The purpose of the present invention is to provide a pattern matching device and computer program that carry out highly accurate positioning even if edge positions and numbers change. The present invention proposes a computer program and a pattern matching device wherein a plurality of edges included in first pattern data to be matched and a plurality of edges included in second pattern data to be matched with the first pattern data are associated, a plurality of different association combinations are prepared, the plurality of association combinations are evaluated using index values for the plurality of edges, and matching processing is carried out using the association combinations selected through the evaluation.

The technology relates to navigating imagery that is organized into clusters based on common patterns exhibited when imagery is captured. For example, a set of captured images which satisfy a predetermined pattern may be determined. The images in the set of set of captured images may be grouped into one or more clusters according to the predetermined pattern. A request to display a first cluster of the one or more clusters may be received and, in response, a first captured image from the requested first cluster may be selected. The selected first captured image may then be displayed.

A method of continuous decoding of motion for a direct neural interface. The method of decoding estimates a motion variable from an observation variable obtained by a time-frequency transformation of the neural signals. The observation variable is modelled using a HMM model whose hidden states include at least an active state and an idle state. The motion variable is estimated using a Markov mixture of experts where each expert is associated with a state of the model. For a sequence of observation vectors, the probability that the model is in a given state is estimated, and from this a weighting coefficient is deduced for the prediction generated by the expert associated with this state. The motion variable is then estimated by combination of the estimates of the different experts with these weighting coefficients.

According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

Systems and methods of providing content selection are provided. For instance, one or more signals indicative of a user selection of an object displayed within a user interface can be received. Responsive to receiving the one or more signals, a content attribute associated with one or more objects displayed within the user interface can be identified. A content entity can be determined based at least in part on the content attribute and the user selection. One or more relevant actions can then be determined based at least in part on the determined content entity. Data indicative of the relevant actions can then be provided for display.

A computation unit calculates luminance differences of individual pixel pairs in a feature area and calculates, based thereon, a local feature value formed from bit values respectively corresponding to the pixel pairs. Specifically, the computation unit calculates a specific luminance difference for a specific pixel pair corresponding to a specific bit value and then compares the result with a specified range including a zero point of luminance difference. Then a first value is assigned to the specific bit value when the specific luminance difference is greater than the upper bound of the specified range. A second value is assigned to the same when the specific luminance difference is smaller than the lower bound of the specified range. A predetermined one of the first and second values is assigned to the same when the specific luminance difference falls in the specified range.

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

During an intraoral scan session, a processing device receives a plurality of intraoral images of a dental site. The processing device determines that a historical template of the dental site is existent and registers a first intraoral image of the plurality of intraoral images to the historical template at a first region of the dental site depicted in the historical template. The processing device may register one or more remaining intraoral images of the plurality of intraoral images to the historical template at a second region of the dental site depicted in the historical template. The first region may be separated from the second region.

An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.

A “Best of Burst Selector,” or “BoB Selector,” automatically selects a subjectively best image from a single set of images of a scene captured in a burst or continuous capture mode, captured as a video sequence, or captured as multiple images of the scene over any arbitrary period of time and any arbitrary timing between images. This set of images is referred to as a burst set. Selection of the subjectively best image is achieved in real-time by applying a machine-learned model to the burst set. The machine-learned model of the BoB Selector is trained to select one or more subjectively best images from the burst set in a way that closely emulates human selection based on subjective subtleties of human preferences. Images automatically selected by the BoB Selector are presented to a user or saved for further processing.