Abstract: An image processing apparatus includes a control unit. The control unit performs OCR processing by identifying a specified area of image data as a detection area, detecting line images contained in the detection area, and analyzing the detected line images to identify a character represented by the line images. Furthermore, when failing to identify a character represented by the line images in the OCR processing, the control unit determines whether the line images represent a part of a character and, upon determination that the line images represent a part of a character, expands the detection area in a direction where the other part of the character to be identified is assumed to be present and performs the OCR processing again on the expanded detection area.

Abstract: Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Abstract: An image capturing apparatus including a substrate, a light source, a sensor, a light shielding element, a first reflective element, and a transparent colloid curing layer is provided. The light source, the sensor, the light shielding element, the first reflective element, and the transparent colloid curing layer are disposed on the substrate. The sensor is located next to the light source. The light shielding element is located between the light source and the sensor. The first reflective element is located between the light shielding element and the sensor. The transparent colloid curing layer covers the light source, the sensor, the light shielding element, and the first reflective element. A manufacturing method of the image capturing apparatus is also provided.

Abstract: At least one image processing apparatus, and at least one method, of the present invention(s) generate a corrected image obtained by removing, from a color image whose pixel values contain components derived from scattered light, at least part of the components derived from the scattered light. The at least one image processing apparatus includes a generation unit configured to generate the corrected image by correcting a pixel value of a first color component of each pixel in the color image by using a first reference intensity and a weight value, and by correcting a pixel value of a second color component of each pixel in the color image by using a second reference intensity and a weight value.

Abstract: Apparatus, methods and systems of object recognition are disclosed. Embodiments of the inventive subject matter generates map-altered image data according to an object-specific metric map, derives a metric-based descriptor set by executing an image analysis algorithm on the map-altered image data, and retrieves digital content associated with a target object as a function of the metric-based descriptor set.

Abstract: A luminance variation (dI) pertaining to each pixel is calculated (21) using a plurality of captured images obtained by image capturing under different illumination conditions, a texture variation (dF) pertaining to each pixel is calculated (22) using a plurality of captured images obtained by image capturing at different time points, and a subject region is extracted (23) based on the luminance variation (dI) and the texture variation (dF). A variation in the texture feature (F) pertaining to each pixel between the images is calculated as the texture variation (dF). The subject can be extracted with a high accuracy even when there are changes in the ambient light or the background.

Abstract: A method for detecting an object in an image includes extracting a first feature vector from a first region of an image using a first subnetwork, determining a second region of the image by resizing the first region into a fixed ratio using a second subnetwork, wherein a size of the first region is smaller than a size of the second region, extracting a second feature vector from the second region of the image using the second subnetwork, classifying a class of the object using a third subnetwork on a basis of the first feature vector and the second feature vector, and determining the class of object in the first region according to a result of the classification, wherein the first subnetwork, the second subnetwork, and the third subnetwork form a neural network, wherein steps of the method are performed by a processor.

Abstract: A computer-implemented method includes generating a metric for a first user that reflects preferences for image attributes, determining image attributes for a first set of images associated with a second user, selecting a subset of the first set of images for the first user based on the metric and the image attributes for the first set of images, and providing the subset of the first set of images to the first user.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for improving quality of location-related content, such as truncated location-related content, for example, while preserving and/or maintaining user privacy.

Abstract: An image analyzing apparatus reprojects an input image in a plurality of different directions to divide the input image into a plurality of partial images, extracts a feature amount from each of the partial images, and calculates a degree of importance of the input image by position from the extracted feature amount in accordance with a predetermined regression model.

Abstract: Comparing extracted card data from a continuous scan comprises an optical character recognition (“OCR”) system for extracted data based on three-dimensional models. The system receives a digital scan of a physical card and obtains a plurality of images of the card from the digital scan of the physical card. The system performs an OCR algorithm on a three-dimensional model based on the images and determines if a confidence level of the results are above a preconfigured level. If the results are below the preconfigured levels, a second three dimensional model is created that includes additional received images. When results are over the preconfigured level, the results are accepted as an accurate extraction.

Abstract: An image recognition apparatus 100 includes a gradient feature computation unit 120 configured to calculate, from an image divided into a plurality of blocks, gradient feature values for each of the plurality of blocks, a combination pattern storage unit 160 configured to store a plurality of combination patterns of the gradient feature values, and a co-occurrence feature computation unit 131 configured to calculate a co-occurrence feature value in a plurality of blocks for each of the plurality of combination patterns. Further, image recognition apparatus 100 includes an arithmetic computation unit 132 configured to calculate an addition value by adding the co-occurrence feature value calculated for each of the plurality of blocks for each of the plurality of combination patterns, a statistical data generation unit 140 configured to generate statistical data from the addition value.

Abstract: An approach to joint acoustic and visual processing associates images with corresponding audio signals, for example, for the retrievals of images according to voice queries. A set of paired images and audio signals are processed without requiring transcription, segmentation, or annotation of either the images or the audio. This processing of the paired images and audio is used to determine parameters of an image processor and an audio processor, with the outputs of these processors being comparable to determine a similarity across acoustic and visual modalities. In some implementations, the image processor and the audio processor make use of deep neural networks. Further embodiments associate parts of images with corresponding parts of audio signals.

Abstract: An image processing method according to an embodiment includes an image acquisition unit, a calculation unit, a region acquisition unit and an estimation unit. The image acquisition unit acquires a target image. The calculation unit calculates a density distribution of targets included in the target image. The estimation unit estimates the density distribution in a first region in the target image based on the density distribution in a surrounding region of the first region in the target image.

Abstract: There is provided with an information processing apparatus. A processing unit obtains output data by inputting training data to a recognition unit. A determination unit determines an error in a discrimination result for the training data obtained by inputting the output data to a plurality of discriminators. A first training unit trains the recognition unit based on the error in the discrimination result.

Abstract: A method and system for detecting occupancy in a space use computer vision techniques. In one embodiment an object is detected in an image of the space. If the object is detected in a first area of the image, a shape of the object is determined based on a first shape feature of the object and if the object is detected in a second area of the image, the shape of the object is determined based on a second shape feature of the object. The object may be determined to be an occupant based on the determined shape of the object.

Abstract: A classification system is described which may include neural network decomposition logic (“NND”), which may perform classification using a neural network (“NN”). The NND may decompose a classification decision into multiple sub-decision spaces. The NND may perform classification using an NN that has fewer neurons than the NND utilizes for classification and/or which accepts feature vectors of a smaller size than are input into the NND. The NND may maintain multiple contexts for sub-decision spaces, and may switch between these in order to perform classification using the sub-decision spaces. The NND may combine results from the sub-decision spaces to decide a classification. By diving the decision into sub-decision spaces, the NND may provide for classification decisions using NNs that might otherwise be unsuitable for a particular classification decisions. Other embodiments may be described and/or claimed.

Abstract: Techniques related to performing skin detection in an image are discussed. Such techniques may include generating skin and non-skin models based on a skin dominant region and another region, respectively, of the image and classifying individual pixels of the image via a discriminative skin likelihood function based on the skin model and the non-skin model.

Abstract: An analog circuit fault mode classification method comprises the following implementation steps: (1) collecting M groups of voltage signal sample vectors Vij to each of fault modes Fi of the analog circuit by using a data collection board; (2) sequentially extracting fault characteristic vectors VijF of the voltage signal sample vectors Vij by using subspace projection; (3) standardizing the extracted fault characteristic vectors VijF to obtain standardized fault characteristic vectors; (4) constructing a fault mode classifier based on a support vector machine, inputting the standardized fault characteristic vectors, performing learning and training on the classifier, and determining structure parameters of the classifier; and (5) completing determination of fault modes according to fault mode determination rules. The fault mode classifier of the present invention is simple in learning and training and reliable in mode classification accuracy.

Abstract: An image is passed through an image identifier to identify a coarse category for the image and a bounding box for a categorized object. A mask is used to identify the portion of the image that represents the object. Given the foreground mask, the convex hull of the mask is located and an aligned rectangle of minimum area that encloses the hull is fitted. The aligned bounding box is rotated and scaled, so that the foreground object is roughly moved to a standard orientation and size (referred to as calibrated). The calibrated image is used as an input to a fine-grained categorization module, which determines the fine category within the coarse category for the input image.

Abstract: An embodiment of the invention provides a method for finding missing persons by learning features for person attribute classification based on deep learning. A first component of a neural network identifies geographic locations of training images; and, a second component of the neural network identifies weather information for each of the identified geographic locations. A third component of the neural network generates image pairs from the training images. For each image pair of the image pairs, the third component of the neural network determines whether images of the image pair include the same person. The neural network generates neural network parameters with the identified geographic locations, the weather information for each of the identified geographic locations, and the determination of whether the images of the image pairs include the same person.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Abstract: An image processing apparatus for storing, in an intermediate data memory, intermediate data generated from print data, and processing the intermediate data is provided. The image processing apparatus has a configuration in which, in a case where it is determined that a size of a work memory exceeds a block size after processing for creating a first bit map is started, and where the processing for creating the first bit map is switched to processing for creating a second bit map, when the processing for creating the first bit map is started, processing for creating a bit map is started without delay by using a bit map memory in advance from the work memory.

Abstract: Provided is a method by which an image forming apparatus forms an image, the method including detecting a boundary area in a portion of image data; determining a direction of the boundary area, a dominant color of the boundary area, and an edge intensity of the boundary area; determining enhancement information with respect to the boundary area based on the direction, the dominant color, and the edge intensity; and forming an image with respect to the image data based on the determined enhancement information.

Abstract: The present invention relates to a method for connecting recyclable logistics apparatus, the recyclable logistics apparatus comprises a wireless beacon unit which periodically transmits a broadcast frame, and the method comprising the following steps: providing a plurality of contact points inside each recyclable logistics apparatus, making a connection between the contact points of each recyclable logistics apparatus, and implanting wires inside each recyclable logistics apparatus for connecting the wire beacon unit and the contact points; and connecting a plurality of recyclable logistics apparatuses in a predetermined manner, so that when a plurality of recyclable logistics apparatuses are stacked as a stack, a parallel circuit is formed in part region inside the stack of recyclable logistics apparatuses. Through this technical solution, the subsequent work efficiency is doubled, the time is greatly saved and the workload is reduced.

Abstract: An active RFID tag includes a first substrate, an electrically conductive layer disposed on the first substrate and etched or printed to form an antenna and a circuit, a thin film photovoltaic cell installed on the electrically conductive layer and electrically coupled to the electrically conductive layer, a thin film energy storage device installed on the electrically conductive layer and electrically coupled to the circuit of the electrically conductive layer, and an RFID chip installed on the electrically conductive layer and electrically coupled to the electrically conductive layer.

Abstract: In order to provide a document of value or security document 1000 having an electronic circuit 1270 with increased mechanical stability, in particular to bending load, it is proposed to form the document of value or security document 1000 from at least two document layers 1100, 1200, 1300, 1400 arranged in a stack and connected to one another by means of a joining process, wherein the stack is formed by a supporting structure layer 1100 and a circuit carrier layer 1230 carrying the electronic circuit 1270. The supporting structure layer 1100 is formed from a fibre composite material.

Abstract: A strip-type substrate includes a foil having a number of substrate units for producing chip card modules. The substrate has an inner face for at least partial direct or indirect contacting of a semiconductor chip and an outer face lying opposite the inner face. The foil includes of steel, in particular high-grade steel, and a first layer of nickel or a nickel alloy on at least some sections of the outer face.

Abstract: This invention is a comprehensive “Dynamic Security Code” (“DSC”) System (“DSC System”) that can change the security code of a prepaid, debit, or credit card (“Payment Card”). In an effort to thwart Card-Not-Present (“CNP”) fraud, the DSC System provides dynamic security code values (“DSC Values”) that have a limited use. The DSC Values provided by this DSC System can be calculated by various methodologies and can be used within existing standard payment card infrastructures. The DSC System can also be used with other form factors and in other environments not related to payments such as balance inquiries. The DSC Values can be calculated by a DSC Generator Server or on the card itself.

Abstract: A multilayer body with a functional layer which includes an antenna element as well as with an optical security element which includes at least one electrically conductive partial region which is galvanically connected to the antenna element. A security document with such a multilayer body, as well as a method for the authentication thereof.

Abstract: An RF tag that includes an inlay (made of an IC chip and an antenna); an auxiliary antenna laminated on the inlay in an insulated state; and a housing that houses the inlay having the auxiliary antenna laminated thereon. Furthermore, the antenna of the inlay forms a loop circuit adjacent to the IC chip, and the auxiliary antenna overlaps part of the loop circuit and is arranged along a longitudinal direction of the inlay such that at least a part of the antenna of the inlay is exposed.

Abstract: A method for assigning a personalized aesthetic score to an image is provided. The method includes providing a base neural network for generating learned features. The base neural network is trained on a first set of training images and the base neural network includes two or more layers comprising one or more initial layers and one or more final layers. The method further includes receiving a second set of training images and updating the base neural network to generate a personalized neural network based on the received second set of training images. Updating the base neural network comprises re-training the final layers of the base neural network with the second set of images and keeping the initial layers of the base neural network, such that the personalized neural network includes two or more layers comprising one or more initial layers and one or more final layers.

Abstract: A processing system that processes parameters of a plurality of artificial neurons and artificial synapses constituting a neural network, the processing system including: a storing unit storing definition information defining a state of a control target for each artificial neuron of the plurality of artificial neurons; a processing unit processing parameter values of each artificial neuron of the plurality of artificial neurons and parameter values of one or more artificial synapses connected to inputs of each artificial neuron using a data access structure accessible data unit by data unit, the data unit being collective for each artificial neuron; and an operation determining unit determining operation of the control target based on: an activation state of at least some artificial neurons of the plurality of artificial neurons specified by parameter values of the at least some artificial neurons; and a state defined by the at least some artificial neurons.

Abstract: A tunable resistance device and methods of forming the same include a magnetic fixed layer having a fixed magnetization, a magnetic free layer, and a non-magnetic conductive layer directly between the magnetic fixed layer and the magnetic free layer. The magnetic fixed layer, the magnetic free layer, and the non-magnetic conductive layer are formed in a lattice of wires, with each wire in the lattice being formed from a stack of the magnetic fixed layer, the magnetic free layer, and the non-magnetic conductive layer.

Abstract: An imaging device connected to a neural network is provided. An imaging device having a neuron in a neural network includes a plurality of first pixels, a first circuit, a second circuit, and a third circuit. Each of the plurality of first pixels includes a photoelectric conversion element. The plurality of first pixels is electrically connected to the first circuit. The first circuit is electrically connected to the second circuit. The second circuit is electrically connected to the third circuit. Each of the plurality of first pixels generates an input signal of the neuron. The first circuit, the second circuit, and the third circuit function as the neuron. The third circuit includes an interface connected to the neural network.

Abstract: Methods and systems for training a neural network include identifying weights in a neural network between a final hidden neuron layer and an output neuron layer that correspond to state matches between a neuron of the final hidden neuron layer and a respective neuron of the output neuron layer. The identified weights are initialized to a predetermined non-zero value and initializing other weights between the final hidden neuron layer and the output neuron layer to zero. The neural network is trained based on a training corpus after initialization.

Abstract: A system for training a neural network includes a first set of neural network units and a second set of neural networking units. Each neural network unit in the first set is configured to compute parameter update data for one of a plurality of instances of a first portion of the neural network. Each neural network unit in the first set includes a communication interface for communicating its parameter update data for combination with parameter update data from another neural network unit in the first set. Each neural network unit in the second set is configured to compute parameter update data for one of a plurality of instances of a second portion of the neural network. Each neural network unit in the second set includes a communication interface for communicating its parameter update data for combination with parameter update data from another neural network unit in the second set.

Abstract: Methods and systems for detecting emission sites include identifying a set of known emitters having visible features and a spectroscopic signature that correspond to sites that emit a substance to form a training set. A classifier is generated based on the training set. New emitters are identified based on the classifier, a spectroscopic signature map, and a map of visible features. An alert is provided responsive to the identification of a new emitter.

Abstract: In an example, a circuit of a neural network implemented in an integrated circuit (IC) includes a layer of hardware neurons, the layer including a plurality of inputs, a plurality of outputs, a plurality of weights, and a plurality of threshold values, each of the hardware neurons including: a logic circuit having inputs that receive first logic signals from at least a portion of the plurality of inputs and outputs that supply second logic signals corresponding to an exclusive NOR (XNOR) of the first logic signals and at least a portion of the plurality of weights; a counter circuit having inputs that receive the second logic signals and an output that supplies a count signal indicative of the number of the second logic signals having a predefined logic state; and a compare circuit having an input that receives the count signal and an output that supplies a logic signal having a logic state indicative of a comparison between the count signal and a threshold value of the plurality of threshold values; wherein

Abstract: A method of updating a classifier on-the-fly is provided. The method includes providing a base classifier. The base classifier is a neural network. The method further includes receiving a class and a set of images associated with the class. The method further includes splitting the set of images into an evaluation set and a training set. The method further includes updating the base classifier on-the-fly to provide an updated classifier. Updating the base classifier includes (1) extracting features for each image from the training set from the base classifier; (2) training the updated classifier using the extracted features; and (3) scoring the evaluation set with the updated classifier.

Abstract: A method for training a neural network of a neural network based speaker classifier for use in speaker change detection. The method comprises: a) preprocessing input speech data; b) extracting a plurality of feature frames from the preprocessed input speech data; c) normalizing the extracted feature frames of each speaker within the preprocessed input speech data with each speaker's mean and variance; d) concatenating the normalized feature frames to form overlapped longer frames having a frame length and a hop size; e) inputting the overlapped longer frames to the neural network based speaker classifier; and f) training the neural network through forward-backward propagation.

Abstract: Systems and methods identify and provide interesting facts about an entity. An example method includes selecting documents associated with at least one unique fact trigger, the documents being from a document repository. The method also includes generating entity-sentence pairs from the documents and, for a first entity of the entities represented by the entity-sentence pairs, clustering the entity-sentence pairs for the first entity using salient terms occurring in the sentence. The method also includes determining a representative sentence for each of the clusters and providing at least one of the representative sentences in response to a query that identifies the first entity. Another example method includes determining that a query relates to an entity in a knowledge base, determining that the entity has an associated unique fact list, and providing at least one of the unique facts in the list in response to the query.

Abstract: Provided are an adaptive knowledge base construction system and method. The adaptive knowledge base construction system includes a machine learning engine analyzing a correlation between pieces of data included in a first data set in a process of learning the first data set input thereto, based on machine learning, a rule generator generating a rule based on the machine learning by using an analysis result obtained by analyzing the correlation, and a semantic rule generator generating a semantic rule from the rule based on the machine learning by using a language expressing ontology, and reflecting the generated semantic rule in a knowledge base to extend the knowledge base.

Abstract: Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Abstract: Disclosed is a contactless powered and operated self-organizing sensing co-processor system for interacting with an object and one or more peripheral units. The system communicates with a communicating device over a communication network. It includes a hub for providing a modulated alternating electric field with variable frequency and releases routing instructions and further communicates data with the communicating device and one or more nanoCloud processors, wherein at least one of the one or more nanoCloud processors interact with the object and the hub.

Abstract: In an approach to topic-based team analytics, a computing device extracts a list of topics based on a thread. The computing device identifies one or more participants with a relationship to one or more topics of the list of topics. The computing device generates a graph of the list of topics, the one or more participants, and relationships of the one or more participants to the one or more topics, wherein the one or more participants are represented as participant nodes of the graph and the one or more topics are represented as topic nodes of the graph, and wherein the relationships of the one or more participants to the one or more topics are represented as one or more edges connecting participant nodes with topic nodes.

Abstract: Methods, systems, and computer program products for expressive temporal predictions over semantically-driven time windows are provided herein. A computer-implemented method includes identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples.

Abstract: The present disclosure provides a data predicting method and apparatus. The method comprises: acquiring at least one time factor of a prediction moment; predicting a desired value of data of the prediction moment according to a preset data model and said at least one time factor of the prediction moment. As compared with a method of predicting the desired value on a year-on-year basis or a month-on-month basis in the prior art, the above technical solution of the present disclosure may effectively improve the prediction accuracy of the desired value of the data of the prediction moment and thereby improve a monitoring effect of monitoring abnormality according to the desired value of the predicted data.

Abstract: Provided is an information processing apparatus, including a calculation section which calculates a proficiency level of a user for operations performed by the user for achieving a prescribed objective based on history information related to the operations and attribute information related to physical features of the user, and a generation section which generates advice for achieving the objective based on the proficiency level calculated by the calculation section.