Abstract: A target detection method a is provided, which relates to the fields of deep learning, computer vision, and artificial intelligence. The method comprises: classifying, by using a first classification model, a plurality of image patches comprised in an input image, to obtain one or more candidate image patches, in the plurality of image patches, that are preliminarily classified as comprising a target; extracting a corresponding salience area for each candidate image patch; constructing a corresponding target feature vector for each candidate image patch based on the corresponding salience area for each candidate image patch; and classifying, by using a second classification model, the target feature vector to determine whether each candidate image patch comprises the target.

Abstract: A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

Abstract: A method and system is provided for planogram compliance check based on visual analysis. The present method and system comprises acquiring a product shelf image, identifying a region of interest (ROI) from the at least one product shelf image, extracting a perspective distortion rectification of the identified ROI to generate a rectified shelf image, applying Hausdroff distance based image map for occupancy estimation in shelf images extracting one or more rows from the rectified shelf image wherein the rectified shelf image comprises a plurality of horizontal row partitions, and deriving a product count and placement information for each of the extracted one or more rows based on the extracted rows and the occupancy estimation.

Abstract: Techniques and systems for recognizing characters on a circuit board are discussed herein. For example, a digital image of a character on a circuit board can be obtained. The digital image can be processed with a gradient, structural, and concavity algorithm. The processing can include identifying a gradient feature based on a stroke shape, identifying a structural feature based on a stroke trajectory, identifying a concavity feature based on a stroke relationship, and so on. The character can be classified based on the gradient feature, the structural feature, and/or the concavity feature. The classifying can be performed using a k-nearest neighbor classifier algorithm and/or a distance metric.

Abstract: A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

Abstract: A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

Abstract: In the case where a user extracts a desired character string by specifying a range by using a finger or the like of him/herself on an image including a character, a specific character (space or the like) located at a position adjacent to the desired character string is prevented from being included unintendedly in the selected range. The character area corresponding to each character included in the image is identified and character recognition processing is performed for each of the identified character areas. Then, from results of the character recognition processing, a specific character is determined and the character area corresponding to the determined specific character is extended. Then, the range selected by the user in the displayed image is acquired and character recognition results corresponding to a plurality of character areas included in the selected range are output.

Abstract: A method, system and computer program product for enhancing customer's satisfaction with his/her shopping experience. Facial motion measurements defined in a model for facial expression recognition are utilized. Features from a magnitude of the facial motion measurements defined in the model are identified. A Bayes classifier is utilized to capture dependences among the features used to recommend a better sales approach to the sales professional, or, alternatively, assign a better sales professional to approach the customer thereby enhancing the customer's shopping experience by reducing the possibility of an unpleasant shopping experience.

Abstract: A method for object classification in a decision tree based adaptive boosting (AdaBoost) classifier implemented on a single-instruction multiple-data (SIMD) processor is provided that includes receiving feature vectors extracted from N consecutive window positions in an image in a memory coupled to the SIMD processor and evaluating the N consecutive window positions concurrently by the AdaBoost classifier using the feature vectors and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions.

Abstract: A system, comprising an input device configured to receive a first item and a second item, and a processor communicably coupled to the input device and configured to determine that the first item is a fragment matching a lexicon, and place the fragment in a section of a document, the section selected based on the matching lexicon, wherein the processor is configured to perform the determination and the placement after it receives the first item but before it receives the second item.

Abstract: An information processing apparatus includes a detecting unit, an extracting unit, a managing unit, and a deriving unit. The detecting unit detects an object from an image. The extracting unit extracts a feature amount of the object detected by the detecting unit. The managing unit manages an element in the object detected by the detecting unit in association with the feature amount of the object in the element. The deriving unit derives a reliability indicative of a certainty for identifying an object with respect to the feature amount of the element.

Abstract: A face tracking system generates a model for extracting a set of facial anchor points on a face within a portion of a face image based a multiple-level cascade of decision trees. The face tracking system identifies a mesh shape adjusted to an image of a face. For each decision tree, the face tracking system identifies an adjustment vector for the mesh shape relative to the image of the face. For each cascade level, the face tracking system combines the identified adjustment for each decision tree to determine a combined adjustment vector for the cascade level. The face tracking system modifies adjustment of the mesh shape to the face in the image based on the combined adjustment vector. The face tracking system reduces the model to a dictionary and atom weights using a learned dictionary. The model may be more easily transmitted to devices and stored on devices.

Abstract: An analysis and visualization system analyzes a digital image of a tissue sample. In the sample, cells of a first type are stained in a first way, and cells of a second type are stained in a second way. The system segments the high-resolution image into first and second objects representing cells of the first and second types, respectively. The system also identifies a region of interest, and divides it into tiles. The system generates, for each tile, a first value and a second value. The first and second values for a tile are indicative of densities of the first and second objects in the tile. From the values, a measured correlation coefficient (CC) value is determined. The system compares the measured CC value to a reference CC value, thereby obtaining a correspondence value. The system then displays the image region along with a visualization of the correspondence value.

Abstract: A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

Abstract: According to an illustrative embodiment an information processing system is provided. The system includes a processor for determining one or more candidate tags based on input data, the candidate tags being included within a hierarchical structure; and a display for displaying the candidate tags in a manner indicative of the candidate tags' positions in the hierarchical structure.

Abstract: A system, method, and computer program product are provided where non-convertible lines in a database that that are comprised of characters that are not recognized by a codepage of the database are identified as result of a predefined assessment and are then exported from the database. The exported non-convertible lines are automatically converted to convertible lines by using a mapping table, and the non-convertible lines are deleted from the database. After deleting the non-convertible lines, a predefined conversion command is executed on the database, thereby converting all characters of the database from an ISO format to a UTF8 format, and then the convertible lines are imported into the database utilizing a predefined import utility that automatically converts all characters of the convertible lines, once inserted into the database, from the ISO format to the UTF8 format.

Abstract: An exemplary method for surfacing feature changes occurring over time at a location is provided. The method includes identifying sets of images captured at a location over a period of time. The sets of images depict objects at the location. Visual changes at the location over the period of time are determined for a given physical feature of the objects depicted in images from the sets of images based on at least a comparison of features in corresponding areas of a first and a second 3D geometry of the location associated with the images. The first and second 3D geometries depict physical features of objects at the location at different times. The sets of images may be ranked based on the determined visual changes for each set. A given set of images from the sets of images may be selected for display on a user interface based on the ranking.

Abstract: Embodiments are directed towards protecting against polymorphic cheat codes in a video game environment. A detour analyzer analyzes game code in client memory for possible hooks to parasite code. For each detected hook to parasite code, hook and/or parasite information is determined to generate a hook/parasite signatures, which are sent to a remote network device. Based on the hook/parasite signatures a weighted combination of scores are generated that is useable to determine a probability value that the parasite code is cheat code. If the determined probability value indicates cheat code, the user of the client device may be banned from future game play. Additionally, the hook/parasite signature information may be used to update the data store to detect polymorphic changes in the cheat code.

Abstract: Methods and systems are provided for sharing a digital image depicting one or more faces. The method may include linking a plurality of computer terminals to a computer network, each computer terminal associated with an individual; receiving a digital image at at least one of the computer terminals; executing a face recognition routine on the digital image, the face recognition routine detecting at least one face in the digital image, each detected face corresponding to a person, the face recognition routine recognizing at least one of the persons as being one of the individuals; and for each individual recognized in the digital image by the face recognition routine, initiating dissemination of the digital image to the computer terminal associated with respective individual whose face is recognized in the digital image.

Abstract: Provided are an age estimation apparatus, an age estimation method, and an age estimation program capable of obtaining a recognition result closely matching the result perceived by human. An age estimation apparatus 10 for estimating an age of a person on image data includes a dimension compressor 11 for applying dimension compression to the image data to output low dimensional data; and an identification device 12 for estimating an age of a person on the basis of a learning result using a feature amount contained in the low dimensional data, wherein a parameter used for the dimension compression by the dimension compressor 11 and the feature amount used for age estimation by the identification device 12 are set on the basis of a result of an evaluation of a generalization capability using a weighting function that shows a degree of seriousness of an age estimation error for every age, and learning of the identification device 12 is performed on the basis of the weighting function.

Abstract: A method for object detection and an apparatus using the same are provided, and the method includes: An image is captured, in which the image includes a plurality of sampling-windows. A first-stage sub-classifier of a classifier is used to detect whether the sampling-windows contain an object therein. The classifier is rotated at least one time by a predetermined rotation angle and the first-stage sub-classifier of the classifier is used to detect whether the sampling-windows contain the object after each rotating, wherein when the object is detected within the sampling-windows, keep detecting whether the sampling-windows contain the object therein sequentially by a second-stage sub-classifier to an Nth stage sub-classifier of the classifier with the same orientation. The image is rotated at least one time by a predetermined image angle and the above-mentioned operations of detecting the object is performed after each rotating. The sampling-windows containing the object are output.

Abstract: Certain embodiments enable image-based stimulus for circuit simulations by extracting a waveform from an image and using that waveform to simulate a circuit. Image-processing aspects may include edge-detection processes to identify a boundary of the waveform in the image.

Abstract: The subject disclosure is directed towards a face detection technology in which image data is classified as being a non-face image or a face image. Image data is processed into an image pyramid. Features, comprising pixel pairs of the image pyramid, are provided to stages of a cascading classifier to remove sub-window candidates that are classified as non-face sub-windows within each stage. The face detection technology continues with one or more subsequent stages to output a result as to whether the image contains a face.

Abstract: A method, system, and computer program product for detecting objects. Members of a general class of objects are searched for in a number of images. Members of a specific class of objects are searched for in a number of regions in the number of images where the number of regions contains at least a portion of the members of the general class. A member in the members of the specific class is a potential threat to a rotorcraft.

Abstract: A method for recognition of a predetermined pattern in an image data set recorded by a device for recording of at least two electromagnetic frequency spectra is provided. A first difference value is formed for the image points of the selected area as a function of a difference between a data vector of a corresponding image point and a first reference data vector. A second difference value is formed for an image point of a selected area as a function of a difference between the data vector of this image point and a second reference data vector. A predetermined pattern is recognized when it is determined at least one pattern correlation quantity is below a predetermined threshold value and a local minimum is present.

Abstract: An image processor comprises first and second hardware accelerators and is configured to implement a classifier. The classifier in some embodiments comprises a cascaded classifier having a plurality of stages with each such stage implementing a plurality of decision trees. At least one of the first and second hardware accelerators of the image processor is configured to generate an integral image based on a given input image, and the second hardware accelerator is configured to process image patches of the integral image through one or more of a plurality of decision trees of the classifier implemented by the image processor. By way of example, the first and second hardware accelerators illustratively comprise respective front-end and back-end accelerators of the image processor, and an integral image calculator configured to generate the integral image based on the given input image is implemented in one of the front-end accelerator and the back-end accelerator.

Abstract: A screen image displaying image-data items in list form is displayed over a predetermined time period. Then, the display color of an image-data item of the image-data items, the image-data item corresponding to predetermined classification information, is changed so that the image-data item can be differentiated from other image-data items. Consequently, a user can clearly identify the classification of the image-data items and easily find an image-data item satisfying a condition without performing a complicated operation.

Abstract: A method for transferring volumetric image data from a server to at least one client comprising: transforming said volumetric images into a hierarchical representation comprising a plurality of coefficients, said hierarchical representation comprising a plurality of levels of essentially non-redundant data, wherein a level of said hierarchical representation comprises transform data sufficient to reconstruct said images at a resolution corresponding to said level; partitioning said coefficients into a plurality of voxels, each voxel comprising “n” coefficients in a horizontal direction, “m” coefficients in a vertical direction, and “P” coefficients in a depth direction; requesting, from a client to a server, transform data in the form of voxels from one or more levels of said hierarchical representation necessary to reconstruct at least a portion of said source volume; transferring, from a client to a server, a request for coefficients of at least a portion of said volumetric image; transferring, from said se

Abstract: A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The image capturing device has a field of view and produces image data representing an image of the field of view. The object classification module is operable to determine whether an object in an image is a member of an object class. The object classification module includes N decision steps configured in a cascade configuration, wherein at least one of the N decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether an object is a member of the object class.

Abstract: An image identifying device includes: a setting unit which sets a section having at least one image in a video; a first recognizing unit which calculates a plurality of feature amounts related to at least the one image and which acquires a plurality of identification results corresponding to each of the feature amounts from an identifier which may identify a plurality of objects belonging to a first category; a selecting unit which selects, based on the identification results, a second category of a third category; and a second recognizing unit which calculates another feature amount related to an image included in another section and acquires another identification result corresponding to the feature amount from another identifier which may identify the objects included in the second category.

Abstract: An image identifying device includes: a setting unit which sets a section having at least one image in a video; a first recognizing unit which calculates a plurality of feature amounts related to at least the one image and which acquires a plurality of identification results corresponding to each of the feature amounts from an identifier which may identify a plurality of objects belonging to a first category; a selecting unit which selects, based on the identification results, a second category of a third category; and a second recognizing unit which calculates another feature amount related to an image included in another section and acquires another identification result corresponding to the feature amount from another identifier which may identify the objects included in the second category.

Abstract: An apparatus and method are provided for recognizing an emotion of an individual based on Action Units. The method includes receiving an input AU string including one or more AUs that represents a facial expression of an individual from an AU detector; matching the input AU string with each of a plurality of AU strings, wherein each of the plurality of AU strings includes a set of highly discriminative AUs, each representing an emotion; identifying an AU string from the plurality of AU strings that best matches the input AU string; and outputting an emotion label corresponding to the best matching AU string that indicates the emotion of the individual.

Abstract: An image acquisition device having a wide field of view includes a lens and image sensor configured to capture an original wide field of view (WFoV) image with a field of view of more than 90°. The device has an object detection engine that includes one or more cascades of object classifiers, e.g., face classifiers. A WFoV correction engine may apply rectilinear and/or cylindrical projections to pixels of the WFoV image, and/or non-linear, rectilinear and/or cylindrical lens elements or lens portions serve to prevent and/or correct distortion within the original WFoV image. One or more objects located within the original and/or distortion-corrected WFoV image is/are detectable by the object detection engine upon application of the one or more cascades of object classifiers.

Abstract: A sort-out cycle such as a judgment of three levels of “usable”, “unusable”, and “reserve” is made to images and, when the judgment of all images is completed, the judgment of the three levels is made again to the “reserve” images is repeated. The number of sort-out cycle times in which the judgment is made is applied as an evaluation to the images which are finally determined as “usable”.

Abstract: Provided are an age estimation apparatus, an age estimation method, and an age estimation program capable of reducing the labor of labeling the image data used for age estimation. An age estimation apparatus for estimating an age of a person on image data includes a dimension compression unit for applying dimension compression to the image data to output low dimensional data; a clustering unit for performing clustering of the low dimensional data outputted; a labeling unit for labeling representative data of each cluster among the low dimensional data clustered; and an identification unit for estimating an age of a person on the basis of a learning result using a feature amount contained in labeled low dimensional data and unlabeled low dimensional data.

Abstract: The described implementations relate to computer vision. In one case image data is received. Resource constraints associated with processing the image data are evaluated. Further, a visual recognition and detection processing strategy is selected for the image data based at least in part on the evaluated resource constraints.

Abstract: A method of processing digital images by transforming a set of pixels from a three-dimensional space to a normalized two-dimensional space, determining a membership class and membership class level of each pixel in the set of pixels, and selectively modifying colors of pixels in the set of pixels based on the determined membership classes and membership class levels.

Abstract: A method of identifying an object captured in a video image in a multi-camera video surveillance system is disclosed. Sets of identifying information are stored in profiles, each profile being associated with one object. The disclosed method of identifying an object includes comparing identifying information extracted from images captured by the video surveillance system to one or more stored profiles. A confidence score is calculated for each comparison and used to determine a best match between the extracted set of identifying information and an object. In one embodiment, the method is used as part of a facial recognition system incorporated into a video surveillance system.

Abstract: There is provided an image processing apparatus that analyzes a pathology image obtained by imaging an analysis object including a setting device for calculating an amount of a luminance difference characteristic as an amount of characteristic of each pixel of a pathology image, and for setting a cellular tissue region in the pathology image, based on the amount of the luminance difference characteristic, using a cellular tissue detector that has studied in advance. The cellular tissue detector may have studied in advance by statistical learning that uses an image corresponding to a cellular tissue.

Abstract: Methods for recognizing a category of an object are disclosed. In one embodiment, a method includes determining, by a processor, a preliminary category of a target object, the preliminary category having a confidence score associated therewith, and comparing the confidence score to a learning threshold. If the highest confidence score is less than the learning threshold, the method further includes estimating properties of the target object and generating a property score for one or more estimated properties, and searching a supplemental image collection for supplemental image data using the preliminary category and the one or more estimated properties. Robots programmed to recognize a category of an object by use of supplemental image data are also disclosed.

Abstract: An approach that provides hierarchical and index based watermarks represented as trees is described. In one embodiment, a watermark tree is formed from feature watermarks generated from a natural language processing (NLP) stack having NLP analytics. The watermark tree represents a hierarchical relationship between each of the feature watermarks. In particular, the watermark tree defines hierarchical pointers that point out inherited watermarks that exist between the feature watermarks according to the hierarchical relationship. Further, the watermark tree includes a time stamp specifying a time that a data set content residing in a corpus was accessed.

Abstract: A method of converting a raster scan image to a vector-based image including (a) reading a scanline of a raster scan image containing n pixels per scanline; (b) classifying n+1 groupings of four adjacent pixels within the scanline using only the four adjacent pixel values; (c) assigning to each grouping (x,y), for all groupings in the scanline, plural vectors based on (1) the classification of the grouping (x,y) and (2) vectors assigned to a grouping (x?1,y) and vectors assigned to a grouping (x, y?1); (d) adding a point (x,y) to at least one vector assigned to the grouping (x,y) when the classification of grouping (x,y) indicates that the point (x,y) corresponds to a corner; (e) determining whether the points added to any vector form a closed loop; (f) writing out the points added to any vector that form a closed loop; and (g) repeating steps (a)-(f) for all of the scanlines of the raster scan image.

Abstract: Methods and apparatuses for constructing a multi-level solver, comprising decomposing a graph into a plurality of pieces, wherein each of the pieces has a plurality of edges and a plurality of interface nodes, and wherein the interface nodes in the graph are fewer in number than the edges in the graph; producing a local preconditioner for each of the pieces; and aggregating the local preconditioners to form a global preconditioner.

Abstract: An apparatus for providing multi-feature based sampling for relevance feedback may include a user interface element. The user interface element may be configured to receive a query image, provide a first set of images determined to be relevant with respect to the query image, and provide a second set of images in which each image of the second set of images comprises a first relevance determination with respect to a first feature of an image and a second relevance determination for at least a second feature of the image. Each image of at least the second set of images may include an associated user interface item to enable a user to provide relevance feedback with respect to each image of the second set of images.

Abstract: A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.

Abstract: A method and apparatus for detecting an object are provided. The method calculates a feature value based on features extracted from an input image by using a classifier of an ith stage, compares the calculated feature value with a first threshold value of the ith stage, compares the feature value with a second threshold value of the ith stage according to a result of the comparison between the feature value and the first threshold value of the ith stage, and jumps to a classifier of an i+2th stage according to a result of the comparison between the feature value and the second threshold value of the ith stage. By doing so, the method can avoid repeated calculations and can rapidly detect the object in the input image.

Abstract: Recognition target area images are sequentially clipped out from an input image. Each of the clipped-out recognition target area images is recognized as or not as an image related to an object by referring to each of a plurality of dictionary data items stored in advance for recognizing the object. Based on the recognition result for a recognition target area image, the order in which each of the plurality of dictionary data items is referred to for a partial image in the neighborhood of the recognition target area image is determined. Thus, the time required for recognizing the object is reduced.

Abstract: Techniques and structures are disclosed in which one or more distortion categories are identified for an image or video, and a quality of the image or video is determined based on the one or more distortion categories. The image or video may be of a natural scene, and may be of unknown provenance. Identifying a distortion category and/or determining a quality may be performed without any corresponding reference (e.g., undistorted) image or video. Identifying a distortion category may be performed using a distortion classifier. Quality may be determined with respect to a plurality of human opinion scores that correspond to a particular distortion category to which an image or video of unknown provenance is identified as belonging. Various statistical methods may be used in performing said identifying and said determining, including use of generalized Gaussian distribution density models and natural scene statistics.

Abstract: In one embodiment, there is provided a method for an Optical Character Recognition (OCR) system. The method comprises: recognizing an input character based on a plurality of classifiers, wherein each classifier generates an output by comparing the input character with a plurality of trained patterns; grouping the plurality of classifiers based on a classifier grouping criterion; and combining the output of each of the plurality of classifiers based on the grouping.

Abstract: Embodiments include a scene classification system and method. In one embodiment, a method includes forming a first plurality of image features from an input image, processing the first plurality of image features in the first scene classifier.