A feature amount acquisition unit divides input image data into blocks each including a plurality of pixels and acquires the feature amounts of the blocks. A first detection unit detects a pair of two blocks whose feature amounts are approximate to each other and extract a first aggregate area in which one block of each of the pairs gathers and a second aggregate area in which the other block of each of the pairs gathers from the input image data. A second detection unit performs additional detection of adding a block adjacent to the first aggregate area to the first aggregate area, and adding a block adjacent to the second aggregate area to the second aggregate area.

The methods, devices, and systems may allow a practitioner to obtain information regarding a biological sample, including analytical data, a medical diagnosis, and/or a prognosis or predictive analysis. The method, devices, and systems may provide a grade or level of development for identified diseases. In addition, the methods, devices and systems may generate a confidence value for the predictive classifications generated, which may, for example be generated in a format to show such confidence value or other feature in a graphical representation (e.g., a color code). Further, the methods, devices and system may aid in the identification and discovery of new classes and tissue sub-types.

The optical character recognition method applies a first OCR engine to provide an identification of characters of at least a first type of characters and zones of at least a second type of characters in the character string image. A second OCR engine is applied on the zones of the at least second type of characters to provide an identification of characters of a second type of characters. The characters identified by the first OCR engine and by the second OCR engine are in a further step combined to obtain the identification of the characters of the character string image.

Methods and systems are provided for electronic authentication. A modified electronic image is generated by altering at least a pixel of an electronic image. The electronic image is an image that has been previously viewed by a user during a setup process. In response to receiving an authentication request from the user, the modified electronic image is displayed to the user via an electronic display along with one or more other electronic images. A determination is made as to whether the user is able to recognize the modified electronic image. In response to determination that the user is able to recognize the modified electronic image, the authenticating request is granted.

A pattern extraction apparatus for extracting a pattern of event occurrence from event time-series data generates an adjacent event graph by fetching adjacent events from the event time-series data, representing each of the adjacent events as a node, connecting the nodes by a directed link having a transition direction between the adjacent events and a weight, representing identical events as a single node, and, if there are a plurality of directed links between identical adjacent events, accumulating weights of the directed links into a single directed link. The pattern extraction apparatus cuts a directed link having an evaluation value smaller than or equal to a predetermined value in the generated adjacent event graph, the expected value being obtained based on the weight of the directed link.

A method for detecting an object includes inputting information of a moving object included in a plurality of images and generating a regression tree. In response to input of a new image, the system communicates information of a moving object included in the newly inputted image into the regression tree, and determines a size of a person included in the new image based on a resultant value of the regression tree.

To improve a search accuracy in template matching based on a coarse-to-fine search method, a first pyramid is generated by hierarchizing a plurality of template images having different resolutions from each other in order of the resolution, a second pyramid is generated by hierarchizing a plurality of search target images having different resolutions from each other corresponding to the same resolutions as the template images in the first pyramid in order of the resolution, and a search process of searching for a feature image included in the template image of the first pyramid from the search target image in the second pyramid in a hierarchy of the same resolution. When the first pyramid is generated, the number of stages of the first pyramid is set such that a size of the feature image included in each of the template images is not smaller than a previously set threshold.

According to an embodiment, a device includes a detector, first and second recognizers, an estimator, a second recognizer, and an output unit. The detector is configured to detect a visible text area including a visible character from an image. The first recognizer is configured to perform character pattern recognition on the visible text area, and calculate a recognition cost according to a likelihood of a character pattern. The estimator is configured to estimate a partially-hidden text area into which a hidden text area estimated to have a hidden character and the visible text area are integrated. The second recognizer is configured to calculate an integrated cost into which the calculated cost and a linguistic cost corresponding to a linguistic likelihood of a text that fits in the entire partially-hidden text area are integrated. The output unit is configured to output a text selected or ranked based on the integrated cost.

A method for efficiently grouping electronic documents that are likely textual near-duplicates includes processing first and second electronic documents to determine respective sets of character sequence counts. The processing may include, for each document, identifying a plurality of non-contiguous character sequences expressed within the document text, with each character sequence including at least one character from each of at least two different words in the text, and determining character sequence counts for each unique character sequence within the identified character sequences. The method also includes generating one or more similarity metrics, at least by comparing the sets of character sequence counts determined for the first and second electronic documents. The method may also include using the similarity metric(s) to calculate a similarity score, and assigning, based on the similarity score, the second electronic document to a same document group as the first electronic document.

A method for converting Punycode text to Unicode text with augmented reality includes the steps of providing a client device, a computer, a scanner, optical code recognition software, Punycode/ASCII and/or Unicode dictionary tables and further a Punycode text. The Punycode text is scanned and saved in the computer and the scanned image is subjected to the optical code recognition software to identify Punycode strings. Those strings are then converted and segregated into Unicode strings.