Abstract: Provided is a fingerprint recognition sensor capable of sensing a fingerprint using optical and capacitive methods, the sensor including: a capacitive fingerprint sensing unit that includes a transistor (T2) in which a flowing current is changed depending on an output voltage of a fingerprint sensing electrode capable of sensing the humans fingerprint; and an optical fingerprint sensing unit which changes the flowing current in the transistor (T2) due to a difference in reverse current of a photodiode generated by light and shade depending on existence or non-existence of the fingerprint.

Abstract: A three-dimensional (3D) image generation apparatus and method using a region extension of an object in a depth map is provided. The 3D image generation apparatus may include a discontinuity preservation smoothing filtering unit to apply a discontinuity preservation smoothing filter preserving discontinuity of a boundary or a shape of a depth image, a boundary preservation filtering unit to apply a max filter to a depth image for increasing a depth value of an object, and a rendering unit to render a two-dimensional (2D) color image and the filtered depth image and to generate a 3D image.

Abstract: A method for identifying keypoints in a digital image including a set of pixels. Each pixel has associated thereto a respective value of an image representative parameter. The method includes approximating a filtered image. The filtered image depends on a filtering parameter and includes for each pixel of the image a filtering function that depends on the filtering parameter to calculate a filtered value of the value of the representative parameter of the pixel. The approximating includes: a) generating a set of base filtered images; each base filtered image is the image filtered with a respective value of the filtering parameter; b) for each pixel of at least a subset of the set of pixels, approximating the filtering function by a respective approximation function based on the base filtered images; the approximation function is a function of the filtering parameter within a predefined range of the filtering parameter.

Abstract: According to an embodiment of the present technology, there is provided an image processing apparatus including a parallax information generation unit. The parallax information generation unit generates parallax information based on a first phase difference distribution and a second phase difference distribution, the first phase difference distribution being generated on a pixel by pixel basis for the first parallax image and the second parallax image, the second phase difference distribution being generated on a sub-pixel by sub-pixel basis based on the first phase difference distribution.

Abstract: A method and system for creating a depth signature from plural images for providing watermark information related to the images. The method comprises analyzing a pair of images, each image containing a plurality of elements, identifying a first element in one of the pair of images, identifying plural elements in the other of the pair of images. The method further comprises measuring a disparity parameter between the first element and a set of the plural elements, matching the first element from the set of plural elements, the matched second element having the smallest measured disparity parameter, and computing a signature based at least in part on the measured disparity between the first and second elements.

Abstract: A method for extraction of descriptors from video content, includes the following steps: a Key Frame Extracting step, applying a local descriptors-based approach to select pictures of the incoming video as key frames that are representative of a temporal region of the video which is visually homogeneous; a Content Analysis step, analysing the content of the key frames and classifying image patches of the key frames as interesting or not for the extraction of descriptors; a Descriptors Extracting step, extracting compact descriptors from the selected key frames, and defining a set of surrounding images also on the basis of input received from the Content Analysis step; a Temporal Coding step, multiplexing information about the time points at which said key frames have been extracted in the Key Frame Extracting step with the compact descriptors extracted in the Descriptors Extracting step, obtaining the descriptors.

Abstract: The present invention provides a method and an apparatus for real time object segmentation of 3D image data based on local feature correspondences between a plurality of views. In order to reduce the computational effort of object segmentation of 3D image data, the segmentation process is performed based on correspondences relating to local features of the image data and a depth map. In this way, computational effort can be significantly reduced and the image segmentation can be carried out very fast.

Abstract: Aspects relate to a method of generating a high-resolution image containing depth information of an object. In one aspect, the method includes downsampling a first reference image and a second reference image from a first resolution to a second resolution, wherein the first resolution is higher than the second resolution, and wherein the first reference image and the second reference image comprising a stereo image pair. The method further includes generating a depth map at the second resolution based on global minimization techniques, using the downsampled stereo image pair. The method also includes upsampling the depth map from the second resolution to the first resolution and using a guided filter to align contours of the upsampled depth map to contours of the first reference image.

Abstract: Recognition process (1) of an object (An) in a query image (2), performing a training step (6) that comprises: —providing (20) a set of training images (Ti), each training image (Ti) comprising an object tag (LOGOi); —determining (21) for each training image (Ti) of said set a plurality of first descriptors (11), each first descriptor (11) being a vector that represents pixel properties in a subregion (Sri) of the associated training image (Ti); —determining (23) a group of exemplar descriptors (111) describing the set of training images (Ti) and resulting from a selection of the first descriptors (11) based on the position of said subregion (Sri) in the associated training image (Ti) and on the pixel properties of said first descriptors (11); performing a query step (7) comprising: —receiving (30) the query image (2) and defining (31) a plurality of vectors (V?) of second descriptors (3) describing the properties of said query image (2); —determining (35) a visual similarity coefficient based on a comparison

Abstract: An example method is disclosed that includes identifying a training set of images, wherein each image in the training set has an identified bounding box that comprises an object class and an object location for an object in the image. The method also includes segmenting each image of the training set, wherein segments comprise sets of pixels that share visual characteristics, and wherein each segment is associated with an object class. The method further includes clustering the segments that are associated with the same object class, and generating a data structure based on the clustering, wherein entries in the data structure comprise visual characteristics for prototypical segments of objects having the object class and further comprise one or more potential bounding boxes for the objects, wherein the data structure is usable to predict bounding boxes of additional images that include an object having the object class.

Abstract: An abnormal pattern analysis method includes determining a service application associated with analysis data, selecting at least one abnormal pattern analysis module in an abnormal pattern analysis framework based on the determined service application and performing an analysis for the analysis data through the selected at least one abnormal pattern analysis module to detect an abnormal pattern.

Abstract: In a boundary line recognition apparatus, based on luminance levels of an image captured by a camera, candidate edge points of a boundary line sectioning a travel road are extracted, and a candidate line of the boundary line is extracted. An apparent width of the candidate line on an image is calculated, from a width of the candidate line in a horizontal direction of the image and an angle of the candidate line relative to a vertical direction of the image. A probability of a candidate line being a boundary line is calculated to be higher, as a degree of the candidate line having characteristics as a boundary line is higher. The calculated probabilities are integrated in respect of a plurality of characteristics to recognize a boundary line. The characteristics include a ratio of the calculated apparent width to an image blur degree is larger than a predetermined value.

Abstract: The present invention relates to an image processing device and method capable of suppressing deterioration in encoding efficiency. A decoded pixel classifying unit 152 and input pixel classifying unit 153 perform class classification of each of the macroblocks of a decoded image or input image, based on orthogonal transform sizes read out from an orthogonal transform size buffer 151. A 4×4 block coefficient calculating unit 154 and 8×8 block coefficient calculating unit 155 calculate filter coefficients in each orthogonal transform block such that residual is the smallest. A pixel classifying unit 161 of a loop filter 113 performs class classification of each of the macroblocks of the decoded image for each of the orthogonal transform block sizes. A filter unit (4×4) 162 and filter unit (8×8) 163 apply appropriate filter coefficients to their respectively corresponding orthogonal transform blocks, and perform filter processing. The present invention can be applied to, for example, an image processing device.

Abstract: A method for a system to generate a recipe for performing wafer alignment, includes: generating first and second alignment data sets, the first alignment data set including image information regarding a first site on a wafer and coordinates of characteristic points at the first site, and the second alignment data set including image information regarding a second site different than the first site on the wafer and coordinates of characteristic points at the second site; and saving the generated first and second alignment data sets as a recipe for wafer alignment; wherein the generating of the first alignment data set includes: selecting, as the first site, a site including a characteristic pattern on the wafer; determining first and second characteristic points at the selected site; and recording coordinates of the determined first and second characteristic points.

Abstract: An image pattern recognition method detects a pattern in a sequence of video images or individual images from detected interest points. Feature vectors are extracted with video data from video regions around the interest points. A forest of decision trees is used to compute a set of bin values in histograms with bins corresponding to leaf nodes of the decision trees. Each bin value is a sum of contributions computed for individual interest points. Non-binary decision functions are used to compute the contributions and node dependent scale values are used to compute the arguments of the non-binary decision functions. The node dependent scale values may be computed from standard deviations of feature values found for the nodes, multiplied by a factor that is common to the nodes. This factor may be adjusted by feedback so that it can be set differently for different detection classes.

Abstract: The present application discloses a method and a device for image processing. The method comprises: performing facial recognition on an image to be processed; determining a skin area to be processed in a facial area according to detection results; determining the locations of skin blemishes in the skin area to be processed; removing the skin blemishes in the facial area according to the determined locations of the skin blemishes in the skin area to be processed to obtain a processed image. The method and the device for image processing can modify people's skin in an image automatically, that is to say, both light-colored blemishes and obvious problems on the skin are removed. In contrast to the solutions for removing skin blemishes manually in various existing APPs for skin beautification, the method provided by the present application provides a better experience for users.

Abstract: An overlapping device which is configured to detect the center positions of a substrate and a support which are held in a center position detecting portion, carry the substrate and the support from the center position detecting portion to an overlapping portion, and overlap the substrate and the support such that the detected center positions of the substrate and the support are overlapped in the overlapping portion.

Abstract: The invention discloses a method for correcting aero-optical thermal radiation noise, comprising steps of: pretreating a degraded image to obtain a multi-scale degraded image group, conducting iteration process of obtaining an optimal solution by using a last scale estimation result as an original value of next scale estimation according to the multi-scale degraded image group, thereby facilitating original-scale bias field estimation, and restoring the degraded image according to the original-scale bias field estimated value thereby obtaining an image after aero-optical thermal radiation noise correction. The invention also discloses a system for correcting aero-optical thermal radiation noise. The invention is capable of solving problems with conventional methods, comprising poor correction effect, high complexity, and incapability in correcting the thermal radiation noise at an image level, and applicable to restoration of an image with aero-optical thermal radiation noise.

Abstract: A method for creating a face replacement database includes steps of creating a face database for storing a plurality of replaced images with a face image rotation angle by using a method for estimating a 3D vector angle from a 2D face image, and defining a region to be replaced in the replaced image. The method for estimating a 3D vector angle from a 2D face image includes the steps of creating a feature vector template; detecting a corner of eye and mouth in a face image; defining a sharp point in a vertical direction of the quadrilateral plane, and converting the vertices into 3D coordinates; computing the four vectors from the sharp point to the four vertices to obtain a vector set, and matching the vector set with the feature vector model to obtain an angle which is defined as a rotation angle of the input face image.

Abstract: A method for automatic video face replacement includes steps of capturing a face image, detecting a rotation angle of the face image, defining a region to be replaced in the face image, and pasting a region to be replaced of one of the replaced images having the corresponding rotation angle of the face image into a target replacing region. Therefore, the region to be replaced of a static or dynamic face image can be replaced by a replaced image quickly by a single camera without requiring a manual setting of the feature points of a target image. These methods support face replacement at different angles and compensate the color difference to provide a natural look of the replaced image.