Abstract: A user identity determining method includes: acquiring target multidimensional feature information of a target user, wherein the target multidimensional feature information includes at least two types of feature information in at least one of biometric feature information or non-biometric feature information; comparing the target multidimensional feature information with multidimensional feature information of a plurality of designated users, respectively, to obtain a comparison result; and determining an identity of the target user based on the comparison result.

Abstract: The present disclosure provides image acquisition methods, apparatuses, systems and electronic devices. One image acquisition method includes: acquiring an initial face image of a user by a first image acquisition apparatus; controlling a second image acquisition apparatus to acquire an eye print image of the user according to an acquisition parameter, the acquisition parameter being determined based on the initial face image; and synthesizing the initial face image and the eye print image into a target face image of the user.

Abstract: There is provided an encoding and decoding method and an information recognition device using the same. A code block includes a center coding region and a peripheral coding region arranged around the center coding region. The encoding and decoding method uses the feature of at least one microdot included in the center coding region as codes. The encoding and decoding method uses the feature of at least one microdot included in the peripheral coding region as codes. The encoding and decoding method uses the relative feature between the center coding region and the peripheral coding region as codes. The information recognition device compares the read feature with pre-stored features to decode information such as position codes, object codes, parameter codes and control codes.

Abstract: A method and a signal processor for receiving a data stream comprising at least two distinct sets of encoded data, at least one set of which is relative to transient/stochastic components of a signal. Based at least in part on the distinct sets of encoded data, the signal processor decodes and reconstructs a corresponding rendition of signal for each set of the encoded data. The distinct sets of renditions of signal are then combined into a single rendition of reconstructed signal.

Abstract: A plurality of illumination elements configured to irradiate an inspection target region with illumination light obliquely at an identical angle in respective directions different from each other and equiangularly spaced from each other around an image capturing part are sequentially turned on and off. The image capturing part generates a plurality of pieces of captured image data by capturing an image of the target region in a normal direction when each of the plurality of illumination elements is turned on. A determination image generation part generates minimum luminance image data in which a minimum value among luminance values of the plurality of pieces of captured image data at an identical pixel position is set as a luminance value at the pixel position, and then generates determination image data based on the minimum luminance image data. A defect determination part determines existence of a defect based on the determination image data.

Abstract: An expression recognition method under a natural scene comprises: converting an input video into a video frame sequence in terms of a specified frame rate, and performing facial expression labeling on the video frame sequence to obtain a video frame labeled sequence; removing natural light impact, non-face areas, and head posture impact elimination on facial expression from the video frame labeled sequence to obtain an expression video frame sequence; augmenting the expression video frame sequence to obtain a video preprocessed frame sequence; from the video preprocessed frame sequence, extracting HOG features that characterize facial appearance and shape features, extracting second-order features that describe a face creasing degree, and extracting facial pixel-level deep neural network features by using a deep neural network; then, performing vector fusion on these three obtain facial feature fusion vectors for training; and inputting the facial feature fusion vectors into a support vector machine for expre

Abstract: A liveness detection method includes: controlling a display screen to display a color according to a predetermined color display sequence, the color display sequence including at least two different colors; capturing an image of a target object in a color display process of the display screen; acquiring a color change sequence of a face of the target object in the image over time; and determining whether the target object is live based on a matching relationship between the color display sequence and the color change sequence.

Abstract: The present disclosure provides image acquisition methods, apparatuses, systems and electronic devices. One image acquisition method includes: acquiring an initial face image of a user by a first image acquisition apparatus; controlling a second image acquisition apparatus to acquire an eye print image of the user according to an acquisition parameter, the acquisition parameter being determined based on the initial face image; and synthesizing the initial face image and the eye print image into a target face image of the user.

Abstract: Defects from a hot scan can be saved, such as on persistent storage, random access memory, or a split database. The persistent storage can be patch-based virtual inspector virtual analyzer (VIVA) or local storage. Repeater defect detection jobs can determined and the wafer can be inspected based on the repeater defect detection jobs. Repeater defects can be analyzed and corresponding defect records to the repeater defects can be read from the persistent storage. These results may be returned to the high level defect detection controller.

Abstract: A learning method is performed by a computer. The method includes: inputting a first image to a model, which outputs, from an input image, candidates for a specific region and confidences indicating probabilities of the respective candidates being the specific region, to cause the model to output a plurality of candidates for the specific region and confidences for the respective candidates; calculating a first value for each of candidates whose confidences do not satisfy a certain criterion among the candidates output by the model, the first value increasing as the confidence increases; calculating a second value obtained by weighting the first value such that the second value decreases as the confidence increases; and updating the model such that the second value decreases.

Abstract: There may be provided a non-uniform Benes network, that may include a first Benes network portion that has a first number (k) of first inputs and k first outputs; a second Benes network portion that has a second number (j) of second inputs and j second outputs; wherein j is smaller than k; and a set of multiplexers that are coupled between a set of switches of an intermediate layer of the first Benes network portion and a first layer of the second Benes network layer.

Abstract: A data capture component receives a video stream comprising a plurality of frames, wherein each frame comprises a data field. One or more text regions in a selected frame of the plurality of frames are identified. One of the one or more identified text regions that corresponds to a set of attributes associated with the data field are selected. The data of the one of the one or more identified text regions of the selected frame are compared with data of one or more text regions of a subsequent frame. Responsive to determining that the data of the one or more text regions of the subsequent frame is a closer match to the set of attributes, the data of the one of the one or more identified text regions of the selected frame are updated. The data of the one of the one or more identified text regions is then provided to a client device.

Abstract: Techniques are provided for synthesis of transformed image views, based on a reference image, using depth information. The transformed image views may simulate a change in position or focal length of a camera that produced the reference image. An example system includes an image transformation circuit configured to transform the reference image corresponding to a first viewpoint, to a transformed image corresponding to a second viewpoint. The system also includes an inverse warping circuit configured to calculate a mapping from the pixels of the transformed image to corresponding pixels of the reference image. The system further includes a hole detection circuit configured to detect holes in the transformed image based on depth discontinuities between the reference and transformed images; and a hole filling circuit configured to in-fill the detected holes using a sampling of selected neighboring pixels from the reference image, to synthesize a view based on the transformed image.

Abstract: An example system includes a processor to receive a number of video frames. The processor is to apply a grid to each video frame of the number of video frames and generate features for each cell in the grid. The processor is to calculate distances between matching regions of two consecutive frames. The processor is to apply max pooling followed by min pooling in horizontal regions of the number of video frames. The processor is to apply max pooling followed by min pooling in vertical regions of the number of video frames. The processor is to detect an application switch in response to detecting both a horizontal change and a vertical change between the two consecutive video frames exceed a threshold.

Abstract: An identification method includes: controlling at least one camera to acquire a face image and an eye image of a target object, wherein the eye image includes at least one of an iris feature or an eye-print feature; and identifying the target object based on the face image and the eye image.

Abstract: Devices, systems, and methods obtain a video of a device, wherein the device includes one or more light emitters that are visible in the video; input the video to a first machine-learning model and executing the first machine-learning model, wherein the first machine-learning model outputs a time series of light-emitting states that indicate respective light-emitting states of the light emitters at respective times in the time series; and input the time series of light-emitting states to a second machine-learning model and executing the second machine-learning model, wherein the second machine-learning model outputs a status of the device.

Abstract: A method includes one or more processors of a payload releasably coupled to a carrier detecting a deviation of a target from an expected target position within an image captured by an image sensor of the payload, and generating one or more control signals for the carrier based at least in part on the detected deviation of the target. The one or more control signals cause the carrier to change a pose of the payload so as to reduce the detected deviation in a subsequent image captured by the image sensor.

Abstract: The present disclosure provides a method, a device and a system for processing image tagging information. The method includes the following. A captured image and a capturing position are acquired from a terminal. Object recognition is applied to the captured image to determine a first object presented by the captured image. A first target image presenting the first object is searched for among one or more historical images associated with the capturing position. When the first target image is searched for, it is determined that the first object is duplicated with tagging information of an electronic map. When the first target image is not searched for, a corresponding position of the first object on the electronic map is tagged.

Abstract: According to aspects of the present disclosure, methods, systems, and media for processing an image are provided. A system may include at least one computer-readable storage medium including a set of instructions for processing an original image, and at least one processor in communication with the at least one computer-readable storage medium. When executing the set of instructions, the system is directed to obtain a first luminance image of the original image; decompose the first luminance image to provide a plurality of first decomposed images; adjust pixel frequencies in at least some of the plurality of first decomposed images to generate a plurality of second decomposed images; generate a second luminance image of the original image based on the plurality of second decomposed images; and determine a final image of the original image based on the first luminance image, the second luminance image, and the original image.

Abstract: A liveness detection method includes: controlling a display screen to display a color according to a predetermined color display sequence, the color display sequence including at least two different colors; capturing an image of a target object in a color display process of the display screen; acquiring a color change sequence of a face of the target object in the image over time; and determining whether the target object is live based on a matching relationship between the color display sequence and the color change sequence.