data from the at least one 3D stream of data on the two or more subjects; and determining an engagement index between the two or more subjects by comparing the time-series of skeletal data on each of the two or more subjects over a time window.

Abstract: A method, computer readable medium, and system are disclosed of enhancing cell images for analysis. The method includes performing a multi-thresholding process on a cell image to generate a plurality of images of the cell image; smoothing each component within each of the plurality of images; merging the smoothed components into a merger layer; classifying each of the components of the merged layer into convex cell regions and concave cell regions; combining the concave cell regions with a cell boundary for each of the corresponding concave cell regions to generate a smoothed shape profile for each of the concave cell regions; and generating an output image by combining the convex cell regions with the concave cell regions with smoothed shape profiles.

Abstract: A method, a computer readable medium, and a system are disclosed for cell segmentation. The method including generating a binary mask from an input image of a plurality of cells, wherein the binary mask separates foreground cells from a background; classifying each of the cell regions of the binary mask into single cell regions, small cluster regions, and large cluster regions; performing, on each of the small cluster regions, a segmentation based on a contour shape of the small cluster region; performing, on each of the large cluster regions, a segmentation based on a texture in the large cluster regions; and outputting an image with cell boundaries.

Abstract: A method and system for recognizing behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; computing feature extractions on the extracted body skeleton data to generate a plurality of 3 dimensional delta units for each frame of the extracted body skeleton data; generating a plurality of histogram sequences for each frame by projecting the plurality of 3 dimensional delta units for each frame to a spherical coordinate system having a plurality of spherical bins; generating an energy map for each of the plurality of histogram sequences by mapping the plurality of spherical bins versus time; applying a Histogram of Oriented Gradients (HOG) algorithm on the plurality of energy maps to generate a single column vector; and classifying the single column vector as a behavior and/or emotion.

Abstract: A method, computer readable storage medium, and system are disclosed for improving communication productivity, comprising: capturing at least one three-dimensional (3D) stream of data on two or more subjects; extracting a time-series of skeletal data from the at least one 3D stream of data on the two or more subjects; and determining an engagement index between the two or more subjects by comparing the time-series of skeletal data on each of the two or more subjects over a time window.

Abstract: A method and system are disclosed for recognizing an object, the method including emitting one or more arranged patterns of infrared rays (IR) from an infrared emitter towards a projection region, the one or more arranged patterns of infrared rays forming unique dot patterns; mapping the one or more arranged patterns of infrared rays on the operation region to generate a reference image; capturing an IR image and a RGB image of an object with a wearable device, the wearable device including an infrared (IR) camera and a RGB camera; extracting IR dots from the IR image and determining a match between the extracted IR dots and the reference image; determining a position of the RGB image on the reference image; and mapping the position of the RGB image to a coordinate on the projection region.

Abstract: A method for recognizing abnormal behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; classifying the extracted body skeleton data as normal behavior or abnormal behavior; and generating an alert, if the extracted skeleton data is classified as abnormal behavior.

Abstract: A method, system and non-transitory computer readable medium for recognizing gestures are disclosed, the method includes capturing at least one three-dimensional (3D) video stream of data on a subject; extracting a time-series of skeletal data from the at least one 3D video stream of data; isolating a plurality of points of abrupt content change called temporal cuts, the plurality of temporal cuts defining a set of non-overlapping adjacent segments partitioning the time-series of skeletal data; identifying among the plurality of temporal cuts, temporal cuts of the time-series of skeletal data having a positive acceleration; and classifying each of the one or more pair of consecutive cuts with the positive acceleration as a gesture boundary.

Abstract: A method, a system, and a non-transitory computer readable medium for recognizing an object are disclosed, the method including: emitting an array of infrared rays from an infrared emitter towards a projection region, the projection region including a first object; generating a reference infrared image by recording an intensity of ray reflection from the projection region without the first object; generating a target infrared image by recording the intensity of ray reflection from the projection region with the first object; comparing the target infrared image to the reference infrared image to generate a predetermined intensity threshold; and extracting the first object from the target infrared image, if the intensity of ray reflection of the target infrared image of the first object exceeds the predetermined intensity threshold.

Abstract: A method and system for recognizing behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; computing feature extractions on the extracted body skeleton data to generate a plurality of 3 dimensional delta units for each frame of the extracted body skeleton data; generating a plurality of histogram sequences for each frame by projecting the plurality of 3 dimensional delta units for each frame to a spherical coordinate system having a plurality of spherical bins; generating an energy map for each of the plurality of histogram sequences by mapping the plurality of spherical bins versus time; applying a Histogram of Oriented Gradients (HOG) algorithm on the plurality of energy maps to generate a single column vector; and classifying the single column vector as a behavior and/or emotion.

Abstract: A method for recognizing abnormal behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; classifying the extracted body skeleton data as normal behavior or abnormal behavior; and generating an alert, if the extracted skeleton data is classified as abnormal behavior.