An information processing apparatus that controls registration of an image and association information to a registration unit of an image retrieval device that includes the registration unit in which the image and association information are registered in an associated manner, a retrieving unit that retrieves an image being similar to a retrieval subject image from the registration unit, and a transmitting unit that transmits the association information associated with the retrieved image to a terminal device, the information processing apparatus comprising: a selecting unit that selects the association information to be associated with a registration target image to be registered in the registration unit; a setting unit that performs setting to a setting item according to the association information selected by the selecting unit; and a transmitting unit that transmits the registration target image and the association information in which the setting item is set, to the image retrieval device.

A method for denoising a range image acquired by a time-of-flight (ToF) camera by first determining locations of edges, and a confidence value of each pixel, and based on the locations of the edges, determining geodesic distances of neighboring pixels. Based on the confidence values, reliabilities of the neighboring pixels are determined and scene dependent noise is reduced using a filter.

Both pixel-oriented analysis and the more accurate yet slower object-oriented analysis are used to recognize patterns in images of stained cancer tissue. Images of tissue from other patients that are similar to tissue of a target patient are identified using the standard deviation of color in the images. Object-oriented segmentation is then used to segment small portions of the images of the other patients into object exhibiting object characteristics. Pixelwise descriptors associate each pixel in the remainder of the images with object characteristics based on the color of pixels at predetermined offsets from the characterized pixel. Pixels in the image of the target patient are assigned object characteristics without performing the slow segmentation of the image into objects. A pixel heat map is generated from the target image by assigning pixels the color corresponding to the object characteristic that the pixelwise descriptors indicate is most likely associated with each pixel.

Techniques related to eye contact correction to provide a virtual user gaze aligned with a camera while the user views a display are discussed. Such techniques may include determining and reducing histogram of oriented gradient features for an eye region of a source image to provide a feature set, applying a pretrained classifier to the feature set to determine a motion vector field for the eye region, and warping and inserting the eye region into the source image to generate an eye contact corrected image.

Techniques related to eye contact correction to provide a virtual user gaze aligned with a camera while the user views a display are discussed. Such techniques may include encoding an eye region of a source image using a pretrained neural network to generate compressed features, applying a pretrained classifier to the features to determine a motion vector field for the eye region, and warping and inserting the eye region into the source image to generate an eye contact corrected image.

A tracking and recognition system is provided. The system includes a computer vision-based identity recognition system configured to recognize one or more persons, without a priori knowledge of the respective persons, via an online discriminative learning of appearance signature models of the respective persons. The computer vision-based identity recognition system includes a memory physically encoding one or more routines, which when executed, cause the performance of constructing pairwise constraints between the unlabeled tracking samples. The computer vision-based identity recognition system also includes a processor configured to receive unlabeled tracking samples collected from one or more person trackers and to execute the routines stored in the memory via one or more algorithms to construct the pairwise constraints between the unlabeled tracking samples.

Provided us a visual cortical circuit apparatus comprising: a current mirror unit which uses a transistor as a current source to generate a current having the same size as that of a reaction; a transconductance unit which takes, as an input, the current generated by the current mirror unit and outputs a voltage using a transconductance; and a buffer unit for converting the voltage output from the transconductance unit into a current and buffering the current.

One or more image parameters of an image may be analyzed using a hierarchical set of models. Executing individual models in the set of models may generate outputs from analysis of different image parameters of the image. Inputs of one or more of the models may be conditioned on a set of outputs derived from one or more preceding model in the hierarchy.

In an example embodiment, an image transformation is automatically performed on a digital image to improve perceived professionalism of a subject of the image. A machine learning algorithm is utilized to generate a professionalism score for the digital image, the utilizing a machine learning algorithm comprising: a training mode where a plurality of sample images with labeled professionalism scores are used to train a classification function in a model that produces as professionalism score as output; an analysis mode where the model is used to generate a professionalism score for the digital image. Then the professionalism score is used as an input to a continuous variable optimization algorithm to determine an optimum version of the digital image from a plurality of possible versions of the digital image on which one or more image transformations have been performed, using the classification function.

An approach is provided for managing processing rules used to process electronic data in computer networks. An application provides the capability for users to define and manage classifications for electronic data. The application also provides the capability for users to define and manage processing rules for each classification. This may include specifying, for each processing rule, a classification to which the processing rule corresponds, one or more conditions under which the processing rule is to be applied and optionally, not applied, a priority for the processing rule, and one or more actions to be performed. The priority may be used to determine which rule is to be applied when more than one rule corresponds to a classification. The application supports the definition and management of classifications and rules on a logical group-by-logical group basis.