Abstract: An apparatus and method for detecting a fake fingerprint is disclosed. The apparatus may divide an input fingerprint image into blocks, determine an image quality assessment (IQA) value associated with each block, determine a confidence value based on the IQA values using a confidence determination model, and determine whether an input fingerprint in the input fingerprint image is a fake fingerprint based on the determined confidence value.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include determining priority values associated with collapse of each edge of a plurality of edges. The method may further include selecting a first edge from the plurality of edges, determining adjusted priority values of edges in a vicinity of the selected first edge, selecting a second edge from remaining edges of the plurality of edges after the selecting of the first edge, and collapsing the selected edges such that vertex split information is generated that is based on the collapsing of the selected edges. In some implementations, the method may further include entropy encoding of the vertex split information.

Abstract: Four computerized machine learning methods for deep semantic segmentation are fast machine learning method, active machine learning method, optimal machine learning method, and optimal transfer learning method. The fast machine learning method performs a fast deep semantic segmentation learning on training images to generate a deep model. The active machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an active deep semantic segmentation learning to generate a second deep model. The optimal machine learning method performs a fast deep semantic segmentation learning on initial training images to generate a first deep model and then an optimal deep semantic segmentation learning to generate a second deep model. The optimal transfer learning method applies a pre-trained first deep model on transfer training images and then an optimal deep semantic segmentation transfer learning to generate a second deep model.

Abstract: An image processing method for obtaining a panoramic image includes obtaining a first image and a second image by a camera unit, and obtaining location information between the viewpoints of the first image and the second image by a gyro. First features of the first image and second features of the second image are obtained according to a feature-capturing algorithm when pixel brightness differences exceed a threshold. Predicted features in the second imam are obtained by the location information, and predicted features that exceed a predetermined distance from the second features are discarded. A panorama image is obtained by combining the first image and the second image according to the first features and the selected second features.

Abstract: The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.

Abstract: A method of generating a template image includes: receiving an input from a user representing identifications of an object in different respective slices of a volumetric image; using the input to determine a volume-of-interest (VOI) that includes voxels in a subset of the volumetric image; and determining the template image using at least some of the voxels in the VOI, wherein the act of determining the template image comprises performing a forward projection of the at least some of the voxels in the VOI using a processor. An image processing method includes: obtaining a volumetric image; performing forward projection of voxels in the volumetric image from different positions onto a first plane using a processor; and summing projections on the first plane resulted from the forward projection from the different positions to create a first image slice in the first plane.

Abstract: Disclosed is a method including receiving an input image including a human, predicting, based on a convolutional neural network that is trained using examples consisting of pairs of sensor data, a corresponding body shape of the human and utilizing the corresponding body shape predicted from the convolutional neural network as input to another convolutional neural network to predict additional body shape metrics.

Abstract: Described is a multiple-camera system and process for detecting a user within a materials handling facility and tracking a position of the user as the user moves through the materials handling facility. In one implementation, a plurality of depth sensing cameras are positioned above a surface of the materials handling facility and oriented to obtain an overhead view of the surface of the materials handling facility, along with any objects (e.g., users) on the surface of the materials handling facility. The depth information from the cameras may be utilized to detect objects on the surface of the materials handling facility, track a movement of those objects and determine if those objects are users.

Abstract: A computer-implemented method includes selecting a kernel and kernel parameters for a first Support Vector Machine (SVM) model, testing the SVM model on a feature matrix T of n feature vectors of length m to produce false positive (FP) data set and false negative (FN) data set by a computer processor, wherein n and m are integer numbers, copying the feature matrix T to produce a feature matrix T_best, and checking if a ratio (T_best sample number)/(SVM support vector number) is above a threshold for the SVM model on T_best. If the ratio is above the threshold, SVM predictions are performed using the SVM model on the feature matrix T_best. The first SVM model can be used classify the faces or the objects in the images. An image-product design can be created based on the faces or the objects in the images classified by the first SVM model using the feature matrix T_best.

Abstract: An inspection apparatus includes circuitry to acquire a reference image used for inspecting a printed matter, acquire a scanned image by scanning the printed matter, generate a difference image indicating a difference between the acquired reference image and the acquired scanned image, extract an edge region from the reference image, detect a proximity region located near the edge region, correct an inspection threshold to be applied to a pixel defining the edge region based on a density difference and a distance between the pixel defining the edge region and an influencing pixel set the proximity region, and an inspection threshold to be applied to a pixel defining the proximity region based on a density difference and a distance between the pixel defining the proximity region and an influencing pixel in the edge region, and inspect the printed matter based on the generated difference image and the corrected inspection threshold.

Abstract: A fingerprint sensor is disclosed. The fingerprint sensor includes a sensor plate, a voltage source, a control circuit and a first conductive plate. The sensor plate detects a first capacitance in response to a touch event on the fingerprint sensor. The voltage source establishes a predetermined voltage difference between a first node and a second node. The control circuit, connected to the sensor plate via the first node, charges a first capacitor associated with the first capacitance during a charge period, and discharges the first capacitor during a discharge period. The first conductive plate, separated from the sensor plate, is coupled with the voltage source via the second node. During the charge period and the discharge period of the control circuit, a voltage difference between the sensor plate and the first conductive plate is maintained at the predetermined voltage difference.

Abstract: A video analysis system includes: a video data acquiring means that acquires video data; a moving object detecting means that detects a moving object from video data acquired by the video data acquiring means, by using a moving object detection parameter, which is a parameter for detecting a moving object; an environment information collecting means that collects environment information representing an external environment of a place where the video data acquiring means is installed; and a parameter changing means that changes the moving object detection parameter used when the moving object detecting means detects a moving object, on the basis of the environment information collected by the environment information collecting means.

Abstract: Provided is a smart-security digital system that enables, when there is a habitual shoplifter or suspicious behavior person, an employee or the like close to the habitual shoplifter or suspicious behavior person to quickly rush to the scene and prevent an act of shoplifting.

Abstract: A computer-implemented method is disclosed for image recognition and other applications. The method employs an SVM model and can reduce false negatives and increase recognition accuracies by raising the sample-to-support-vector ratio.

Abstract: The present application discloses a facial image processing apparatus. The facial image processing apparatus includes a memory; and one or more processor. The memory and the at least one processor are communicatively connected with each other. The memory stores computer-executable instructions for controlling the one or more processors to automatically identify a facial feature from a facial image; automatically extract an image portion defining the facial feature from the facial image; perform a detail enhancement process on the image portion to obtain a detail-enhanced image portion corresponding to the image portion; and perform an image composition process to compose the detail-enhanced image portion and the facial image to obtain an enhanced facial image.

Abstract: An object symmetry axis detection method based on an RGB-D camera includes: obtaining three-dimensional point cloud data of an image of a target object in natural scenes, identifying data corresponding to a point cloud of an area where the target object is located from the three-dimensional point cloud data according to a color difference threshold segmentation method, establishing a spherical coordinate system centering about a centroid of the target point cloud data, selecting n2 candidate symmetry planes with a spherical coordinate system segmentation method, and calculating a symmetry axis of the target point cloud data according to the n2 candidate symmetry planes as the symmetry axis of the target object.

Abstract: The invention relates to a method and apparatus for monitoring yarn quality in the textile industry. Textile yarn is checked for quality to meet the required criteria such as diameter evenness and unwanted foreign fiber presence. The invention utilizes an artificial light illuminating the yarn, focusing element, diffraction element and the image sensor for capturing of focused diffracted rays from illuminated yarn image. The presence of foreign fibres in yarn and yarn diameter is determined by processing of captured image.

Abstract: Techniques are provided for segmentation of a hand from a forearm in an image frame. A methodology implementing the techniques according to an embodiment includes estimating a wrist line within an image shape that includes a forearm and a hand. The wrist line estimation is based on a search for a minimum width region of the shape that is surrounded by adjacent regions of greater width on each side of the minimum width region. The method also includes determining a forearm segment, and a hand segment that is separated from the forearm segment by the wrist line. The method further includes labeling the forearm segment and the hand segment. The labeling is based on a connected component analysis of the forearm segment and the hand segment. The method further includes removing the labeled forearm segment from the image frame to generate the image segmentation of the hand.

Abstract: Described is a multiple-camera system and process for re-identifying a user located in a materials handling facility based on user patterns and/or descriptors representative of the user. In one implementation, a user pattern and/or a plurality of descriptors representative of a user are maintained as a position of a user is tracked through a materials handling facility. If the tracking of the user is lost, the last known position is stored with the user pattern and/or descriptors. If a new object is detected and confirmed to be a user, a user pattern and/or descriptors of the new object are compared with the stored user pattern and/or descriptors to determine if the new object is the user.

Abstract: A method and system are provided for determining a time-dependent, three-dimensional perfusion data set relating to the perfusion of at least one vessel and/or of tissue of an examination object. Projection images of the vessel and/or tissue are acquired in a plurality of recording geometries by an X-ray detector at a plurality of recording times in each case, which images describe detected intensities in a plurality of imaging regions of the X-ray detector. The perfusion data set is determined by associating a weighted sum of specified time-dependent base functions with each voxel of the perfusion data set.