Other

Columbia University

Kriegman-belhumeur Vision Technologies, LLC

Neeraj Kumar

Peter Neil Belhumeur

Shree Kumar Nayar

David J Kriegman

David W Jacobs

Dimitri Bitouk

Amit Sethi

Kamal Prasad Peddinti

Alexander C Berg

An automated batch stainer for staining biological specimens on microscope slides. The automated batch stainer includes a slide rack assembly configured to hold microscope slides, a robotic arm that manipulates the slide rack assembly, at least one bath containing reagents and capable of receiving the slide rack assemblies, a heating chamber capable of heating multiple slide rack assemblies, a bar code reader, at least one software program including a graphical user interface and configured to calculate the timing and sequence of the staining protocol and implement the staining protocol by controlling the movements of the robotic arm.

Automated batch stainer for immunohistochemistry

Systems, methods and devices for determination of disease class scores for patient tissue are disclosed. The disease class scores may be based on the probability or probability-like metric for a disease condition or outcome. The system includes an imaging apparatus and a computing system with instructions executable by a processor. The computer system may locate one or more points-of-interest on the pre-processed images of the patient tissue using a point-of-interest detector and generate one or more disease spatial maps including one or more probability or probability-like metric of disease classifications on the detected points-of-interest. The information in the disease spatial maps is aggregated to produce disease class scores.

Systems and methods for computational pathology using points-of-interest

A system is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.

Method and system for localizing parts of an object in an image for computer vision applications

A method is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.

Methods, systems, and media for swapping faces in images are provided. In some embodiments, a detected face and face data corresponding to an input image is received. A pose bin associated with the detected face is then identified based on the face data. Next, the detected face is aligned to a generic face associated with the pose bin. At least a portion of a candidate face associated with the pose bin is selected. The at least a portion of the candidate face is then copied to a copy of the input image that is aligned with the generic image to form a swapped-face image. The swapped-face image is next aligned to the input image to form an output image, and then the output image is outputted to a display.

Methods, systems, and media for swapping faces in images

Methods, systems, and media for automatically classifying face images are provided. In some embodiments, features of the face image to be classified for an attribute are selected, wherein each of the features corresponds to a different region of the face image and specifies one or more of a type of pixel data to be evaluated for the region, a normalization to be applied for the region, and an aggregation to be applied for the region. The face image is classified with respect to the attribute based on the features of the image, and the attribute and a confidence value are assigned to the face image based on the classifying. A query is received from a user, and the attribute is identified as corresponding to the query. The face image is determined as corresponding to the attribute, and the face image is identified to the user as corresponding to the query.

Growing community of inventors

New York, NY, United States of America

Neeraj Kumar