Abstract: An electronic device includes a display, a display driver integrated circuit (DDI) electrically coupled with the display, a memory that stores an image, and a processor. The processor is configured to analyze at least one feature of the image and to determine at least one of a movement range, a movement interval, and a movement period of the image based on the at least one feature. The DDI is configured to display the image in the display when at least part of the processor is deactivated, and to change a display location of the image within the movement range depending on the movement interval and the movement period.

Abstract: An information processing apparatus realizing an enlargement function without system-level customization is provided. The information processing apparatus includes an application unit, an enlargement control unit, and a screen control unit. The application unit generates and draws an image in a normal display area. The enlargement control unit enlarges the image drawn in the normal display area, and generates and draws an enlarged image including part of the enlarged image in an expansion display area. The screen control unit displays the image drawn in the normal display area on a display when the enlargement function is invalid, and displays the enlarged image drawn in the expansion display area on the display when the enlargement function is valid.

Abstract: Provided are a method and apparatus for fusion of a panoramic video to solve a problem that a panoramic video image formed by stitching is incomplete. The method includes: acquiring a panoramic video image (21); extracting a moving target from at least two cuboid three-dimensional images (22); performing image fusion processing on the panoramic video image and the moving target to form a panoramic video image incorporated with the moving target (23). Thereby, problems such as overlapping and disappearance of a moving target, in whole or in part, in a panoramic video image generated by adopting a cuboid three-dimensional model are solved, thereby ensuring the completeness of a panoramic video image and improving the quality of the panoramic video image.

Abstract: A non-transitory computer-readable medium for monitoring a semiconductor fabrication process includes an image conversion model having an artificial neural network. The image conversion model, when executed, causes the processor to receive a first image and a second image of a semiconductor wafer. The artificial neural network is trained by inputting a dataset representing the first image and the second image, generating a conversion image of the semiconductor wafer and calibrating weights and biases of the artificial neural network to match the conversion image to the second image. A third image of the semiconductor wafer is generated based on the calibrated weights and biases of the artificial neural network. The image conversion model with the trained artificial neural network may be transmitted to another device for image conversion of low resolution images.

Abstract: In a method for super resolution imaging, the method includes: receiving, by a processor, a low resolution image; generating, by the processor, an intermediate high resolution image having an improved resolution compared to the low resolution image; generating, by the processor, a final high resolution image based on the intermediate high resolution image and the low resolution image; and transmitting, by the processor, the final high resolution image to a display device for display thereby.

Abstract: Provided is an image restoration apparatus and method. The image restoration apparatus may store an image restoration model including a convolutional layer corresponding to kernels having various dilation gaps, and may restore an output image from a target image obtained by rearranging a compound eye vision (CEV) image by the image restoration model.

Abstract: A surround view system for a vehicle includes a processor (10) and cameras (2 to 5) that can be arranged on the vehicle so that the cameras (2 to 5) can record images of an outside environment of the vehicle. The processor is configured to analyze a position (?) and/or a movement of a movable part (14) of the vehicle (1) and to generate a composite image of the outside environment from individual images recorded by the cameras. Furthermore, the processor is configured to calculate an image processing region within the composite image for an adaptive image processing, to determine, based on the analyzed position and/or movement of the movable part (14), that a back-projection of the movable part (14) goes beyond the image processing region, and to modify the image processing region so that the back-projection of the movable part (14) is within the modified image processing region.

Abstract: Various techniques are provided for reducing noise in captured image frames. In one example, a method includes determining row values for image frames comprising scene information and noise information. The method also includes performing first spectral transforms in a first domain on corresponding subsets of the row values to determine first spectral coefficients. The method also includes performing second spectral transforms in a second domain on corresponding subsets of the first spectral coefficients to determine second spectral coefficients. The method also includes selectively adjusting the second spectral coefficients. The method also includes determining row correction terms based on the adjusted second spectral coefficients to reduce the noise information of the image frames. Additional methods and systems are also provided.

Abstract: A method and device for denoising processing, a storage medium, and a terminal are provided. The method includes the following. A to-be-processed picture and a reference picture are obtained from multiple pictures taken continuously. A displacement of a terminal generated within a period in which the to-be-processed picture and the reference picture are taken is obtained. According to the displacement, reference pixel points of the reference picture each corresponding to one of to-be-processed pixel points of the to-be-processed picture are determined. According to a pixel value of each of the reference pixel points, a pixel value of a corresponding to-be-processed pixel point is adjusted.

Abstract: A method for picture processing is provided. The method includes the following. Multiple pictures are obtained through photographing. At least two pictures are selected from the multiple pictures, and according to facial information of each of the at least two pictures, feature information of a preset facial part in each of the at least two pictures is obtained. One or more target pictures are determined in response to the feature information of the preset facial part in one of the at least two pictures indicating that the preset facial part is in a preset feature state, where the one of the at least two pictures is determined as one target picture. When multiple target pictures are obtained, a multi-frame denoising processing is performed on the target pictures to obtain an output picture.

Abstract: When swinging of a camera does not follow a motion of a subject in following-photographing, the position of the subject deviates among a plurality of image, and thus there is a problem that the subject that is moving is blurred. An image processing apparatus includes: a standard region designation unit configured to designate a partial region in an image as a standard region; a standard region motion blur setting unit configured to set a motion blur in the standard region designated by the standard region designation unit as a predetermined motion blur; and a motion blur adjustment unit configured to adjust a motion blur in the standard region so that the motion blur becomes the predetermined motion blur and adjust a motion blur on a screen of a region different from the standard region in accordance with the adjusting of the motion blur in the standard region.

Abstract: The disclosure provides a method for dynamically adjusting image clarity and an image processing device using the same method. The method includes: retrieving an image frame and searching for a predetermined object therein; evaluating a first sharpness of the predetermined object; if the first sharpness of the predetermined object is lower than a sharpness threshold, calculating a difference value between the first sharpness and the sharpness threshold; dividing the image frame into blocks and evaluating a risk value and an effect value of increasing a second sharpness of each block; inputting the difference value, the risk value, and the effect value to a classifying model to generate a clarity setting value; and adjusting a clarity of a display on showing the image frame based on the clarity setting value.

Abstract: Techniques are provided for unsupervised training of a neural network to perform compression of a high dynamic range (HDR) image. A methodology implementing the techniques according to an embodiment includes performing global tone mapping on an HDR training image to generate a low dynamic range (LDR) training image. The method also includes applying the neural network to the HDR training image and the LDR training image to generate a delta image representing image detail lost in the global tone mapping operation. The method further includes summing the delta image with the LDR training image to generate an output training image, and generating a loss function calculated from a weighted sum of a contrast loss and a compression loss. The contrast loss is based on the output training image and the HDR training image, and the compression loss is based on the output training image and the LDR training image.

Abstract: A method for determining a condition of an object, in particular whether the object is a normal condition or an abnormal condition. The method includes processing data of an object based on a determination model to determine a condition of the object. The method also includes classifying the object as in a normal condition or an abnormal condition based on the processing. The abnormal condition may indicate that the object is defective.

Abstract: A system for inspecting objects includes an imaging assembly, a detection module, an analysis module, and a memory. The imagining assembly can be a borescope that enters an unlit, closed conduit to inspect objects that are components of a turbine engine. Using an artificial light source to illuminate the conduit and one or more objects of the turbine engine, the borescope can obtain a video image of the one or more objects of the turbine engine. The video image can be used to determine a rank of distress for the one or more objects.

Abstract: A method and a system for patch-based scene segmentation using neural networks are presented. In an embodiment, a method comprises: using one or more computing devices, receiving a digital image comprising test image; using the one or more computing devices, creating, based on the test image, a plurality of grid patches; using the one or more computing devices, receiving a plurality of classifiers that have been trained to identify one or more materials of a plurality of materials; using the one or more computing devices, for each patch of the plurality of grid patches, labelling each pixel of a patch with a label obtained by applying, to the patch, one or more classifiers from the plurality of classifiers; using the one or more computing devices, generating, based on labels assigned to pixels of the plurality of grid patches, a grid of labels for the test image.

Abstract: A fabric inspection device includes a sheet conveying portion, an image reading portion, a timing control portion, a storage portion, a comparing control portion, and a display portion. The image reading portion reads a plurality of images of the same pattern recorded repeatedly with a predetermined pitch on a recording sheet conveyed by the sheet conveying portion. The timing control portion regulates the timing of image reading by the image reading portion. The storage portion stores the plurality of images read by the image reading portion. The comparing control portion takes, out of the plurality of images stored in the storage portion, part of an image continuously recorded on the recording sheet corresponding to the forefront one pitch as a reference image and compares the image data of a comparison image recorded later than the reference image with image data of the reference image to check for image defects.

Abstract: Photographs can sometimes be altered or changed in order to create photographs which appear to be of a scene which never took place, or did not take place at the place and time when the photograph is purported to have been taken. One way of circumventing known systems to detect this sort of falsified image is to take a photograph of a printed altered image. The present invention is a method of detecting such “photographs of photographs” by comparing two photographs of the same seen taken at the same time, with different exposures.

Abstract: A machine learning method is used for improving accuracy of an automated optical inspection (AOI) device. The method includes obtaining an image of a component to be inspected, processing the image to generate digital image information, establishing a machine learning model according to the digital image information, inputting the digital image information into the machine learning model for determination, verifying accuracy of a result of determination by the machine learning model, adjusting and optimizing the machine learning model according to the result of determination of the accuracy of the machine learning model, and improving the machine learning model until the machine learning model reaches a predetermined accuracy.

Abstract: A system is provided for identifying damages of a vehicle. During operation, the system can obtain a set of digital images associated with a set of tagged digital images as training data. Each tagged digital image in the set of tagged digital images may include at least one damage object. The system can train a damage identification model based on the training data. When training the damage identification model, the system may identify at least a damage object in the training data based on a target detection technique. The system may also generate a set of feature vectors for the training data. The system can use the set of feature vectors to optimize a set of parameters associated with the damage identification model to obtain a trained damage identification model.

Abstract: Embodiments described herein provide a system for facilitating image sampling for training a target detector. During operation, the system obtains a first image depicting a first target. Here, the continuous part of the first target in the first image is labeled and enclosed in a target bounding box. The system then generates a set of positive image samples from an area of the first image enclosed by the target bounding box. A respective positive image sample includes at least a part of the first target. The system can train the target detector with the set of positive image samples to detect a second target from a second image. The target detector can be an artificial intelligence (AI) model capable of detecting an object.

Abstract: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

Abstract: An information processing method, device, cloud-based processing device, and computer program product are related to the field of data processing technologies and can cause an increased efficiency in detecting whether a road area contains a depression region. The information processing method includes: acquiring a depth image; processing the depth image to obtain a means-by-row graph, based on which a road area in the depth image is determined; determining a suspected depression region in the road area; and judging over the suspected depression region based on a depression threshold to determine whether the depth image contains a depression region.

Abstract: A method for determining experience quality of virtual reality (VR) multimedia includes, in a process of playing VR multimedia, obtaining a first sensory parameter, a second sensory parameter, and a third sensory parameter of the VR multimedia, where the first sensory parameter, the second sensory parameter, and the third sensory parameter are obtained by performing sampling separately according to at least two same perceptual dimensions, and are respectively parameters that affect fidelity experience, enjoyment experience, and interaction experience, and determining a mean opinion score (MOS) of the VR multimedia based on the first sensory parameter, the second sensory parameter, and the third sensory parameter of the VR multimedia. Because the third sensory parameter is a parameter that affects the interaction experience, an interaction feature of the VR multimedia is considered.

Abstract: A method of detecting hardware Trojans in an IC includes providing a golden IC layout data set or SEM image data taken at long dwelling time on an active area of the golden IC after polishing it from the backside. Next, the IC under authentication (IUA) sample is prepared for fast SEM imagining (shorter dwelling time) after backside thinning. Next step is to perform image processing on the IUA's SEM image, which includes histogram equalization with noise filtering using Gaussian and Median filters. In the last step, the IUA sample data with the shorter dwelling time is compared with the golden IC layout data or the golden image data from high quality (longer dwelling time) SEM scanning process. At the end the result of the comparison is used to identify hardware Trojans.

Abstract: A method for configuring a neural network comprises: accessing a plurality of three-dimensional (3D) emission image data sets collected by an emission scanner from respective brains of respective subjects; transforming each of the plurality of 3D emission image data sets to a respective two-dimensional (2D) image; cropping portions of each respective 2D image to remove image data corresponding to tissue outside of a striatum of each of the respective brains, to form respective cropped 2D striatum images; and training a neural network to detect a presence of a Parkinsonian syndrome using the cropped 2D striatum images.

Abstract: An ultrasound system (100) and operating method (200) are disclosed in which the system is adapted to receive a sequence (15) of 2-D ultrasound image frames (150) of a prenatal entity from an ultrasound probe (14) and, for each image frame in said sequence, control the display device to display the received image frame; attempt to segment the image frame for recognition of an anatomical feature of interest (151) of said prenatal entity in said image frame; and accept the image frame for further processing upon recognition of said feature, said further processing comprising: determine a geometric property of the recognized anatomical feature of interest for each accepted image frame; and control the display device to display the determined geometric properties of the accepted image frames in said sequence with each displayed image frame. Such an operating method may be made available as a computer program product for installation on the ultrasound system.

Abstract: A determination processing unit determines a disease region in a medical image including an axisymmetric structure. A first determination section of the determination processing unit generates a feature amount map of the medical image from the medical image. A second determination section second inverts the feature amount map with reference to a symmetry axis to generate an inverted feature amount map. A third determination section superimposes the feature amount map and the inverted feature amount map on each other and determines the disease region in the medical image using the feature amount map and the inverted feature amount map superimposed on each other.

Abstract: Majorana photons are transmitted through a biological tissue sample to image the tissue. The Majorana photons have a circular polarization, a radial polarization or an azimuthal polarization. The transmitted photons are processed to produce a digital image of the biological tissue sample.

Abstract: The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.

Abstract: A system for computer-aided triage can include a router, a remote computing system, and a client application. A method for computer-aided triage can include determining a parameter associated with a data packet, determining a treatment option based on the parameter, and transmitting information to a device associated with a second point of care.

Abstract: A method of matching medical images according to user-defined matches rules. In one embodiment, the matched medical images are displayed according user-defined display rules such that the matched medical images may be visually compared in manner that is suitable to the viewer's viewing preferences.

Abstract: An endoscope image processing device includes a processor. The processor sequentially receives an observation image obtained by performing image pickup of an object inside a tubular portion of a subject, performs processing for detecting a region of interest with respect to the observation image, performs judgement processing as to whether degradation of visibility of the region of interest included in the observation image is predicted, and performs emphasis processing for emphasizing the position of the region of interest when a judgement result indicating that the degradation of visibility of the region of interest included in the observation image is predicted is acquired by the judgement processing.

Abstract: In one embodiment, a patient's brain is evaluated after onset of a stroke by capturing computed tomography angiography (CTA) images of the brain, analyzing the CTA images with a CTA image analysis program to evaluate the patient's brain, and generating results based upon the analysis that provide an assessment of the brain. In some cases, the CTA image analysis program comprises a machine-learning algorithm that has been trained on the results of perfusion imaging analysis.

Abstract: An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

Abstract: A method of determining respiratory phase of a living body from a sequence of digitized fluoroscopic images of a living-body region exhibiting respiratory displacement, the method employing programmable computing apparatus and comprising the steps of: (a) in each living-body-region image in the sequence, defining one or more zones with each image having identical image-to-image zone locations, sizes, and shapes; (b) for each image, computing an average pixel intensity for each zone to form a sequence thereof for each zone; (c) for each zone, modifying the average pixel intensities by (i) computing the mean value of the sequence of average pixel intensities for such zone, and (ii) subtracting the mean from each average pixel intensity in the zone; (d) for each zone sequence, computing a figure-of-merit; (e) selecting the zone having the highest figure-of-merit; and (f) using the sequence of pixel intensities of the selected zone to determine respiratory phase.

Abstract: A method of object detection includes obtaining a set of images depicting overlapping regions of an area containing a plurality of objects. Each image includes input object indicators defined by input bounding boxes, input confidence level values, and object identifiers. The method includes identifying candidate subsets of input object indicators in adjacent images. Each candidate subset has input overlapping bounding boxes in a common frame of reference, and a common object identifier. The method includes adjusting the input confidence levels for each input object indicator in the candidate subsets; selecting clusters of the input object indicators satisfying a minimum input confidence threshold, having a common object identifier, and having a degree of overlap satisfying a predefined threshold; and detecting an object by generating a single output object indicator for each cluster, the output object indicator having an output bounding box, an output confidence level value, and the common object identifier.

Abstract: Fiducial markers are printed patterns detected by algorithms in imagery from image sensors for applications such as automated processes and augmented reality graphics. The present invention sets forth extensions and improvements to detection technology to achieve improved performance, and discloses applications of fiducial markers including multi-camera systems, remote control devices, augmented reality applications for mobile devices, helmet tracking, and weather stations.

Abstract: A computer-implemented method for representing environmental elements includes receiving scan data comprising at least a point cloud representing at least an environmental element from a sensor, segmenting the point cloud into point clusters, and partitioning the point clusters into hierarchical grids. The method also includes establishing a Gaussian distribution for points in each cell of each of the hierarchical grids, and constructing a Gaussian Mixture Model based on the Gaussian distribution for representing the environmental element.

Abstract: Embodiments of present disclosure discloses system and method for acquisition of optimal images of object in multi-layer sample. Initially, images for FOV of multi-layer sample comprising objects are retrieved. Each of images are captured by varying focal depth of image capturing unit associated with system. Further, objects associated with multi-layer sample in FOV are identified. For identification, cumulative foreground mask of FOV is obtained based on adaptive thresholding performed on foreground image of FOV. Based on contour detection performed on cumulative foreground mask of FOV, object masks, corresponding to objects, is obtained for identifying objects. Further, sharpness of each of images associated with each of object masks is computed. Based on sharpness, optimal images from images for each of objects is selected for acquisition of optimal images of objects in multi-layer sample.

Abstract: The invention relates to a system for dynamically maximizing the contrast between the foreground and background in images and/or image sequences, comprising at least one image capturing device (C1, C2), at least one analysis module (A1, A2) which is connected to the image capturing device (C1, C2) and which is designed to determine a transformation between a two-dimensional coordinate system of the image capturing device (C1, C2) and a two-dimensional coordinate system of a display device (L) and carry out a segmentation in order to determine at least the occlusion of background objects by means of foreground objects in an image; at least one control module (K) which is connected to the analysis module (A1, A2); and at least one display device (L) which is designed to display display information, in particular video clips, wherein the image capturing device (C1, C2) is designed to capture the display information displayed on the display device (L), the analysis module (A1, A2) is additionally designed to asce

Abstract: Provided is an image processing apparatus comprising: one or more memories that store a set of instructions; and one or more processors that execute the instructions to obtain a plurality of inputted images that are contiguously captured, perform determination of a first region based on the obtained inputted images, the first region being formed from pixels each having a change in pixel value below a predetermined threshold in a predetermined period, the determination being performed in each of a plurality of the continuous predetermined periods, determine a second region based on a plurality of the first regions determined in the plurality of the continuous predetermined periods, respectively, determine a third region by subjecting image data representing the determined second region to image processing, and update a background image based on the obtained inputted images and any of the determined third region.

Abstract: A method includes detecting a first movement pattern of a first device using a camera of a second device and establishing a connection between the first and second devices responsive to detecting the first movement pattern. A device includes a camera and a processor coupled to the camera, wherein the processor is to detect a first movement pattern of a second device using the camera and establish a connection with the second device responsive to detecting the first movement pattern.

Abstract: A method of determining and displaying movement of an object in an environment using a moving camera includes identifying later environment features located in the environment in a later image, earlier environment features located in the environment in an earlier image, and earlier object features located on the object in the earlier image. The method further includes estimating object features in the later image using the earlier object features and a determined camera movement. The method further includes locating, in the later image, matched object features that are actual object features in the later image at a same location as the estimated object features. The method further includes determining that the object has moved between the earlier image and the later image if a number of matched object features does not exceed a threshold.

Abstract: A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.

Abstract: A determination unit determines whether or not a first foreground area extracted from a first captured image represents a moving object. A warning unit outputs warning when the first foreground area is determined to be not representing the moving object. The first foreground area is determined to be not representing the moving object when the object represented by the first foreground area is not moving. First, the determination unit determines whether or not the object represented by the first foreground area is moving by comparing the first foreground area with a second foreground area extracted from a second captured image generated before the first captured image. Then, the determination unit determines whether or not the object is moving at time before the first captured image is generated based on information of the object at time before the first captured image is generated.

Abstract: An apparatus for calculating an estimation value of movement information of a mobile body based on information from a camera mounted on the mobile body. The apparatus includes an extractor that extracts feature points from frame images input from the camera, a calculator that calculates an optical flow indicating movements of the feature points between a current frame image and a previous frame image, and an estimator that calculates the estimation value based on the optical flow. The estimator switches a calculation method of the estimation value based on at least one of (i) a speed of the mobile body, (ii) a number of the feature points extracted by the extractor, and (iii) feature amounts indicating uniqueness of the feature points.

Abstract: An apparatus, system and method for tracking objects within a venue. A plurality of detector stations are disposed throughout the venue. Each detector station includes an imaging assembly, a transceiver, and a symbology accelerator having least one processor. The processor is configured to obtain image data captured by the imaging assembly and analyze the image data to detect one or more objects. Each of the one or more objects includes a symbol indicative of an object identifier. The processor is configured to decode each of the symbols to obtain the respective identifier and associate the detected objects or respective symbols with a respective physical location within the venue. For each of the one or more objects, the processor is configured to transmit, via the transceiver to a remote server, at least one data packet including the respective physical location and the identifier for the object.

Abstract: A medical image diagnostic apparatus according to an embodiment includes processing circuitry. The medical image diagnostic apparatus performs image registration between medical image data. The processing circuitry extracts a structure of the subject included in the medical image data. The processing circuitry sets a display scale of the subject to a specified value. The processing circuitry performs image registration between the medical image data on the display scale of the specified value.

Abstract: A method for generating a synthesized medical image by receiving a normal image includes generating first data based on a random selection, generating second data, and, based at least in part on the first and second data, modifying the normal image to form the synthesized medical image. Modifying the normal image comprises combining the first data and the second data. The first data characterizes an image that represents a lesion and the second data characterizes a transformation of that image as well as a location of the lesion.