Abstract: This invention relates to a corrosion protection pretreatment agent, which comprises a solvent, at least one inorganic inhibitor, at least one silane compound, and a bi-functional epoxy resin precursor (C); the invention also provides a method for treating metallic surface by using the corrosion protection pretreatment agent, an article which has at least one portion comprising a surface treated by the corrosion protection pretreatment agent that demonstrates improved corrosion resistance, adhesion properties and impact resistance.

Abstract: This invention relates to a protection coating composition, which comprises a) at least one resin, the resin being selected from a group consisting of polyurethane resin (A), polyurethane-acrylic resin(B), b) a solvent, c) a bi-functional epoxy resin precursor (C), the bi-functional epoxy resin precursor (C) being represented by the following formulas: R1—Y—[CH2—CH(OH)—R3—CH(OH)—CH2—X—R4—X]n—R2. This invention also provides a method for treating metallic surface by using the protection coating composition, an article which has at least one portion that comprises side treated surface by the protection coating composition, which demonstrates improved corrosion resistance, adhesion properties and anti-aging adhesion properties.

Abstract: A search apparatus for searching media data for a target region that matches query data includes a first feature extraction unit configured to extract a first feature vector from the query data using a first trained neural network; a second feature extraction unit configured to obtain a first region from the media data and extract a second feature vector from the first region using a second trained neural network; a localization unit configured to determine a candidate for the target region using a third trained neural network, based on the first feature vector, the second feature vector, and the first region or a location of the first region; and a control unit configured to repeat the operations of the second feature extraction unit and the localization unit until a predetermined condition is satisfied, by using the determined candidate for the target region as the first region to be used by the second feature extraction unit.

Abstract: One aspect of the present invention is an image adjustment method including: deriving a statistic from a first pixel having a first luminance and a second pixel having a second luminance co-occurring in a local region in an input image, and deriving a two-dimensional luminance histogram having the statistic as a numerical value corresponding to the first luminance and the second luminance; deriving a numerical value by weighting the two-dimensional luminance histogram by using a weighting coefficient determined according to a third luminance greater than the first luminance and less than or equal to the second luminance, and deriving a one-dimensional luminance histogram having the numerical value as a numerical value corresponding to the third luminance; and deriving a luminance conversion function to cause the one-dimensional luminance histogram derived to be converted into a predetermined histogram, and converting a luminance of each of pixels of the input image by using the luminance conversion function.

Abstract: An image adjustment device includes: an illumination component derivation unit that derives an illumination component of a grayscale image; a reflectance component derivation unit that derives a reflectance component image that is a resulting image in which the illumination component is removed from the grayscale image; a contrast component derivation unit that derives a contrast component based on a contrast value between a pixel of the reflectance component image and a peripheral area of the pixel; a histogram derivation unit that derives a luminance histogram of the grayscale image weighted according to the contrast value for each pixel of the contrast component; a conversion function derivation unit that derives a luminance conversion function for converting a luminance such that a luminance histogram of a converted grayscale image in which the grayscale image is converted by the luminance conversion function and a predetermined histogram are matched with or similar to each other; and a luminance conversi

Abstract: A method for converting state space representation (SSR) information to observation space representation (OSR) information includes: obtaining the SSR information, obtaining the OSR information, obtaining information of a virtual observation distance, and obtaining delay information of a troposphere and delay information of an ionosphere. A device for converting SSR information to OSR information includes: a satellite antenna, a global navigation satellite system (GNSS) board, a radio antenna, a mobile network module and antenna, a Bluetooth module and antenna, a Wi-Fi module and antenna, a status indicator light, a plurality of output interfaces, and a power supply unit. A conversion algorithm is realized for converting SSR information to OSR information, and the converted OSR information follows the international standard protocols and can be received by most GNSS receivers. A conversion device is developed based on the aforementioned method.

Abstract: Provided herein is a bi-phase composition comprising: (A) a continuous aqueous phase; (B) an oil phase comprising an unstable active agent, and (C) hydrophobic particles adhered on the surface of the oil phase.

Abstract: The purpose of the present invention is to enable learning of a neural network for extracting features of images having high robustness from an undiscriminating image region while minimizing the number of parameters of a pooling layer. A parameter learning unit 130 learns parameters of each layer in a convolutional neural network configured by including a fully convolutional layer for performing convolution of an input image to output a feature tensor of the input image, a weighting matrix estimation layer for estimating a weighting matrix indicating a weighting of each element of the feature tensor, and a pooling layer for extracting a feature vector of the input image based on the feature tensor and the weighting matrix.

Abstract: An image adjustment device includes: an illumination component derivation unit that derives an illumination component of a grayscale image; a reflectance component derivation unit that derives a reflectance component image that is a resulting image in which the illumination component is removed from the grayscale image; a contrast component derivation unit that derives a contrast component based on a contrast value between a pixel of the reflectance component image and a peripheral area of the pixel; a histogram derivation unit that derives a luminance histogram of the grayscale image weighted according to the contrast value for each pixel of the contrast component; a conversion function derivation unit that derives a luminance conversion function for converting a luminance such that a luminance histogram of a converted grayscale image in which the grayscale image is converted by the luminance conversion function and a predetermined histogram are matched with or similar to each other; and a luminance conversi

Abstract: A method for converting state space representation (SSR) information to observation space representation (OSR) information includes: obtaining the SSR information, obtaining the OSR information, obtaining information of a virtual observation distance, and obtaining delay information of a troposphere and delay information of an ionosphere. A device for converting SSR information to OSR information includes: a satellite antenna, a global navigation satellite system (GNSS) board, a radio antenna, a mobile network module and antenna, a Bluetooth module and antenna, a Wi-Fi module and antenna, a status indicator light, a plurality of output interfaces, and a power supply unit. A conversion algorithm is realized for converting SSR information to OSR information, and the converted OSR information follows the international standard protocols and can be received by most GNSS receivers. A conversion device is developed based on the aforementioned method.

Abstract: A search apparatus for searching media data for a target region that matches query data includes a first feature extraction unit configured to extract a first feature vector from the query data using a first trained neural network; a second feature extraction unit configured to obtain a first region from the media data and extract a second feature vector from the first region using a second trained neural network; a localization unit configured to determine a candidate for the target region using a third trained neural network, based on the first feature vector, the second feature vector, and the first region or a location of the first region; and a control unit configured to repeat the operations of the second feature extraction unit and the localization unit until a predetermined condition is satisfied, by using the determined candidate for the target region as the first region to be used by the second feature extraction unit.

Abstract: The present invention provides a two-component foamable silicone composition comprising: a component A comprising (a) at least one polyorganosiloxane having at least one vinyl group, (b) at least one chemical blowing agent, and (c) at least one catalyst; and a component B comprising (d) at least one polyorganosiloxane having at least one vinyl group, and (e) at least one polyorganosiloxane having at least one —SiH group, and at least one expandable graphite in an amount of less than 10% by weight, based on the total weight of the component B, which exhibits excellent flame retardancy, good sealing performance, high compression rate, low gasket density, good dispensing continuity and good cell structure. The present invention also provides a preparing method and use thereof.

Abstract: The purpose of the present invention is to enable learning of a neural network for extracting features of images having high robustness from an undiscriminating image region while minimizing the number of parameters of a pooling layer. A parameter learning unit 130 learns parameters of each layer in a convolutional neural network configured by including a fully convolutional layer for performing convolution of an input image to output a feature tensor of the input image, a weighting matrix estimation layer for estimating a weighting matrix indicating a weighting of each element of the feature tensor, and a pooling layer for extracting a feature vector of the input image based on the feature tensor and the weighting matrix.

Abstract: An image collation device includes: a tentative matching point extraction unit configured to extract a plurality of tentative matching points that are pairs of corresponding feature points between a pair of input images; and a spatial verification unit configured to evaluate a degree of similarity between the pair of input images on the basis of all of a limiting conditions regarding consistency of a scale factor, a limiting conditions regarding consistency of a rotational angle, and a limiting conditions regarding consistency of a relative positional relationship according to a comparison of or a difference between geometric transformation parameters of the tentative matching points for each of the pairs of tentative matching points that is a combination of the tentative matching points extracted by the tentative matching point extraction unit.

Abstract: An image collation device includes: a tentative matching point extraction unit configured to extract a plurality of tentative matching points that are pairs of corresponding feature points between a pair of input images; and a spatial verification unit configured to evaluate a degree of similarity between the pair of input images on the basis of all of a limiting conditions regarding consistency of a scale factor, a limiting conditions regarding consistency of a rotational angle, and a limiting conditions regarding consistency of a relative positional relationship according to a comparison of or a difference between geometric transformation parameters of the tentative matching points for each of the pairs of tentative matching points that is a combination of the tentative matching points extracted by the tentative matching point extraction unit.