An analyzing method and an analyzing system for graphics process are provided. The analyzing method includes the following steps. A graphics application program is provided and a plurality of graphics parameters of the graphics application program are obtained. The graphics application program is classified to be at least one of a plurality of groups according to the graphics parameters. A plurality weighting coefficients are obtained. A total loading of a graphics processing unit for performing the graphics application program is calculated according to the weighting coefficients and the graphics parameters.

A method includes obtaining from an online social media site a plurality of instances of images of objects associated with a person; analyzing with a data processor the plurality of instances of the images with a plurality of predetermined style classifiers to obtain a score for each image for each style classifier; and determining with the data processor, based on the obtained scores, a likely preference of the person for a particular style of object. The plurality of instances of images of objects associated with the person can be images that were posted, shared or pinned by person, and images that the person expressed a preference for. In a non-limiting embodiment the object is clothing, and the style can include a fashion style or fashion genre including color preferences. A system and a computer program product to perform the method are also disclosed.

One or more systems and/or methods for providing content to a user are provided. An image, associated with a user, may be evaluated utilizing an image classifier to identify an object within the image. The object may be utilized to identify a predicted class for the user. In an example, the predicted class may correspond to a life event (e.g., graduating college, having a baby, buying a house, etc.) and/or a life stage (e.g., adolescence, retirement, etc.). Locational information (e.g., a geotag) for the image may be evaluated to determine an image location (e.g., a location where the image was generated). Responsive to the image location corresponding to a home location of the user, the predicted class may be determined to be a class associated with the user. Content (e.g., promotional content) may be selected from a content repository based upon the class and subsequently provided to the user.

A system, method and apparatus for detecting facial expressions according to EMG signals.

A system, method and apparatus for detecting facial expressions according to EMG signals.

There is provided an information processing apparatus. A multidimensional input vector is input. For each dimension of the input vector, a function value of a single-variable function with an element of the dimension as a variable is derived, by referring to a lookup table indicating a correspondence between a variable and a function value of the single-variable function. A product of the single-variable functions approximates a function value of a multiple-variable function. For each dimension of the input vector, a product of the function value derived by the derivation unit and a predetermined coefficient corresponding to the dimension is calculated. A value calculated using the total of the products calculated by the product calculation unit for each dimension of the input vector is output as a classification index indicating a class of the input vector.

A method, apparatus, and program product facilitate the automation of an oil & gas process, e.g., a drilling process, through the use of a dynamic phase machine incorporating multiple autonomous agents.

Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

Captured samples of a physical structure or other scene are mapped to a predetermined multi-dimensional coordinate space, and spatially-adjacent samples are organized into array cells representing subspaces thereof. Each cell is classified according to predetermined target-identifying criteria for the samples of the cell. A cluster of spatially-contiguous cells of common classification, peripherally bounded by cells of different classification, is constructed, and a boundary demarcation is defined from the peripheral contour of the cluster. The boundary demarcation is overlaid upon a visual display of the physical scene, thereby visually demarcating the boundaries of a detected target of interest.