A system and method for matching a pattern may include generating a set of model descriptors representative of segment features of a first imaged object. A set of query descriptors representative of segment features of a second imaged object may be generated. A first model segment may be selected to align with the query segments to determine if any of the query segments correspond with the first model segment based on respective model descriptors and query descriptors. An hypothesis may be generated by computing a transformation to match the selected first model descriptor with a query descriptor. The hypothesis may be validated by a model fitting algorithm when comparing other transformed model descriptors with other query descriptors. Based on a consensus value from the model fitting algorithm, a determination as to whether a pattern match exists between the first and second images may be made.

Data characterizing a load on a rod of a down-hole pump at different positions of a stroke of the rod can be received. An image characterizing the load of the rod at the different positions of the stroke can be determined. A defect in operating conditions of the down-hole pump can be determined. The determining can include comparing the image to a set of predetermined images with associated defects. The determined defect can be provided.

A standard image of a product to be a standard for an inspection target is displayed, to set a first region so as to surround a standard pattern in the standard image. Further, a second region for characterizing a position and a posture of the standard pattern is set in the standard image. In a first search step, a feature extracted from the first region set in the standard image is searched from an inspection target image, to roughly obtain the position and the posture of the standard pattern in the inspection target image. In the second search step, the feature extracted from the second region set in the standard image is searched from the inspection target image, to minutely obtain at least one of the position and the posture of the standard pattern in the inspection target image.

The application discloses a lip movement capturing method and device and a storage medium. The method includes: acquiring a real-time image shot by a photographic device and extracting a real-time facial image from the real-time image; inputting the real-time facial image into a pretrained lip average model and recognizing t lip feature points representative of positions of lips in the real-time facial image; and calculating a movement direction and movement distance of the lips in the real-time facial image according to x and y coordinates of the t lip feature points in the real-time facial image. According to the application, movement information of the lips in the real-time facial image is calculated according to the coordinates of the lip feature points to implement real-time capturing of movements of the lips.

A method includes receiving an image from a mammogram, removing noise from the image, computing a point of interest on the de-noised image, creating a mesoscale region of interest on the de-noised image, computing a connectivity for the mesoscale region of interest, identifying a connected component using the computed connectivity, where the connected component represents a branch of a global curvilinear structure, selecting a set of branches based on a physical property for each branch of the global curvilinear structure, pruning each branch based on an error-tolerant, adaptive polynomial fit, identifying remaining regions of interest in each pruned branch, and growing a chain formed by remaining points of interest included in the remaining regions of interest, where the chain represents a macroscopic, global curvilinear calcified arterial structure. The quantitation of the calcified arterial structures may be used as a biomarker for risk stratification of heart disease.

An ultrasound diagnostic apparatus includes: an image acquisition unit that acquires an ultrasound image by transmitting an ultrasound beam from an ultrasound probe toward a subject and receiving an ultrasound beam reflected from the subject; an image analysis unit that analyzes the acquired ultrasound image; and a movement amount detection unit that detects a movement amount of the ultrasound probe. The image analysis unit performs image analysis on an ultrasound image of a single frame in a case where the movement amount of the ultrasound probe is equal to or greater than a threshold value, and performs image analysis on ultrasound images of a plurality of frames in a case where the movement amount of the ultrasound probe is less than the threshold value.

A headphone for spatial audio rendering includes a first database having an impulse response pair corresponding to a reference speaker location. A head sensor provides head orientation information to a second database having rotation filters, the filters corresponding to different azimuth and elevation positions relative to the reference speaker location. A digital signal processor combines the rotation filters with the impulse response pair to generate an output binaural audio signal to transducers of the headphone. Efficiencies in creating impulse response or HRTF databases are achieved by sampling the impulse response less frequently than in conventional methods. This sampling at coarser intervals reduces the number of data measurements required to generate a spherical grid and reduces the time involved in capturing the impulse responses. Impulse responses for data points falling between the sampled data points are generated by interpolating in the frequency domain.

A system and method that transforms data formats into contour metrics and further transforms each contour of that mapping into contours pattern metric sets so that each metric created has a representation of one level of contour presentation, at each iteration of the learning contour identification system defined herein. This transformation of data instance to contour metrics permits a user to take relevant data of a data set, as determined by a learning contour identification system, to machines of other types and function, for the purpose of further analysis of the patterns found and labeled by said system. The invention performs with data format representations, not limited to, signals, images, or waveform embodiments so as to identify, track, or detect patterns of, amplitudes, frequencies, phases, and density functions, within the data case and then by way of using combinations of statistical, feedback adaptive, classification, training algorithm metrics stored in hardware, identifies patterns in past data cases that repeat in future, or present data cases, so that high-percentage labeling and identification is a achieved.

An approach is provided for a vertex-based evaluation of polygon similarity. The approach, for instance, involves processing, by a computer vision system, an image to generate a first set of vertices of a first polygon representing an object depicted in the image. The approach also involves for each vertex in the first set of vertices, determining a closest vertex in a second set of vertices of a second polygon, and determining a distance between said each vertex in the first set of vertices and the closest vertex in the second set of vertices. The approach further involves calculating a polygon similarity of the first polygon with respect to the second polygon based on a total of the distance determined for said each vertex in the first set of vertices normalized to a number of vertices in the first set of vertices.

Embodiments of the present disclosure disclose a method and apparatus for detecting a body. A specific embodiment of the method includes: acquiring a to-be-detected image, the to-be-detected image including a body image; importing the to-be-detected image into a pre-established body key point detection model to obtain a body frame of a body corresponding to the body image, a position of a body key point, and a judgment result of whether the body key point belongs to the body in the to-be-detected image; and outputting the body frame of the body corresponding to the body image, the position of the body key point, and the judgment result of whether the body key point belongs to the body in the to-be-detected image.