Abstract: An automatic detecting method and an automatic detecting apparatus using the same are provided. The automatic detecting apparatus includes an inputting unit, a dividing unit, a contouring unit, a range analyzing unit, a boundary analyzing unit, an edge detecting unit, an expanding unit and an overlapping unit. The dividing unit is used for dividing an overlooking image into four clusters via a clustering algorithm. The contouring unit is used for obtaining a contour. The range analyzing unit is used for obtaining a detecting range. The boundary analyzing unit is used for obtaining a circular boundary in the detecting range. The edge detecting unit is used for obtaining a plurality of edges in the circular boundary. The expanding unit is used for expanding the edges to obtain a plurality of expanded edges. The overlapping unit is used for overlapping the expanded edges and the contour to obtain a defect pattern.

Abstract: A method for processing multiple images according to various embodiments of the present invention may comprise the operations of: acquiring multiple images by using multiple cameras; extracting image information for each of the acquired multiple images; preprocessing the multiple images on the basis of the extracted image information, and generating parameters corresponding to the multiple images, respectively; selecting one or more images required according to a zoom magnification among the acquired multiple images; and applying parameters corresponding to the selected one or more images and performing image processing. Other embodiments are possible.

Abstract: Presented herein are systems and methods for feature detection in images. A computing system may identify a biomedical image having features. The computing system may apply the biomedical image to a feature detection model. The feature detection model may include an encoder-decoder block to generate a feature map corresponding to the biomedical image, a confidence map generator having a second set of parameters to generate a confidence map using the feature map, and a localization map generator to generate a localization map using the feature map. The computing system may generate a resultant map based on the confidence map and the localization map. The resultant map identifying one or more points corresponding to the one or more features. The computing system may provide the one or more points identified in the resultant map for the biomedical image.

Abstract: An image enhancement method includes the following steps. Separating an original image signal into a high frequency image signal and a low frequency image signal by performing a wavelet transform through a first transformation circuit. Separating the high frequency image signal into a high frequency separation signal and a high-intermediate frequency separation signal by performing the wavelet transform through a second transformation circuit. Separating the low frequency image signal into a low-intermediate frequency separation signal and a low frequency separation signal by performing the wavelet transform. Enhancing high frequency separation signal to generate an enhanced high frequency separation signal configured to enhance the sharpness of an image.

Abstract: A system for facilitating lesion analysis is configurable to identify a user profile associated with a user accessing the system. The user profile indicates a radiology specialty associated with the user. The system is also configurable to access a plurality of cross-sectional medical images associated with a particular patient and identify a subset of cross-sectional medical images from the plurality of cross-sectional medical images that correspond to the radiology specialty indicated by the user profile. The system is also configurable to present the subset of cross-sectional medical images to the user in navigable form.

Abstract: A system for detecting a scan irregularity in scanning process during check-out at a retail store, includes an image receiving module for receiving a video stream of a scanning zone, an image processing module for detecting visual scan intervals in image frames of the video stream, and a decision module. The decision module is configured to process each detected visual scan interval, wherein a processed visual scan interval includes a valid scan action, wherein the valid scan action is a user action performed for scanning an item. The decision module is further configured to detect a scan irregularity in the check-out process, wherein the scan irregularity occurs when an item identified for scanning in a processed visual scan interval is absent in a list of scanned items generated by the scanner during corresponding interval, and provide an alert regarding the scan irregularity at a user computing device.

Abstract: A method, system, and computer-readable medium for producing images is described. The system comprises an input image providing unit for providing input images in which structures that are in fact spatially separated are represented in a spatially superimposed manner in at least one spatial direction. The system further comprises a neural network providing unit for providing a neural network which is adapted to produce, on the basis of input images in which structures that are in fact spatially separated are represented in a spatially superimposed manner in at least one spatial direction, output images in which the structures that are in fact spatially separated are represented in a spatially separated manner in the at least one spatial direction. Finally, an image producing unit produces images on the basis of the input images provided and the neural network provided.

Abstract: Computed tomography (CT) screening, diagnosis, or another image analysis tasks are performed using one or more networks and/or algorithms to either integrate complementary tomographic image reconstructions and radiomics or map tomographic raw data directly to diagnostic findings in the machine learning framework. One or more reconstruction networks are trained to reconstruct tomographic images from a training set of CT projection data. One or more radiomics networks are trained to extract features from the tomographic images and associated training diagnostic data. The networks/algorithms are integrated into an end-to-end network and trained. A set of tomographic data, e.g., CT projection data, and other relevant information from an individual is input to the end-to-end network, and a potential diagnosis for the individual based on the features extracted by the end-to-end network is produced.

Abstract: Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Abstract: A monitoring system is configured to monitor a property. The monitoring system includes a camera, a sensor, and a monitor control unit. The monitor control unit is configured to receive image data and sensor data. The monitor control unit is configured to determine that the image data includes a representation of a person. The monitor control unit is configured to determine an orientation of a representation of a head of the person. The monitor control unit is configured to determine that the representation of the head of the person likely includes a representation of a face of the person. The monitor control unit is configured to determine that the face of the person is likely concealed. The monitor control unit is configured to determine a malicious intent score that reflects a likelihood that the person has a malicious intent. The monitor control unit is configured to perform an action.

Abstract: A method and system for automatically inferring a subject's body position in a two-dimensional image produced by a medical-imaging system are disclosed. The image is labeled with a body position selected from a semantically meaningful set of candidate positions sequenced in order of their relative locations in a subject's body. A processor performs procedures that each identify a class of image features related to pixel intensity, such as a histogram of gradients, local binary patterns, or Haar-like features. A second set of procedures employs applications of a pretrained convolutional neural network that has learned to recognize features of a specific class of medical images. The results of both types of procedures are then mapped by a pretrained support-vector machine onto candidate image labels, which are mathematically combined into a single, semantically meaningful, label most likely to identify a body position of the subject shown by the image.

Abstract: A method for determining position of a target includes determining at least two measurement points of the target, obtaining vector data of at least two direction vectors based on an imaging position of the target on an imaging system and parameter data of the imaging system, and determining a distance between the target and the imaging system according to the vector data and a current vertical height of the imaging system. Each of the at least two direction vectors is a vector from the imaging system to a corresponding one of the at least two measurement points.

Abstract: A component mounting system includes a component mounter, and a server. The component mounter includes a component information recognizer, and a data output unit. The component information recognizer recognizes component information by imaging a character of the component information printed on a surface of a component. The data output unit outputs the component information together with board information to the server, the component information being recognized by the component information recognizer, the board information being information of a board on which the component is installed.

Abstract: An analysis section for performing a predetermined image analysis processing for each of a first image and a second image generated by a performance of duplex scan by a scanner section and a receipt processing apparatus for determining an image of a front of a receipt on the basis of an analysis result of the analysis section.

Abstract: On the basis of user input a set of contour points of a 3D object is detected in a number of 2D slice images representing the 3D object. Next a 2D object is segmented in each of the slice images by the set of contour points so as to obtain segmentation masks. Finally, by interpolation between computed segmentation masks, the 3D object is segmented.

Abstract: A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.

Abstract: Surveillance systems process video streams obtained by a drone to either obscure or highlight objects in a surveillance area based on tags associated with the objects. A method of providing obscurant data includes receiving image data including an image of a target and receiving a preference setting corresponding to the target. Obscurant data of at least a portion of the image data corresponding to the target are determined using the received preference setting. A method of providing surveillance image data includes capturing image data including an image of a target, querying a database to receive a preference setting corresponding to the target, determining the obscurant data of the portion of the image data, and selectively modifying the received image data according to the determined obscurant data to provide the surveillance image data.

Abstract: A method includes acquiring a plurality of pictures of a single item to be classified placed on a checkout counter, the plurality of pictures corresponding to a plurality of shooting angles one to one, performing an object detection in each of the pictures to obtain a rectangular area image, the rectangular area image being an image corresponding to a rectangular area containing the item, obtaining a plurality of primary classification results correspondingly, according to the plurality of rectangular area images and a pre-trained first-level classification model, and obtaining a first-level classification result of the plurality of pictures according to the plurality of primary classification results and a pre-trained first-level linear regression model, using the first-level classification result as a first classification result and performing checkout according to the first classification result.

Abstract: Dimensions of a surface feature are determined by capturing an image of the surface feature and determining a scale associated with the image. Structured light may be projected onto the surface, such that the position of structured light in the captured image allows determination of scale. A non-planar surface may be unwrapped. The surface may alternatively be projected into a plane to correct for the scene being tilted with respect to the camera axis. A border of the surface feature may be input manually by a user. An apparatus and system for implementing the method are also disclosed.

Abstract: An apparatus for producing a fundus image includes: a processor and a memory; an illumination component including a light source and operatively coupled to the processor; a camera including a lens and operatively coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the apparatus to: execute an automated script for capture of the fundus image; and allow for manual capture of the fundus image.