Abstract: Systems and methods for generating a visual image from audio data and for training the same. The method may include: mapping audio data registered with a microphone array onto closest visual representations in a data manifold for latent representation of images of a visual modality; and generating a visual image of the visual modality from the closest visual representations.

Abstract: A system and method are provided for registering input images to reference objects to generate output parameters. The method includes obtaining an input image and a reference object, wherein the reference object comprises a reference image or a template corresponding to a model of content in the reference image. The method also includes performing a registration of the input image by estimating a geometric transform and/or a set of non-linear transform parameters, without using predefined image features. The method also includes measuring a misalignment error between the input images and the reference object and generating a set of output parameters. The output parameters can include a set of one or more camera parameters or a set of geometric and non-linear transformation parameters.

Abstract: A system and method are provided for registering input images to reference objects to generate output parameters. The method includes obtaining an input image and a reference object, wherein the reference object comprises a reference image or a template corresponding to a model of content in the reference image. The method also includes performing a registration of the input image by estimating a geometric transform and/or a set of non-linear transform parameters, without using predefined image features. The method also includes measuring a misalignment error between the input images and the reference object and generating a set of output parameters. The output parameters can include a set of one or more camera parameters or a set of geometric and non-linear transformation parameters.

Abstract: A method for training a feature detector of an image processing device, including the steps of detecting features in the image to generate a score map, computing a center of mass on the score map to generate a location, extracting a patch from the image at the location by a first spatial transformer, estimating an orientation of the patch, rotating the patch in accordance with the patch orientation with a second spatial transformer, and describing the rotated patch to create a description vector.

Abstract: A method for training a feature detector of an image processing device, including the steps of detecting features in the image to generate a score map, computing a center of mass on the score map to generate a location, extracting a patch from the image at the location by a first spatial transformer, estimating an orientation of the patch, rotating the patch in accordance with the patch orientation with a second spatial transformer, and describing the rotated patch to create a description vector.

Abstract: An apparatus for detecting an object includes a filter for filtering a current input image and a background model generated based on a previous input image, a homography matrix estimation unit for estimating a homography matrix between the current input image and the background model, an image alignment unit for converting the background model by applying the homography matrix to a filtered background model and aligning a converted background model and a filtered current input image, and a foreground/background detection unit for detecting a foreground by comparing corresponding pixels between the converted background model and the filtered current input image.

Abstract: Provided are a method and apparatus for detecting an abnormal movement. The apparatus includes a feature tracing unit configured to extract features of a moving object in an input image, trace a variation in position of the extracted features according to time, and ascertain trajectories of the extracted features; a topic online learning unit configured to classify the input image in units of documents which are bundles of the trajectories, and ascertain probability distribution states of topics, which constitute the classified document, by using an online learning method which is a probabilistic topic model; and a movement pattern online learning unit configured to learn a velocity and a direction for each of the ascertained topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics.

Abstract: Provided are a method and apparatus for detecting an abnormal movement. The apparatus includes a feature tracing unit configured to extract features of a moving object in an input image, trace a variation in position of the extracted features according to time, and ascertain trajectories of the extracted features; a topic online learning unit configured to classify the input image in units of documents which are bundles of the trajectories, and ascertain probability distribution states of topics, which constitute the classified document, by using an online learning method which is a probabilistic topic model; and a movement pattern online learning unit configured to learn a velocity and a direction for each of the ascertained topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics.