Abstract: Embodiments relate to a method, an electronic device, and a program product for training an encoder and processing data. The method includes inputting sample point cloud data for an object to an encoder to obtain encoded data for the object, and determining, by transforming the encoded data, a plurality of invariant portions for the object and a plurality of variable portions for the object, an invariant portion in the plurality of invariant portions indicating an invariant feature of the object and a variable portion in the plurality of variable portions indicating a variable feature of the object. The method further includes determining, based on the plurality of invariant portions and the plurality of variable portions, a similarity loss and a spatial loss for the sample point cloud data, and adjusting, based on the similarity loss and the spatial loss, a parameter of the encoder to obtain a trained encoder.

Abstract: An information processing device includes: an acquisition unit that acquires a captured image; a superimposition unit that generates a superimposition image in which images are superimposed on unused areas of the captured image; and a determination unit that determines a state of the captured image on the basis of characteristics of areas on which the images are superimposed in the superimposition image transmitted from the superimposition unit.

Abstract: A method for learning and applying a similarity measure between geophysical objects is provided. Similarity measures may be used for a variety of geophysics applications, including inverse problems. For example, an inverse problem may seek to minimize or maximize an associated objective function, which summarizes the degree of similarity between observed data and simulated data. However, when comparing between two or more geophysical objects in the context of the inverse problem, it is difficult to determine whether the observed difference between the two or more geophysical objects is due to noise or intrinsic dissimilarity between the objects. In this regard, an application-specific similarity measure, which may be tailored to the specific application, such as the specific inverse problem, may be generated and applied in order to better solve the inverse problem.

Abstract: A training data acquirer acquires training data including article image data, image-filter-related data indicating a combination of a plurality of image filters used for image processing of the article image data and a value of a parameter for each of the plurality of image filters, and optical character recognition (OCR) score data indicating a score of character recognition output through OCR when image processing is performed on the article image data using the image filters based on the image-filter-related data. A trained model generator generates a trained model indicating a relationship between the article image data, the image-filter-related data, and the OCR score data through machine learning using the training data.

Abstract: Embodiments described herein provide various examples of a machine-learning-based visual-haptic system for constructing visual-haptic models for various interactions between surgical tools and tissues. In one aspect, a process for constructing a visual-haptic model is disclosed. This process can begin by receiving a set of training videos. The process then processes each training video in the set of training videos to extract one or more video segments that depict a target tool-tissue interaction from the training video, wherein the target tool-tissue interaction involves exerting a force by one or more surgical tools on a tissue. Next, for each video segment in the set of video segments, the process annotates each video image in the video segment with a set of force levels predefined for the target tool-tissue interaction. The process subsequently trains a machine-learning model using the annotated video images to obtain a trained machine-learning model for the target tool-tissue interaction.

Abstract: An apparatus comprising an acquisition unit configured to acquire a captured image from a camera that is capable of controlling an orientation, a display control unit configured to display the captured image onto a display screen, a detection unit configured to detect, within the display screen, a subject at which a user is looking, and an orientation changing unit configured to change an orientation of the camera in such a way that the subject does not fall out of an imaging region of the camera is provided.

Abstract: A pet is identified by capturing or receiving an image containing the pet, isolating the pet in the image, and identifying the pet. Isolating the pet may include image enhancement, such as outlining the pet (e.g., a fur outline) or removing a background of the image. The identification may include comparison of various pet characteristics to a database of characteristics associated with known pets. The characteristics may be type of animal characteristics, breed characteristics, and specific pet characteristics and/or the pet's name. Pets and their characteristics may be may be added to the database using similar criteria forming a profile for each pet that may be used for other purposes. More than one pet may be linked to an owner such as through a mobile device ID and more than one owner or individual may be linked to a pet.

Abstract: An information processing apparatus that generates an image in a virtual space to be presented to a user detects information as to a position of a target object in a real space, places a virtual object corresponding to the target object at a position in the virtual space obtained on the basis of the detected information as to the position of the target object in the real space, acquires user body size data representing the size of a body of the user in the real space, and determines the position in the virtual space at which the virtual object is to be placed, using the detected information as to the position of the target object in the real space, and the user body size data about the user in the real space.

Abstract: An automated system and method for assessing and rating accessibility are provided. A processor collects raw data corresponding to geographical objects obtained from sensors, the raw data including tagged and non-tagged data; operates on the raw data to extract features and reduce dimensionality of the raw data, thereby generating processed data having extracted features; generates accessibility data from the processed data; uses supervised machine learning techniques to develop models from the processed data; and implements the models and generates accessibility tags based on the extracted features. A database is configured to store geographical data related to the geographical objects and the accessibility tags corresponding to the geographical locations. An API is configured to access the database and provide a user interface for a user device to use an application to display the accessibility data and the accessibility tags on the user device customized to a disability.

Abstract: A monocular snapshot four-dimensional imaging method and system are provided. The method includes constructing a numerical simulation model of an imaging system, and optimizing a phase distribution of an entrance pupil plane of the imaging system to determine a point spread function and a lens parameter corresponding to the phase distribution; building the imaging system, calibrating the point spread function, and obtaining a distortion parameter of the imaging system; taking a single shot on a target scene to obtain a single encoded image containing four-dimensional light field information of the target scene, and correcting the single encoded image according to the distortion parameter of the imaging system; and decoding the four-dimensional light field information of the target scene from the single encoded image according to the calibrated point spread function to obtain an intensity image, a polarization contrast image and a depth map of the target scene.

Abstract: Techniques are generally described for performing outfit recommendation using a recurrent neural network. In various examples, a computing device may receive a first state vector representing an outfit comprising at least one fashion item. First image data depicting a second fashion item of a first item category may be received. A machine learning mode may generate a first output feature vector based on the first state vector, the first image data, a first attribute vector, and the first item category. The first output feature vector may be compared to other feature vectors representing other fashion items in the first category to determine distances between the first output feature vector and the other feature vectors. A set of fashion items may be recommended based on the distances between the first output feature vector and the other feature vectors.

Abstract: A method for detecting defects in product applied in a computer device inputs an image of a product under test to an automatic encoder to obtain a reconstructed image, and the image is segmented into N image blocks and the reconstructed image is segmented into N image blocks. The computer device associates each of the N testing blocks with one reconstructed blocks according to positions of the N testing blocks in the image and positions of the N reconstructed blocks in the reconstructed image. The computer device further calculates mean square errors between each of the N testing blocks and each of the N reconstructed blocks, and associates each mean square error with each of the N testing blocks, whether the product has defects being determined based on the mean square errors corresponding to each of the N testing blocks.

Abstract: A method comprising: receiving a dataset comprising a plurality of data instances; extracting a feature vector representation of each of the data instances in the dataset; choosing a first data instance for adding to a subset of the dataset, wherein the first data instance is removed from the dataset; performing an iterative process comprising: (i) identifying one of the data instances in the dataset which represents a maximal information addition to the subset, based, at least in part, on measuring an information difference parameter between the feature vector representation of the identified data instance and the feature vector representations of all of the data instances in the subset, and (ii) adding the identified data instance to the subset and removing the identified data instance from the dataset, until the information difference parameter is lower than a predetermined threshold; and outputting the subset as a representative subset of the dataset.

Abstract: Provided are methods for automatically detecting traffic signals using sensor data. The methods can include obtaining first sensor data and second sensor data regarding an environment of an autonomous vehicle, where the first sensor data represents at least one image of the environment generated by at least one image sensor of the autonomous vehicle, where the second sensor data represents a three-dimensional point cloud associated with the environment, and where the second sensor data is generated by at least one range sensor of the autonomous vehicle. The method can also include determining a portion of the at least one image corresponding to a traffic signal in the environment, determining a point cluster of the point cloud corresponding to the portion of the at least one image, and determining a location of the traffic signal based on the point cluster. Systems and computer program products are also provided.

Abstract: A trained artificial intelligence engine analyzes a subject's dental images and dental survey results to determine whether the subject has an apparent dental condition. The artificial intelligence engine is trained using manually-classified dental images and model survey results. The trained artificial intelligence engine can annotate the subject's dental images to indicate landmark structures and the location of the apparent dental condition. In addition, the trained artificial intelligence engine can generate a confidence score that indicates the likelihood that the subject has the apparent dental condition.

Abstract: Systems and methods monitor a workspace for safety purposes using sensors distributed about the workspace. The sensors are registered with respect to each other, and this registration is monitored over time. Occluded space as well as occupied space is identified, and this mapping is frequently updated. Based on the mapping, a constrained motion plan of machinery can be generated to ensure safety.

Abstract: A method is disclosed for determining a position of a first image region in an image corresponding to a second image region in the image. The method comprises: providing the first image region comprising a reference position; providing the image; determining a plurality of signature values for different image positions in the first image region; assigning relative offsets of a plurality of image positions in the first image region to respectively determined signature values; determining a plurality of correspondence signature values to different image positions in the image; determining relative offsets relative to determined correspondence signature values with use of the assignment; calculating target positions by summing the relative offset and corresponding image position; incrementally assigning a weighting value to a calculated target position, thus forming an incremental weighting value; and determining the position of the first image region by finding an extremum in the incremental weighting values.

Abstract: An iris recognition apparatus for performing recognition of a target person based on an iris pattern of the target person, is provided with: an imaging device that performs an imaging processing for sequentially outputting imaged images while changing a focal position; and a determining device that starts a focusing determination for determining whether or not an image is a focused image before the imaging device completes the imaging processing.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: The present disclosure is directed to devices and methods for synchronizing capturing of spectral images with the capturing of thermal images. A thermal imaging device of an aerial vehicle captures a sequence of thermal image of thermal images. Capturing of spectral images by a spectral imaging device of the aerial vehicle is synchronized with the capturing of the thermal images. Irradiance data indicative of a background temperature is sensed. A digital surface model of an area of interest is generated based on the sequence of spectral images. An emissivity of a target is estimated and a temperature of a pixel of the digital surface model of the target is estimated based on the sequence of thermal images, the irradiance data indicative of the background temperature and the estimated emissivity of the target.