Abstract: A method for sending a screen image by a computing device is described. A modification image representing a modified region of the screen image is determined. The modification image has a lossless format. Location information is encoded in one or more alpha channels of the modification image. The modification image is sent.

Abstract: Embodiments of the invention provide a method of visual saliency estimation comprising receiving an input sequence of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The method further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The method further comprises encoding each map of features extracted as neural spikes.

Abstract: An image processing device and an image processing method thereof are provided. The image processing device uses feature points of an image to calculate weights of endpoints of straight lines in the image, and determines coordinates of the endpoints of the straight lines after image morphing. The image processing device further uses the transformed coordinates of the endpoints of the straight lines to calculate corresponding coordinates of points of the straight lines. The image processing device further uses orthogonal relation between coordinates of image points and the coordinates of points of the straight lines to add feature points, and calculates coordinates of the image points after image morphing.

Abstract: The present invention relates to a method and device for recognizing a situation based on an image using a template. The present invention provides the device for recognizing a situation based on an image. The device includes a camera unit capturing an image which is divided into a plurality of regions and having a template for each of the regions, the template defining characteristics of each of the regions, and a control unit detecting an object in the image and determining the situation surrounding the object according to the template mapped to the region to which the detected object belongs. In addition, the present invention provides the method of operating the device for recognizing a situation.

Abstract: Provided herein are various systems and methods of adjusting images of an image series that are preloaded (and/or otherwise processed) in view of behavior data associated with viewing of other previous exams having similar characteristics (e.g., same modality) and/or by the same user.

Abstract: In accordance with the present invention, there is provided a system and method for dark noise reduction in pulse width modulated (PWM) displays. The system includes means for determining bright corrected pixel values for dark portions of an image corresponding to a first subframe and means for determining dark corrected pixel values for dark image portions of an image corresponding to a second sub frame of the image frame.

Abstract: A platform for generating a first character recognition-based work including a first plurality of automatically-made edits, each edit being characterized by a Unicode and a confidence score. The platform may identify at least one edit as being of questionable accuracy based on the confidence score, may determine a unique character signature of the edit, and may receive a manual correction made to the edit. The platform may also store the manual correction in association with the character signature and the Unicode, such that the manual correction is configured for use in generating a second plurality of automatically-made edits in a second character recognition-based work different than the first work.

Abstract: A method and system for estimating motion blur of an image associated with a moving object. The direction of one-dimensional motion blur may be estimated by inspecting a power spectrum associated with the image. A radon transform with respect to the image power spectrum is computed in the direction of the motion blur. A family of kernels with respect to the one-dimensional motion blur may then be defined utilizing a shutter triggering sequence associated with an image capturing device. The family of kernels may be modeled utilizing a modulation transfer function (MTF). Each modulation transfer function may be compared with the radon transform of the power spectrum associated with the image via a correlation function. The kernel with highest correlation with respect to the radon transform of the image power spectrum may be employed for de-blurring the image.

Abstract: Systems and techniques are provided for pruning a node from a possible nodes list for Hidden Markov Model with label transition node pruning. The node may be a label transition node. A frame may be at a predicted segmentation point in decoding input with the Hidden Markov Model. The node may be scored at the frame. The node may be pruned from the possible nodes list for the frame when score for the node is greater than the sum of a best score among nodes on the possible nodes list for the frame and a beam threshold minus a penalty term. A possible nodes list may be generated for a subsequent frame using label selection. A second node may be pruned from the possible nodes list for the subsequent frame with early pruning.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying objects in images. One of the methods includes obtaining a first training image; down-sampling the first training image to generate a low-resolution first training image; processing the low-resolution first training image using a first neural network to generate a plurality of features of the low-resolution first training image and first scores for the low-resolution first training image; processing the first scores and the features of the low-resolution first training image using an initial patch locator neural network to generate an initial location of an initial patch of the first training image; locally perturbing the initial location to select an adjusted location for the initial patch of the first training image; and updating the current values of the parameters of the initial patch locator neural network to generate updated values using the adjusted location.

Abstract: A system and method for facilitating patent grant and patent application claims examination; including the functions of automated importing of patent claims, automated parsing of the claims into their hierarchy, and compression/expansion of the parsed claims to/from the independent claim level.

Abstract: In one embodiment, L dimensional images are trained, mapped, and aligned to an M dimensional topology to obtain azimuthal angles. The aligned L dimensional images are then trained and mapped to an N dimensional topology to obtain 2N vertex classifications. The azimuthal angles and the 2N vertex classifications are used to map L dimensional images into 0 dimensional images.

Abstract: Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue.

Abstract: Tacit knowledge associated with software development problems may be shared by providing a framework configured to pre-process raw service data of a software product for identification of data elements. Pre-processing operations may be applied to the raw service data to extract data elements. An identified operating problem in the service data may be efficiently analyzed by referring to a repository of stored records that include previously performed user actions when facing the identified operating problem.

Abstract: Certain aspects of the present disclosure relate to methods and apparatus for neuro-simulation with a single two-dimensional device to track objects. The neuro-simulation may report a point of interest in an image that is provided by the device. The device may center on the point of interest using one or more actuators. The simulation mechanism may input pixels and output a plurality of angles to the actuators to adjust their direction.

Abstract: Image classification techniques using images with separate grayscale and color channels are described. In one or more implementations, an image classification network includes grayscale filters and color filters which are separate from the grayscale filters. The grayscale filters are configured to extract grayscale features from a grayscale channel of an image, and the color filters are configured to extract color features from a color channel of the image. The extracted grayscale features and color features are used to identify an object in the image, and the image is classified based on the identified object.

Abstract: Generally, this disclosure provides systems, devices, methods and computer readable media for a depth camera with ML techniques for recognition of patches within an SL pattern. The system may include a projection module to project an ML-based SL pattern onto a scene; a camera to receive an image of the SL pattern reflected from the scene; a patch recognition and location module to generate a descriptor vector for a patch segmented from the received image and to query an ML system with the descriptor vector, the ML system configured to provide a patch label associated with the descriptor vector, the patch label comprising a location of the patch relative to the projected SL pattern; and a depth estimation module to triangulate a distance between the camera and a region of the scene associated with the patch based on the location of the patch relative to the projected SL pattern.

Abstract: A method and a digital filter, for use with photo and video images, includes using a camera or video camera equipped with sensors and an electronic shutter to capture a plurality of frames of low resolution and producing one frame of high resolution. A plurality of frames are exposed. Initial images are in the form of a continuous sequence of frames with high-speed capture. The frequency of the frames is inversely proportional to the magnitude of that part of the light-sensitive region of the sensor that is being scanned. The initial images are aligned and an enhanced image is produced. The enhanced image is filtered using a nonlinear filter which includes a neural network that is pretrained using a test image including radial and sinusoidal test charts, as well as reference points.

Abstract: Disclosed are a component recognizing apparatus and a component recognizing method. The component recognizing apparatus includes: an image preprocessing unit configured to extract component edges from an input component image by using a plurality of edge detecting techniques, and detect a component region by using the extracted component edges; a feature extracting unit configured to extract a component feature from the detected component region, and create a feature vector by using the component feature; and a component recognizing unit configured to input the created feature vector to an artificial neural network which has learned in advance to recognize a component category through a plurality of component image samples, and recognize the component category according to a result.

Abstract: The disclosed subject matter relates to computer implemented methods for sharing digital image edit operations. In one aspect, a method includes storing a first digital image edit stack, which includes at least one digital image edit operation performed by a first user of a social network upon a first digital image hosted on the social network. The method further includes receiving indication of a first request for the first digital image edit stack, based upon an operation performed by a second user of the social network. The method further includes providing the digital image edit stack for the second user, in response to the received indication.

Abstract: Apparatus and methods for detecting salient features. In one implementation, an image processing apparatus utilizes latency coding and a spiking neuron network to encode image brightness into spike latency. The spike latency is compared to a saliency window in order to detect early responding neurons. Salient features of the image are associated with the early responding neurons. A dedicated inhibitory neuron receives salient feature indication and provides inhibitory signal to the remaining neurons within the network. The inhibition signal reduces probability of responses by the remaining neurons thereby facilitating salient feature detection within the image by the network. Salient feature detection can be used for example for image compression, background removal and content distribution.

Abstract: An image distortion correction method and an image distortion correction device are provided. The image distortion correction method uses a neural network model to perform a correcting operation on an original image so as to obtain a correction image with plural correction points. Firstly, a position coordinate of the correction point is inputted into the neural network model, so that a first direction coordinate correction amount is outputted from the neural network model. Then, the position coordinate of the correction point is inputted into the neural network model, so that a second direction coordinate correction amount is outputted from the neural network model. Afterwards, a pixel value of the original image corresponding to the first direction coordinate correction amount and the second direction coordinate correction amount is used as a pixel value of the correction point.

Abstract: Disclosed are a hand positioning method and a human-computer interaction system. The method comprises a step of continuously capturing a current image so as to obtain a sequence of video images; a step of extracting a foreground image from each of the captured video images, and then carrying out binary processing so as to obtain a binary foreground image; a step of obtaining a vertex set of a minimum convex hull of the binary foreground image, and then creating areas of concern serving as candidate hand areas; and a step of extracting hand imaging features from the respective created areas of concern, and then determining a hand area from the candidate hand areas by carrying out pattern recognition based on the extracted hand imaging features.

Abstract: A learning apparatus in the present invention includes a weak discriminator generation unit that generates a weak discriminator which calculates a discrimination score of an instance of a target based on a feature and a bag label, a weak discrimination unit which calculates the discrimination score based on the generated weak discriminator, an instance probability calculation unit that calculates an instance probability of the target instance based on the calculated the discrimination score, a bag probability calculation unit that calculates a probability that no smaller than two positive instances are included in the bag based on the calculated instance probability and a likelihood calculation unit which calculates likelihood representing plausibility of the bag probability based on the bag label.

Abstract: Described is a system for object detection using classification-based learning. A fusion method is selected, then a video sequence is processed to generate detections for each frame, wherein a detection is a representation of an object candidate. The detections are fused to generate a set of fused detections for each frame. The classification module generates a classification score labeling each fused detection based on a predetermined classification threshold. Otherwise, a token indicating that the classification module has abstained from generating a classification score is generated. The scoring module produces a confidence score for each fused detection based on a set of learned parameters from the learning module and the set of fused detections. The set of fused detections are filtered by the accept-reject module based on one of the classification score or the confidence score. Finally, a set of final detections representing an object is output.

Abstract: Systems and methods for sequence transcription with neural networks are provided. More particularly, a neural network can be implemented to map a plurality of training images received by the neural network into a probabilistic model of sequences comprising P(S|X) by maximizing log P(S|X) on the plurality of training images. X represents an input image and S represents an output sequence of characters for the input image. The trained neural network can process a received image containing characters associated with building numbers. The trained neural network can generate a predicted sequence of characters by processing the received image.

Abstract: An embodiment discloses a method utilizing a device for handling multiple documents during scanning. The method includes receiving multiple documents on or within a sleeve to enable scanning of at least one of a first side and a second side of each of the documents in a single scanning operation. The method includes identifying one or more dimensional characteristics of the sleeve and each document arranged on or within the sleeve. The method further includes scanning at least one of the first side and the second side of each document. Moreover, the method includes tagging the scanned documents based on at least one of content characteristics and the dimensional characteristics of the documents.

Abstract: A dictionary of atoms for coding data is learned by first selecting samples from a set of samples. Similar atoms in the dictionary are clustered, and if a cluster has multiple atoms, the atoms in that cluster are merged into a single atom. The samples can be acquired online.

Abstract: The present invention relates to a convolutional-neural-network-based classifier, a classifying method by using a convolutional-neural-network-based classifier and a method for training the convolutional-neural-network-based classifier. The convolutional-neural-network-based classifier comprises: a plurality of feature map layers, at least one feature map in at least one of the plurality of feature map layers being divided into a plurality of regions; and a plurality of convolutional templates corresponding to the plurality of regions respectively, each of the convolutional templates being used for obtaining a response value of a neuron in the corresponding region.

Abstract: A sample set of images is received. Each image in the sample set may be associated with one or more social cues. Correlation of each image in the sample set with an image class is scored based on the one or more social cues associated with the image. Based on the scoring, a training set of images to train a classifier is determined from the sample set. In an embodiment, an extent to which an evaluation set of images correlates with the image class is determined. The determination may comprise ranking a top scoring subset of the evaluation set of images.

Abstract: Described is a system for object and behavior recognition which utilizes a collection of modules which, when integrated, can automatically recognize, learn, and adapt to simple and complex visual behaviors. An object recognition module utilizes a cooperative swarm algorithm to classify an object in a domain. A graph-based object representation module is configured to use a graphical model to represent a spatial organization of the object within the domain. Additionally, a reasoning and recognition engine module consists of two sub-modules: a knowledge sub-module and a behavior recognition sub-module. The knowledge sub-module utilizes a Bayesian network, while the behavior recognition sub-module consists of layers of adaptive resonance theory clustering networks and a layer of a sustained temporal order recurrent temporal order network. The described invention has applications in video forensics, data mining, and intelligent video archiving.

Abstract: Systems and methods for developing and using adaptive threshold values for different input images for object detection are disclosed. In embodiments, detector response histogram-based systems and methods train models for predicting optimal threshold values for different images. In embodiments, when training the model, an optimal threshold value for an image is defined as the value that maximizes the reduction of false positive image patches while preserving as many true positive image patches as possible. Once trained, the model may be used to set different threshold values for different images by inputting a detector response histogram for the image patches of an image into the model to determine a threshold value for detection.

Abstract: Certain embodiments of the present invention provide methods and systems for determining a hanging protocol for display of clinical images in a study. Certain embodiments provide a machine learning hanging protocol analysis system. The example system includes an image processing module to process image data to provide one or more features. The example system includes a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms to the processed image data and additional data. The learning engine is to continue to refine an available selection of candidate layouts based on the processed image data and additional data to provide one or more layout choices for selection to form a hanging protocol for display of image and other data.

Abstract: Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed.

Abstract: Down-sampling of an image may be performed in the DCT domain. A multiple layered network is used to select transform matrices for down-sampling a DCT image of size M×N to a DCT image of size I×J. A spatial domain down-sampling method is selected and applied to the DCT image to produce a down-sampled DCT reference image. A learning with forgetting algorithm is used to apply a decay to the elements of the transform matrix and select a transform matrices which solve an optimization problem. The optimization problem is a function of the visual quality of images obtained using the transform matrices and the computational complexity associated with using the transform matrices. The visual quality is a measure of the difference between the down-sampled DCT image obtained using the transform matrices and the visual quality of the DCT reference image obtained using a spatial domain down-sampling method.

Abstract: A method and apparatus for processing image data is provided. The method includes the steps of employing a main processing network for classifying one or more features of the image data, employing a monitor processing network for determining one or more confusing classifications of the image data, and spawning a specialist processing network to process image data associated with the one or more confusing classifications.

Abstract: One or more techniques and/or systems are disclosed for mitigating machine solvable human interactive proofs (HIPs). A classifier is trained over a set of one or more training HIPs that have known characteristics for OCR solvability and HIP solving pattern from actual use. A HIP classification is determined for a HIP (such as from a HIP library used by a HIP generator) using the trained classifier. If the HIP is classified by the trained classifier as a merely human solvable classification, such that it may not be solved by a machine, the HIP can be identified for use in the HIP generation system. Otherwise, the HIP can be altered to (attempt to) be merely human solvable.

Abstract: Approaches to segmentation or detection of objects and their boundaries in images (or other data sets) do not rely on machine learning approaches that aim to minimize pixel-level agreement between a computer and a human. Optimizing such pixel-level agreement does not, in general, provide the best possible result if boundary detection is a means to the ultimate goal of image segmentation, rather than an end in itself. In some examples, end-to-end learning of image segmentation specifically targets boundary errors with topological consequences, but otherwise does not require the computer to “slavishly” imitate human placement of boundaries. In some examples, this is accomplished by modifying a standard learning procedure such that human boundary tracings are allowed to change during learning, except at locations critical to preserving topology.

Abstract: An image processing method is provided for an image processing apparatus which executes processing by allocating a plurality of weak discriminators to form a tree structure having branches corresponding to types of objects so as to detect objects included in image data. Each weak discriminator calculates a feature amount to be used in a calculation of an evaluation value of the image data, and discriminates whether or not the object is included in the image data by using the evaluation value. The weak discriminator allocated to a branch point in the tree structure further selects a branch destination using at least some of the feature amounts calculated by weak discriminators included in each branch destination.

Abstract: A user emotion detection method for a handwriting input electronic device is provided. The method includes steps of: obtaining at least one handwriting input characteristic parameter; determining a user emotion parameter by an artificial neural network of the handwriting input electronic device according to the handwriting input characteristic value and at least one associated linkage value; displaying the user emotion parameter on a touch display panel of the handwriting input electronic device; receiving a user feedback parameter; determining whether to adjust the at least one associated linkage value and if yes, adjusting the at least one associated linkage value according to the user feedback parameter to construct and adjust the artificial neural network.

Abstract: Contact-less remote-sensing crack detection and/quantification methodologies are described, which are based on three-dimensional (3D) scene reconstruction, image processing, and pattern recognition. The systems and methodologies can utilize depth perception for detecting and/or quantifying cracks. These methodologies can provide the ability to analyze images captured from any distance and using any focal length or resolution. This adaptive feature may be especially useful for incorporation into mobile systems, such as unmanned aerial vehicles (UAV) or mobile autonomous or semi-autonomous robotic systems such as wheel-based or track-based radio controlled robots, as utilizing such structural inspection methods onto those mobile platforms may allow inaccessible regions to be properly inspected for cracks.

Abstract: The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.

Abstract: Disclosed herein are systems and methods for facilitating the usage of an online workforce to remotely monitor security-sensitive sites and report potential security breaches. In some embodiments, cameras are configured to monitor critical civilian infrastructure, such as water supplies and nuclear reactors. The cameras are operatively connected to a central computer or series of computers, and images captured by the cameras are transmitted to the central computer. After initially registering with the central computer, Guardians “log on” to a central website hosted by the central computer and monitor the images, thereby earning compensation. Site owners compensate the operator of the computer system for this monitoring service, and the operator in turn compensates Guardians based on, for example, (i) the amount of time spent monitoring, and/or (ii) the degree of a given Guardian's responsiveness to real or fabricated security breaches.

Abstract: A method for determining that a user associated with a first identifiable device or identifiable service is also associated with a second identifiable device or identifiable service by a) generating one or more first image descriptors for one or more first images stored on the first identifiable service associated with a first user, b) generating one or more second image descriptors for one or more second images stored on the second identifiable service associated with a second user, c) calculating, based on the generated first and second image descriptors, the probability that the first user is also the second user. Also provided is a computer readable storage medium containing program code for implementing the method.

Abstract: A collaborative feature extraction system uses crowdsourced feedback to improve its ability to recognize objects in three-dimensional datasets. The system accesses a three-dimensional dataset and presents images showing possible objects to a group of users, along with potential identifiers for the objects. The users provide feedback as to the accuracy of the identifiers, and the system uses the feedback to adjust parameters for candidate identifiers to improve its recognition of three-dimensional assets in future iterations.

Abstract: A system and method for generating training images. An existing training image is associated with a classification. The system includes an image processing module that performs color-space deformation on each pixel of the existing training image and then associates the classification to the color-space deformed training image. The technique may be applied to increase the size of a training set for training a neural network.

Abstract: Disclosed herein is an apparatus and method for detecting a person from an input video image with high reliability by using gradient-based feature vectors and a neural network. The human detection apparatus includes an image preprocessing unit for modeling a background image from an input image. A moving object area setting unit sets a moving object area in which motion is present by obtaining a difference between the input image and the background image. A human region detection unit extracts gradient-based feature vectors for a whole body and an upper body from the moving object area, and detects a human region in which a person is present by using the gradient-based feature vectors for the whole body and the upper body as input of a neural network classifier. A decision unit decides whether an object in the detected human region is a person or a non-person.

Abstract: A character recognition apparatus and method based on a character orientation are provided, in which an input image is binarized, at least one character area is extracted from the binarized image, a slope value of the extracted at least one character area is calculated, the calculated slope value is set as a character feature value, and a character is recognized by using a neural network for recognizing a plurality of characters by receiving the set character feature value. Accordingly, the probability of wrongly recognizing a similar character decreases, and a recognition ratio of each character increases.

Abstract: A system for efficient image feature extraction comprises a buffer for storing a slice of at least n lines of gradient direction pixel values of a directional gradient image. The buffer has an input for receiving the first plurality n of lines and an output for providing a second plurality m of columns of gradient direction pixel values of the slice to an input of a score network, which comprises comparators for comparing the gradient direction pixel values of the second plurality of columns with corresponding reference values of a reference directional gradient pattern of a shape and adders for providing partial scores depending on output values of the comparators to score network outputs which are coupled to corresponding inputs of an accumulation network having an output for providing a final score depending on the partial scores.

Abstract: A wordspotting system and method are disclosed for processing candidate word images extracted from handwritten documents. In response to a user inputting a selected query string, such as a word to be searched in one or more of the handwritten documents, the system automatically generates at least one computer-generated image based on the query string in a selected font or fonts. A model is trained on the computer-generated image(s) and is thereafter used in the scoring the candidate handwritten word images. The candidate or candidates with the highest scores and/or documents containing them can be presented to the user, tagged, or otherwise processed differently from other candidate word images/documents.