Abstract: An image processing apparatus includes an obtaining unit, a first classification unit, and a second classification unit. The obtaining unit obtains plural types of document images. The first classification unit classifies each of the plural types of document images obtained by the obtaining unit into any of classification items included in one category among plural categories, the plural categories each including plural classification items. The second classification unit classifies, in a case where there is a document image that is not classified into the one category by the first classification unit among the plural types of document images, the document image, which is not classified into the one category, into another category that includes at least one of classification items into which some of the document images are classified by the first classification unit.

Abstract: An approach is provided in which a knowledge manager locates centroids in a high-dimensional vector space that are closest to a new image feature set and performs nearest neighbor searches on feature sets included in clusters corresponding to the located centroids. The knowledge manager then selects feature sets closest to the new image feature set based on the nearest neighbor searches and in turn, marks images corresponding to the selected closest features sets as similar images to a new image corresponding to the new image feature set.

Abstract: Provided are a system and method for determining a color of a foreground and a computer readable recording medium therefor. The system includes: at least one processor configured to implement a motion detector which detects a motion region from an image; and a color determiner which determines the color of the foreground in the motion region based on a difference between colors of the motion region and colors of a surrounding region of the motion region, for displaying the determined foreground color with the foreground at a display device.

Abstract: A device includes an illumination device that casts light of a prescribed polarization direction on a scattering body, a camera that picks up images of the scattering body at a plurality of different polarization angles, and a processor that executes a process of generating and outputting an inner layer image, of an inner layer of an inside of the scattering body, in response to a depth from a surface of the scattering body on the basis of the images of the scattering body picked up at the plurality of different polarization angles.

Abstract: A method for image processing by: obtaining an original image including a bulleted list; determining a list bounding box enclosing the bulleted list; determining line bounding boxes nested within the list bounding box enclosing lines of the bulleted list; identifying bullets based on the line bounding boxes; identifying indentations based on the bullets; and generating a bulleted list object for the bulleted list in the original image based on the indentations.

Abstract: Provided are a system and method for determining at least one color of a foreground and a computer readable recording medium therefor. The system includes: a motion detector configured to detect a motion region from an image of an area; and a color determiner configured to determine at least one color of the foreground included in the motion region based on a difference between at least one color of the motion region and at least one color of a background which is a region outside the motion region in the image, for displaying the foreground color with the foreground at a display device.

Abstract: Provided is a process including: determining that a mobile computing device has crossed a geofence associated with a merchant store; sending, to a remote classifier server, a request for object-recognition classifiers for objects in the merchant store; receiving a set of object-recognition classifiers; receiving with the mobile computing device from user a request to search for offers; capturing an image with a camera of the mobile computing device; receiving one or more wireless beacon identifiers with the mobile computing device; based on the wireless beacon identifiers, selecting a subset of the object-recognition classifiers in the set of object-recognition classifiers; and recognizing an object in the captured image based on the selected subset of the object-recognition classifiers; and requesting, from a remote offer publisher server, offers corresponding to the recognized object; and receiving offers from the remote offer publisher server; and displaying the received offers to the user.

Abstract: In an example embodiment, method, apparatus and computer program product are provided. The method includes facilitating receipt of a deconvolved image including a plurality of component images. A guide image is selected from the component images and a cross-filtering is performed of component images other than the guide image to generate filtered component images. The cross-filtering is performed of a component image by iteratively performing, selecting a pixel and a set of neighboring pixels around the pixel in the guide image, computing a set of weights corresponding to the set of neighboring pixels based at least on spatial differences between the pixel and the set of neighboring pixels, and cross-filtering a corresponding pixel of the pixel in the component image based on the set of weights to generate a filtered corresponding pixel in the component image. The filtered component images form a filtered deconvolved image with reduced chromatic aberration.

Abstract: A virtual viewpoint synthesis method and system, including: establishing a left viewpoint virtual view and a right viewpoint virtual view; searching for a candidate pixel in a reference view, and marking a pixel block in which the candidate pixel is not found as a hole point; ranking the found candidate pixels according to depth, and successively calculating a foreground coefficient and a background coefficient for performing weighted summation; enlarging the hole-point regions of the left viewpoint virtual view and/or the right viewpoint virtual view in the direction of the background to remove a ghost pixel; performing viewpoint synthesis on the left viewpoint virtual view and the right viewpoint virtual view; and filling the hole-points of a composite image.

Abstract: Disclosed herein are technologies directed to discovering semantic similarities between images and text, which can include performing image search using a textual query, performing text search using an image as a query, and/or generating captions for images using a caption generator. A semantic similarity framework can include a caption generator and can be based on a deep multimodal similar model. The deep multimodal similarity model can receive sentences and determine the relevancy of the sentences based on similarity of text vectors generated for one or more sentences to an image vector generated for an image. The text vectors and the image vector can be mapped in a semantic space, and their relevance can be determined based at least in part on the mapping. The sentence associated with the text vector determined to be the most relevant can be output as a caption for the image.

Abstract: A lane mark recognition device detects an edge located in a proximal area in front of the vehicle, and determines a lane mark candidate on the basis of the detected edge and an edge that is located in a distal area farther than the proximal area in front of the vehicle and that is continuous with the edge. Therefore, an edge of another vehicle (leading vehicle) or the like located in the distal area in a captured image is not detected as a lane mark candidate (is excluded as a non lane mark candidate).

Abstract: Methods, apparatuses and systems may provide for operating a machine learning device by obtaining training image data, conducting an offline prediction analysis of the training image data with respect to one or more real-time parameters of an image capture device, and generating one or more parameter detection models based on the offline prediction analysis. Additionally, methods, apparatuses and systems may provide for operating the image capture device by obtaining a candidate image associated with the image capture device, determining that the candidate image corresponds to a particular type of scene represented in a parameter prediction model, and adjusting one or more real-time parameters of the image capture device based at least in part on one or more parameter values associated with the particular type of scene.

Abstract: A method and system for ghost detection in an image are described. Initially, a mode for detecting ghosting artifact is determined for deciding whether to perform analog or inter field ghosting analysis on the image. Based on the determined mode, a plurality of fields is determined to generate a field overlay image. The image is updated based on the field overlay image and an inter-field average of absolute difference is computed. A significant edge image, a principal edge image, and a delta gradient image are generated. A confidence score indicative of a likelihood of ghosting artifact in the image, is computed based on at least the inter-field average of absolute difference, a first and a second count of pixels in the delta gradient image, a third count of pixels in the principal edge image, and a count of rows of the significant edge image that correspond to the ghosting artifact.

Abstract: An information processing apparatus includes a first acquiring unit, an addition unit, a second acquiring unit, and an extraction unit. The first acquiring unit acquires a first group of elements included in a first image generated by reading a document. The addition unit generates multiple second images by adding noises that differ from each other to the first image. The second acquiring unit acquires second groups of elements included in the respective multiple second images. The extraction unit extracts an element representing characteristics of the document from the first group of elements in accordance with the degrees of similarity between elements included in the first group of elements and elements included in the multiple second groups of elements.

Abstract: The invention describes a method for representing geometry information to utilize for scalable coding of piecewise smooth spatial data sets. The method may also be applicable to vector data such as motion, where this data tends to exhibit piecewise smooth characteristics. The hierarchical geometry representation detailed in this invention is spatially scalable and amenable to embedded quantization and coding techniques. These features enable the geometry representation to be incorporated into highly scalable image coding schemes to attain efficient compression and output bit-streams with embedded resolution and quality scalability.

Abstract: An image processing system includes a processing device having a memory device for storing computer-executable instructions. The processing device is programmed to define a first descriptor in a first image, generate a mask from the first descriptor, and define a second descriptor in a second image. The processing device is further programmed to compare the first descriptor to the second descriptor to define a first error vector, determine a second error vector by applying a mask, and compute the total error of the second error vector to determine an error between the first descriptor and the second descriptor.

Abstract: A face clustering system for video face clustering in a video sequence, the system including an inherent supervision summarization device configured to collect group-level supervision and instance level supervision within a same chunklet based on a user input of face images for a person, a discriminative projection learning device configured to embed group constraints of the group-level supervision into a transformed space, and configured to generate an embedding space from the original image feature space, and a clustering device, in the embedding space, configured to execute pair-wise based clustering to cluster the video images into different clusters with the instance level supervision collected by the inherent supervision summarization device.

Abstract: A multimodal sensing system includes various devices that work together to automatically classify an action. A video camera captures a sequence of digital images. At least one other sensor device captures other sensed data (e.g., motion data). The system will extract video features from the digital images so that each extracted image feature is associated with a time period. It will extract other features from the other sensed data so that each extracted other feature is associated with a time period. The system will fuse a group of the extracted video features and a group of the extracted other features to create a fused feature representation for a time period. It will then analyze the fused feature representation to identify a class, access a data store of classes and actions to identify an action that is associated with the class, and save the identified action to a memory device.

Abstract: An electronic device having a fingerprint verification function is provided. The electronic device includes a display; a touch recognition sensor pattern for recognizing a touch input on the display; a fingerprint recognition sensor pattern for recognizing a fingerprint input on the display; and a processor functionally connected to the display, the touch recognition sensor pattern and the fingerprint recognition sensor pattern. The processor determines whether a fingerprint recognition mode is executed, and selectively activates the touch recognition sensor pattern and the fingerprint recognition sensor pattern according to whether the determined fingerprint recognition mode is executed.

Abstract: A system that identifies attributes of an item depicted in an image using artificial intelligence is provided. For example, the system may use one or more deep belief networks (DBNs) or convolution neural networks (CNNs) trained to analyze images and identify attributes in items depicted in the images. A first artificial intelligence module may analyze an image to determine a type of item depicted in the image. The system may then select a second artificial intelligence module that is associated with the type of item and use the second artificial intelligence module to identify attributes in the item depicted in the image. Identified attributes, if associated with a confidence level over a threshold value, may be provided to a user. The user may provide feedback on the accuracy of the identified attributes, which can be used to further train the first and/or second artificial intelligence modules.