Abstract: A binary logic circuit for performing an interpolation calculation between two endpoint values E0 and E1 using a weighting index i for generating an interpolated result P, the values E0 and E1 being formed from Adaptive Scalable Texture Compression (ASTC) low-dynamic range (LDR) colour endpoint values C0 and C1 respectively, the circuit comprising: an interpolation unit configured to perform an interpolation between the colour endpoint values C0 and C1 using the weighting index i to generate a first intermediate interpolated result C2; and combinational logic circuitry configured to receive the interpolated result C2 and to perform one or more logical processing operations to calculate the interpolated result P according to the equation P=?((C2<<8)+C2+32)/64? when the interpolated result is not to be compatible with an sRGB colour space, and according to the equation P=?((C2<<8)+128·64+32)/64? when the interpolated result is to be compatible with an sRGB colour space.

Abstract: A binary logic circuit for performing an interpolation calculation between two endpoint values E0 and E1 using a weighting index i for generating an interpolated result P, the values E0 and E1 being formed from Adaptive Scalable Texture Compression (ASTC) colour endpoint values C0 and C1 respectively, the colour endpoint values C0 and C1 being low-dynamic range (LDR) or high dynamic range (HDR) values, the circuit comprising: an interpolation unit configured to perform an interpolation between the colour endpoint values C0 and C1 using the weighting index i to generate a first intermediate interpolated result C2; combinational logic circuitry configured to receive the interpolated result C2 and to perform one or more logical processing operations to calculate the interpolated result P according to the equation: (1) P=?(C2<<8)+C2+32)/64? when the interpolated result is not to be compatible with an sRGB colour space and the colour endpoint values are LDR values; (2) P=?(C2<<8)+128.

Abstract: A data processing device for motion segmentation in images obtained by cameras that move in a background environment includes an input for receiving a temporal sequence of images from the cameras and a processor. The processor is adapted for, for at least two images, of the temporal sequence of images, that are obtained by at least two cameras at different points in time, determining epipoles, defining corresponding image regions of limited image disparity due to parallax around the epipoles in the at least two images, and applying a motion segmentation algorithm to the corresponding image regions. Warping is applied to the corresponding image regions to compensate for camera rotation and misalignment beyond a threshold value.

Abstract: An apparatus and method for successive multi-frame image denoising are herein disclosed. The apparatus includes a first subtractor including a first input to receive a frame of the image, a second input to receive a reference frame, and an output; an absolute value function block including an input connected to the output of the first subtractor and an output; a second subtractor including a first input connected to the output of the absolute value function block, a second input for receiving a first predetermined value, and an output; and a maximum value divider function block including an input connected to the output of the second subtractor and an output for outputting filter weights.

Abstract: Pairing a user response and associated context with a neural network associated with a virtual assistant computer during a dynamic text conversation with an end user. The virtual assistant computer receives a detected user generated text input; determines context of the detected user generated text input; compares the context of the detected user generated text input by comparing a confidence score representing context of the user generated input to a classification associated with each of a plurality of existing nodes of a neural network. For confidence scores below a threshold relative to the classification associated with each of the existing nodes of the neural network, the virtual assistant computer creates a new node within the neural network and assigns the context of the user generated text to the new node.

Abstract: A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

Abstract: An approach is provided for determining an optimal alignment of a device. The approach, for example, involves receiving image data from a device mounted in a vehicle. The approach also involves presenting an alignment template in a user interface of the device as an overlay on the image data, wherein the alignment template provides one or more guidelines indicating a target alignment of the device to capture images from the vehicle for an application. The approach further involves processing the image data against the alignment template to determining an alignment of the device in relation to the target alignment.

Abstract: The disclosure provides an image recognition method and an image recognition device. The method includes: acquiring an image and capturing a plurality of feature points in the image; obtaining a capsule network, where the capsule network sequentially includes a convolution layer, a primary capsule layer, a routing capsule layer, and an output layer; inputting the image and the feature points into the convolution layer to generate a plurality of feature vectors; inputting the feature vectors and the feature points into the primary capsule layer to generate a plurality of activity vectors; and generating a recognition result corresponding to the image by the routing capsule layer and the output layer based on the activity vectors.

Abstract: Methods and systems for outputting depth data during a medical procedure on a patient. Depth data is outputted, representing at least one of relative depth data and general depth data. Tracking information about the position and orientation of a medical instrument and depth information about variations in depth over a site of interest are used. Relative depth data represents the depth information relative to the position and orientation of the instrument. General depth data represents the depth information over the site of interest independently of the position and orientation of the instrument.

Abstract: A method for generating a neural network for detecting one or more objects in images includes generating one or more self-supervised proposal learning losses based on the one or more proposal features and corresponding proposal feature predictions. One or more consistency-based proposal learning losses are generated based on noisy proposal feature predictions and the corresponding proposal predictions without noise. A combined loss is generated using the one or more self-supervised proposal learning losses and one or more consistency-based proposal learning losses. The neural network is updated based on the combined loss.

Abstract: A system identifies anomalies in an image of an object. An input image of the object containing zero or more anomalies is supplied to an image encoder. The image encoder generates an image model. The image model is applied to an image decoder that forms a substitute non-anomalous image of the object. Differences between the input image and the substitute non-anomalous image identify zero or more areas of the input image that contain the zero or more the anomalies. The system implements a flow-based model and has been trained using (a) a set of augmented anomaly-free images of the object applied at the image encoder and (b) a reconstruction loss calculated based on a norm of differences between each augmented anomaly-free image of the object and a corresponding output image from the image decoder.

Abstract: An image compression apparatus includes: an image acquisition unit configured to acquire a raw data image; a pre-processing network configured to receive the raw data image and pre-process the raw data image according to a pattern estimation method learned beforehand; and an encoder unit configured to receive the pre-processed image and compress the pre-processed image according to a pre-designated standard compression technique to output a compressed image. The pre-processing network, which can be added during learning and can be implemented as an artificial neural network, can have learned beforehand by way of a backpropagation of a restoration error through a codec modeling unit that has learned beforehand to simulate a standard codec unit, where the restoration error can be obtained by comparing a restored image obtained based on a simulated decoded image with the raw data image.

Abstract: A method for automatically analyzing and constructing communications to a plurality of recipients includes automatically separating communication content files into page groups in a system comprising one or more intelligent communication design servers, wherein each of the page groups is associated a recipient of the communications, inputting the communication content files into an intra-page machine prediction model to produce intra-page parameters, inputting the communication content files and the intra-page parameters into an intra-page machine prediction model to produce intra-group parameters and inter-group parameters, automatically constructing standard communication design files by an intelligent communication content learning and constructing engine based on the communication content files and the intra-page parameters, intra-group parameters, and inter-group parameters, and printing and finishing physical mailing pieces to be mailed to the recipients based on the standard communication design files.

Abstract: Provided an apparatus that receives time series data from a data storage unit storing time series of sample data or feature values calculated from the sample data, computes a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof, selects a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure and stores the selected state model in a model storage unit.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining a graph of nodes and edges that represents an interaction history of the agent with the environment; generating an encoded representation of the graph representing the interaction history of the agent with the environment; processing an input based on the encoded representation of the graph using an action selection neural network, in accordance with current values of action selection neural network parameters, to generate an action selection output; and selecting an action from a plurality of possible actions to be performed by the agent using the action selection output generated by the action selection neural network.

Abstract: A computer-implemented method, a computer program product, and a computer system for multi-sample dropout in deep neural network training. A computer creates multiple dropout samples in a minibatch, starting from a dropout layer and ending at a loss function layer in a deep neural network. At the dropout layer in the deep neural network, the computer applies multiple random masks for respective ones of the multiple dropout samples. At a fully connected layer in the deep neural network, the computer applies a shared parameter for all of the multiple dropout samples. After the loss function layer in the deep neural network, the computer calculates a final loss value, by averaging loss values of the respective ones of the multiple dropout samples.

Abstract: A system and method for autonomously defining regions of interest for a container are provided. The system comprises a platform for supporting the container, a detector for capturing feature data of the container while on the platform, and a computer system. The computer system is in communication with the detector and platform. The computer system is programmed to locate features of the container from the captured feature data, and define the regions of interest for the container based on the located features.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate an output for the training input; processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output, wherein the discriminative score characterizes a prediction for whether the network input was generated using: (i) the student neural network, or (ii) a brain emulation neural network; and adjusting current values of the student neural network parameters using gradients of an objective function that depends on the discriminative score for the student neural network output.

Abstract: A system and method for inserting a composited image or otherwise generated graphic into a selected video by way of a programmatic process. According to some embodiments, a system may comprise an Automated Placement Opportunity Identification (APOI) engine, a Placement Insertion Interface (PII) engine, a preview system, and an automated compositing service. The system finalizes a graphic composite into a video and provides a user with a preview for final export or further manipulation.

Abstract: The present disclosure discloses a system and a method that includes receiving, at a decoder, a latent representation of an image having a first domain, and generating a reconstructed image having a second domain, wherein the reconstructed image is generated based on the latent representation.