Abstract: Embodiments relate to generating keypoint descriptors of the keypoints. An apparatus includes a pyramid image generator circuit and a keypoint descriptor generator circuit. The pyramid image generator circuit generates an image pyramid from an input image. The keypoint descriptor generator circuit determines intensity values of sample points in the pyramid images for a keypoint and determines comparison results of comparisons between the intensity values of pairs of the sample points. The keypoint descriptor generator circuit generate bit values defining the comparison results for the keypoint, each bit value corresponding with one of the comparison results, and generate a sequence of the bit values defining an ordering of the comparison results based on importance levels of the comparisons, where the importance level of each comparison defines how much the comparison is representative of features. Bit values for comparisons having the lowest importance levels may be excluded from the sequence.

Abstract: An object detection network includes: a hybrid voxel feature extractor configured to acquire a raw point cloud, extract a hybrid scale voxel feature from the raw point cloud, and project the hybrid scale voxel feature to generate a pseudo-image feature map; a backbone network configured to perform a hybrid voxel scale feature fusion by using the pseudo-image feature map to generate multi-class pyramid features; and a detection head configured to predict a three-dimensional object box of a corresponding class according to the multi-class pyramid features. The object detection network can effectively solve a problem that under a single voxel scale, inference time is longer if the voxel scale is smaller, and an intricate feature cannot be captured and a smaller object cannot be accurately located if the voxel scale is larger. Different classes of 3D objects can be detected quickly and accurately in a 3D scene.

Abstract: A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

Abstract: A method for box filtering includes obtaining, by a computing device, a form image, and identifying, by the computing device, a region of the form image that includes boxes. Vertical lines in the region of the form image are detected. The boxes in the region are detected according to the plurality of vertical lines, and image content is extracted from the boxes.

Abstract: A determination apparatus extracts a plurality of specific events that have values greater than an event determination threshold from among a plurality of events that have occurred in chronological order. The determination apparatus generates a feature amount related to adjacent occurrence intervals of the plurality of specific events, using the plurality of specific events. The determination apparatus generates array data corresponding to the plurality of events using points each having components of the event determination threshold and the feature amount, while changing the event determination threshold. The determination apparatus determines a type of the plurality of events using the array data.

Abstract: The technique of the present disclosure can add a gloss to image data according to the hair flow of a subject. An image data obtaining unit obtains input image data, and a characteristic amount calculating unit calculates characteristic amounts for the input image data. The characteristic amount calculating unit obtains a degree to which directions of hair flow are concentrated to a predetermined direction (a degree of alignment) in a predetermined range based on the directions of hair flow, and sets this as a characteristic amount indicative of the degree of alignment of the directions of hair flow. A determining unit estimates the state of the hair flow of the entire input image data based on the characteristic amounts thus calculated, and determines whether to correct the input image data. A correcting unit performs a gloss adding correction to the input image data.

Abstract: An image-recognition method is provided. The method includes the following steps: receiving a plurality of check-point images, and classifying the check-point images into a plurality of groups; classifying the check-point images in each group into a plurality of types to generate first structured data, wherein the first structured data includes a first layer and a second layer, and the first layer indicates the groups in different statuses, the second layer is located with directories of the first layer, and the types in each group of the second layer indicate different components in a status corresponding to each group; and balancing a number of the check-point images in each type of each group in the first structured data to generate second structured data, wherein the second structured data is used to train an AI model for image recognition.

Abstract: In some implementations, a device may detect edges in an image, and may identify, based on the edges, a rectangle that bounds a document in the image. The device may detect lines in the image, and may identify edge candidate lines by discarding one or more of the lines. The device may identify intersection points where lines, included in the edge candidate lines, intersect with one another. The device may identify corner candidate points by discarding one or more points included in the intersection points, and may identify a corner point included in the corner candidate points. The corner point may be a point, included in the corner candidate points, that is closest to one corner of the bounding rectangle. The device may perform perspective correction on the image of the document based on identifying the corner point.

Abstract: A fingerprint image processing device includes a memory, and a processor coupled to the memory. The processor performs operations. The operations include reading a tenprint card image which includes a plurality of fingerprint patterns and at least one ruled line to separate one fingerprint imprint area from another fingerprint imprint area, and extracting from the tenprint card image a fingerprint image which includes at least one of the fingerprint patterns, a part of a fingerprint imprint area, and a part of a next fingerprint imprint area.

Abstract: A device that includes a converter engine configured to receive an input signal at one of a first input and a second input. In response to receiving the input signal at the first input, the device is configured to identify a real world value in a converter table based on the input signal, fetch a correlithm object linked with the real world value, and to output the identified correlithm object as the first output signal. In response to receiving the input signal at the second input, the device is configured to identify a correlithm object from the converter table with the shortest distance, to fetch a real world value from the converter table linked with the identified correlithm object, and to output the identified real world value as the second output signal.

Abstract: A system and method for processing candidate edges are presented. The method may be performed when spatial structure information and 2D image information are stored. The method may include identifying, based on the spatial structure information, a 3D location that represents a corner of an object structure; identifying, based on the spatial structure information, a 3D vector that extends from the 3D location and is parallel with an edge; determining a 2D location within the 2D image information that corresponds to the 3D location; determining a 2D vector within the 2D image information that corresponds to the 3D vector; determining an edge detection region; identifying a cluster of candidate edges within the edge detection region that does not represent any object edge; identifying, as a 2D object edge, a candidate edge that is not part of the identified cluster; and performing segmentation based on the 2D object edge.

Abstract: Disclosed is an image analysis method implemented by a computer, the method including analyzing a partial image which is a part of an image of a planar subject, generating partial-image analysis data representing a characteristic of the partial image, comparing, for each of a plurality of images, candidate-image analysis data with the partial-image analysis data, the candidate-image analysis data representing a characteristic of each of the plurality of images, and selecting a candidate image among the plurality of images, the candidate image including a part corresponding to the partial image.

Abstract: Methods, systems, and devices for object detection are described. An example method for object detection is provided which may include capturing at least three polarization angles of a scene. The method may include translating polarization parameters associated with the at least three polarization angles to a reference angle to create a vector map and resolving the vector map into parallel components and perpendicular components, wherein the parallel components are parallel to a plane of incidence of light in the scene and the perpendicular components are perpendicular. The method may further include determining a range map based at least in part on the parallel and perpendicular components, detecting an object present in the scene using the range map and an airlight scattering polarization component, and outputting an indication of the object.

Abstract: The present disclosure provides a detection apparatus and method, and image processing apparatus and system. The detection apparatus extracts features from an image, detects objects in the image based on the extracted features; and detects key points of the detected objects based on the extracted features, the detected objects and a pre-obtained key point sets. According to the present disclosure, the whole detection speed can be ensured not to be influenced by the number of objects in the image to be detected while the objects and key points thereof are detected, so as to better meet the requirements of timeliness and practicability of the detection by the actual computer vision task.

Abstract: This application is directed to vectoring a raster image in which an electronic device detects a contour of a component in the raster image, builds tangent vectors for each point of the contour and identifies a plurality of segmentation points on the contour. One or more points of sharp angle are identified on the contour in accordance with a determination that each point of sharp angle corresponds to two distinct tangent vectors and that an angle between the two distinct tangent vectors falls below a predefined threshold. A respective one of the segmentation points is positioned at each identified point of shape angle. The electronic device approximates a piecewise smooth fitting curve (e.g., a piecewise Bezier curve) having two or more fitting segments to connect the plurality of segmentation points on the contour. The piecewise smooth fitting curve is thereby provided to vectorize the raster image.

Abstract: A geometric parameter measurement method and a device thereof, an augmented reality device, and a storage medium are provided. The geometric parameter measurement method includes: acquiring a left eye image and a right eye image upon a user gazing at a target object, and determining a left eye sight line and a right eye sight line based on the left eye image and the right eye image, which images are acquired upon the user gazing at the target object; determining, according to the left eye sight line and the right eye sight line, a convergence point of the left eye sight line and the right eye sight line; and calculating geometric parameters of the target object in a case where the convergence point coincides with a desired position on the target object.

Abstract: A computer-implemented method for image capture by a mobile device, comprising: receiving, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device; identifying a specific frame of the video stream; generating a plurality of hypotheses defining image borders within the specific frame; selecting, by a neural network, a particular hypothesis among the plurality of hypotheses; producing a candidate image by applying the particular hypothesis to the specific frame; determining a value of a quality metric of the candidate image; determining that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream; wherein the image capture application is a zero-footprint application.

Abstract: Systems and methods of the present disclosure can facilitate determining a three-dimensional surface representation of an object. In some embodiments, the system includes a computer, a calibration module, which is configured to determine a camera geometry of a set of cameras, and an imaging module, which is configured to capture spatial images using the cameras. The computer is configured to determine epipolar lines in the spatial images, transform the spatial images with a collineation transformation, determine second derivative spatial images with a second derivative filter, construct epipolar plane edge images based on zero crossings of second derivative epipolar planes image based on the epipolar lines, select edges and compute depth estimates, sequence the edges based on contours in a spatial edge image, filter the depth estimates, and create a three-dimensional surface representation based on the filtered depth estimates and the original spatial images.

Abstract: In variants, the method for automatic debris detection includes: determining a region image; optionally determining a parcel representation for the region image; generating a debris representation using the region image; generating a debris score based on the debris representation; and optionally monitoring the debris score over time.

Abstract: Disclosed is an electronic apparatus. The electronic apparatus includes a processor configured to downscale an image using a trained first artificial intelligence (AI) model and to encode a downscaled image, and the processor is configured to control downscaling of the image based on quality information of the image obtained using a trained second AI model, and the second AI model may be trained using feature information of the image obtained from the first AI model.