Abstract: A touch screen and a terminal are disclosed. The touch screen comprises: a touch panel, at least three pick-up devices, and a data calculation module, whereof: the touch panel covers the pick-up devices and is used to provide a touch operation platform; the pick-up devices are used to detect effective sound signals produced by touch operations, and to record detection time; and the data calculation module is used to ascertain, on the basis of recorded detection times, the time differential of the effective sound signal detected by each pick-up device, and to calculate the position of a touch-point by using the time differential and the path differential from a touch-point to each pick-up device.

Abstract: A touch screen and a terminal are disclosed. The touch screen comprises: a touch panel, at least three pick-up devices, and a data calculation module, whereof: the touch panel covers the pick-up devices and is used to provide a touch operation platform; the pick-up devices are used to detect effective sound signals produced by touch operations, and to record detection time; and the data calculation module is used to ascertain, on the basis of recorded detection times, the time differential of the effective sound signal detected by each pick-up device, and to calculate the position of a touch-point by using the time differential and the path differential from a touch-point to each pick-up device.

Abstract: Provided are an object verification apparatus and method. The object verification apparatus includes a matching unit performing a plurality of different matching algorithms on a query image and generating a plurality of scores; a score normalization unit normalizing each of the generated scores to be adaptive to the query image; a weight estimation unit estimating weights of the normalized scores based on the respective matching algorithms applied; and a score fusion unit fusing the normalized scores by respectively applying the weights estimated by the weight estimation unit to the normalized scores.

Abstract: An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen.

Abstract: There is provided a method of estimating disparity for 3D object recognition. The method includes obtaining a plurality of images having different resolutions for the stereo image, estimating a disparity map for a lowest-resolution level image, estimating a coarse disparity map for an upper resolution level image by using the disparity, obtaining a fine disparity map for the upper resolution level image by using the coarse disparity, and outputting the fine disparity map as a final disparity map for the stereo image if the upper resolution level image has a resolution of a highest level.

Abstract: A face detection method and apparatus using a level-set method.

Abstract: A image verification method, medium, and apparatus using a local binary pattern (LBP) discriminant technique. The verification method includes generating a kernel fisher discriminant analysis (KFDA) basis vector by using the LBP feature of an input image, obtaining a Chi square inner product by using the LBP feature of an image registered in advance and a kernel LBP feature and projecting to a KFDA basis vector, obtaining a Chi square inner product by using the LBP feature of a query image and a kernel LBP feature and projecting to a KFDA basis vector, and obtaining the similarity degree of the target image and the query image that are obtained as Chi square inner product results, and projected to the KFDA basis vector. According to the method, medium, and apparatus, the KFDA based LBP shows superior performance over conventional LBP, KFDA, and biometric experimentation environment (BEE) baseline algorithms.

Abstract: A face descriptor generating method and apparatus and face recognition method and apparatus using extended local binary pattern (LBP) are provided. Since LBP features are selected by performing a supervised learning process on the extended LBP features and the selected extended LBP features are used in face recognition, it is possible to reduce errors in face recognition or identity verification and to increase face recognition efficiency. In addition, the extended LBP features are used so that it is possible to overcome the problem of time-consumption of the process.

Abstract: A method of extracting a certain area from a digital image, the method including: combining image information and shape information based on an input image and prior shape information; and extracting a certain area from the input image by using the image information.

Abstract: A global localization apparatus, medium, and method, with the global localization method including selecting one from a plurality of samples and shifting the selected sample according to a movement of a robot, generating a new sample within a predetermined range of the shifted sample, determining either the shifted sample or the new sample as a next sample at a next time step according to a predetermined condition for the shifted sample and the next sample, repeating for all the samples, and estimating a next position of the robot according to positions of the next samples when the number of next samples is equal to or larger than the maximum number of samples.

Abstract: Provided are an object verification apparatus and method. The object verification apparatus includes a matching unit performing a plurality of different matching algorithms on a query image and generating a plurality of scores; a score normalization unit normalizing each of the generated scores to be adaptive to the query image; a weight estimation unit estimating weights of the normalized scores based on the respective matching algorithms applied; and a score fusion unit fusing the normalized scores by respectively applying the weights estimated by the weight estimation unit to the normalized scores.

Abstract: A image verification method, medium, and apparatus using a local binary pattern (LBP) discriminant technique. The verification method includes generating a kernel fisher discriminant analysis (KFDA) basis vector by using the LBP feature of an input image, obtaining a Chi square inner product by using the LBP feature of an image registered in advance and a kernel LBP feature and projecting to a KFDA basis vector, obtaining a Chi square inner product by using the LBP feature of a query image and a kernel LBP feature and projecting to a KFDA basis vector, and obtaining the similarity degree of the target image and the query image that are obtained as Chi square inner product results, and projected to the KFDA basis vector. According to the method, medium, and apparatus, the KFDA based LBP shows superior performance over conventional LBP, KFDA, and biometric experimentation environment (BEE) baseline algorithms.

Abstract: A method, apparatus, and medium for removing shading of an image are provided. The method of removing shading of an image includes: smoothing an input image; performing a gradient operation for the input image; performing normalization using the smoothed image and the images for which the gradient operation is performed; and integrating the normalized images. The apparatus for removing shading of an image includes: a smoothing unit smoothing an input image using a predetermined smoothing kernel; a gradient operation unit performing a gradient operation for the input image using a predetermined gradient operator; a normalization unit performing normalization using the smoothed image and the images for which the gradient operation is performed; and an image integration unit integrating the normalized images.

Abstract: A face detection method and apparatus using a level-set method.

Abstract: There is provided a method of estimating disparity for 3D object recognition. The method includes obtaining a plurality of images having different resolutions for the stereo image, estimating a disparity map for a lowest-resolution level image, estimating a coarse disparity map for an upper resolution level image by using the disparity, obtaining a fine disparity map for the upper resolution level image by using the coarse disparity, and outputting the fine disparity map as a final disparity map for the stereo image if the upper resolution level image has a resolution of a highest level.

Abstract: An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen.

Abstract: A global localization apparatus, medium, and method, with the global localization method including selecting one from a plurality of samples and shifting the selected sample according to a movement of a robot, generating a new sample within a predetermined range of the shifted sample, determining either the shifted sample or the new sample as a next sample at a next time step according to a predetermined condition for the shifted sample and the next sample, repeating for all the samples, and estimating a next position of the robot according to positions of the next samples when the number of next samples is equal to or larger than the maximum number of samples.