Abstract: Disclosed is a method for scheduling of shooting of a satellite image based on deep learning, which is performed by a computing device. The method may include: generating a prediction image and a cloud amount prediction value up to a future time desired by a user based on a pre-shot satellite image by using a pre-trained neural network model; and determining a shooting schedule of a satellite for at least one region of interest based on the cloud amount prediction value.

Abstract: A terminal apparatus includes a controller configured to: transmit, to a server apparatus, a suggestion request requesting a suggestion of a function to be invoked; receive, from the server apparatus, a suggestion response that specifies a suggested function determined by the server apparatus; and invoke the suggested function. The suggestion request includes face image data of a user. The suggested function is determined by applying the face image data to a learned model. The controller is configured to: evaluate, based on face images of the user, a degree of change in facial expressions before and after invoking the suggested function; and transmit, to the server apparatus, a learning request requesting re-learning of the learned model when the evaluated degree of change in the facial expressions satisfies a predetermined condition.

Abstract: A system and methods for a CODEC driving a real-time light field display for multi-dimensional video streaming, interactive gaming and other light field display applications is provided applying a layered scene decomposition strategy. Multi-dimensional scene data is divided into a plurality of data layers of increasing depths as the distance between a given layer and the plane of the display increases. Data layers are sampled using a plenoptic sampling scheme and rendered using hybrid rendering, such as perspective and oblique rendering, to encode light fields corresponding to each data layer. The resulting compressed, (layered) core representation of the multi-dimensional scene data is produced at predictable rates, reconstructed and merged at the light field display in real-time by applying view synthesis protocols, including edge adaptive interpolation, to reconstruct pixel arrays in stages (e.g. columns then rows) from reference elemental images.

Abstract: A method for creating a high-resolution image of an object from low-resolution images of the object is provided. Both the low-resolution images and the high-resolution image are composed of a pixel grid. An image recording device successively records low-resolution images, in which pitches of the grid points of the pixel grid are increased in one image dimension in comparison with the pitches of the grid points of the pixel grid in the high-resolution image to be created. A data processing system registers the low-resolution images with respect to one another to obtain registered images which are superimposed to obtain the high-resolution image. The grid points of the low-resolution images and the grid points of the high-resolution image have same dimensions and the data processing system uses image information obtained from different positions of the object relative to the grid points in the individual low-resolution images to create the high-resolution images.

Abstract: A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

Abstract: A character recognizing apparatus includes an acquiring unit, an identifying unit, and a character recognizing unit. The acquiring unit acquires a string image that is an image of a string generated in accordance with one of multiple string generation schemes. The identifying unit identifies a range specified for a result of character recognition in each of the multiple string generation schemes. The character recognizing unit performs first character recognition on the string image, and if a result of the first character recognition has a feature of a particular string generation scheme of the multiple string generation schemes, the character recognizing unit performs second character recognition on the string image within the range specified for a result of character recognition in the particular string generation scheme.

Abstract: An apparatus and method for executing a safety function is particularly applicable to monitoring a safety area of a technical installation. An imaging unit acquires an event that triggers the safety function within a defined working area. A controller carries out a safety-related reaction depending on the triggering event. A test unit is configured to verify the operability of the imaging unit and includes a processing unit and a projection unit. The projection unit projects a pattern with defined properties into the working area. The processing unit evaluates the image data acquired by the imaging unit to detect the projected pattern within the acquired image data. Further, the processing unit extracts the specific properties of the detected projected pattern, and compares the specific properties of the detected projected pattern with the defined properties.

Abstract: Described is a method for instilling the haptic dimension of texture to virtual and holographic objects using mid-air ultrasonic technology. A set of features is extracted from imported images using their associated displacement maps. Textural qualities such as the micro and macro roughness are then computed and fed to a haptic mapping function together with information about the dynamic motion of the user's hands during holographic touch. Mid-air haptic textures are then synthesized and projected onto the user's bare hands. Further, mid-air haptic technology enables tactile exploration of virtual objects in digital environments. When a user's prior and current expectations and rendered tactile texture differ, user immersion can break. A study aims at mitigating this by integrating user expectations into the rendering algorithm of mid-air haptic textures and establishes a relationship between visual and mid-air haptic roughness.

Abstract: The present disclosure relates to a method and an apparatus for detecting a subject, an electronic device, and a computer readable storage medium. The method includes the following. A current image and a previous image are obtained. A transformation matrix between the current image and the previous image is obtained in response to determining that the current image indicates the shaking state. The previous image is corrected based on the transformation matrix. The subject detection model is updated based on the corrected previous image. The subject detection is performed on the current image based on the updated subject detection model, to obtain a target subject.

Abstract: An image analyzing method of increasing analysis accuracy is applied to an image monitoring apparatus with a processor. The image analyzing method is executed by the first processor and includes receiving statistic data of an external electronic unit, transforming the statistic data into feedback data via specific algorithm, and generating a control parameter according to the feedback data. The control parameter is used to optimize image data output by the image monitoring apparatus.

Abstract: A set of input images from satellites (or other remote sensors) can be orthorectified and stitched together to create a mosaic. If the resulting mosaic is not of suitable quality, the input images can be adjusted and the processes of orthorectifying and creating the mosaic can be repeated. However, orthorectifying and creating the mosaic uses a large amount of computational resources and takes a lot of time. Therefore, performing numerous iterations is expensive and sometimes not practical. To overcome these issues, it is proposed to generate an indication of accuracy of the resulting mosaic prior to orthorectifying and creating the mosaic by accessing a set of points in the plurality of input images, projecting the points to a model, determining residuals for the projected points, and generating the indication of accuracy of the orthorectified mosaic based on the determined residuals.

Abstract: The present invention discloses a space allocation system and a method for allocating space to objects with multi-variate characteristics. In the present invention, intra-container allocation and/or inter-container allocation is performed to generate a combination in which the objects can be placed in one or more pre-defined storage spaces in a plurality of storage containers. The generated combination is further optimized for efficient space allocation to objects with multi-variate characteristics.

Abstract: A method for translating an image, a method for training an image translation model, and related electronic devices are proposed. In the method for translating an image, an image translation request carrying an original image is obtained. A down-sampled image is generated by down sampling the original image. A pre-translated image, a mask image, and deformation parameters are generated based on the down-sampled image. A size of the pre-translated image and a size of the mask image are the same as a size of the original image. A deformed image is obtained by deforming original image based on the deformation parameters. The deformed image, the pre-translated image and the mask image are fused to generate a target translation image.

Abstract: A processor-implemented method of detecting a moving object includes: estimating a depth image of a current frame; determining an occlusion image of the current frame by calculating a depth difference value between the estimated depth image of the current frame and an estimated depth image of a previous frame; determining an occlusion accumulation image of the current frame by adding a depth difference value of the occlusion image of the current frame to a depth difference accumulation value of an occlusion accumulation image of the previous frame; and outputting an area of a moving object based on the occlusion accumulation image.

Abstract: A clustering method includes: multiple data to be clustered are acquired, the data to be clustered including exact matching information; multiple data sets consisting of data to be clustered with same exact matching information are determined; a clustering center is determined according to the amount of data to be clustered in each of the multiple data sets; and the multiple data to be clustered are clustered according to the clustering center to obtain target clusters. Accordingly, multiple data sets each consisting of data to be clustered with same exact matching information are determined, and a clustering center is determined according to the amount of the data to be clustered in each of the multiple data sets, so that unreasonable determination of the clustering centers can be improved, and accuracy of a clustering algorithm can further be improved.

Abstract: Systems and methods for macro stereo time-lapse photography, producing a stereographic time-lapse digital video, and macro stereographic time-lapse digital videos. A method of producing a sequence of time-lapse stereographic images of a subject, by positioning a camera with a macro lens at a first position relative to the subject; using the camera to obtain a first stack of images of the subject from the first position; positioning the camera at a second position relative to the subject; using the camera to obtain a second stack of images of the subject from the second position; and storing the first stack of images and the second stack of images as a stack pair; and then selectively repeating.

Abstract: In one embodiment, a method includes accessing first-resolution images corresponding to frames of a video, computing a motion vector based on a first-resolution image of a first frame in the video and a first-resolution image of a second frame in the video, generating a second-resolution warped image associated with the second frame by using the motion vector to warp a second-resolution reconstructed image associated with the first frame, generating a second-resolution intermediate image associated with the second frame based on the first-resolution image associated with the second frame, computing adjustment parameters by processing the first-resolution image associated with the second frame and the second-resolution warped image associated with the second frame using a machine-learning model, and adjusting pixels of the second-resolution intermediate image associated with the second frame based on the adjustment parameters to reconstruct a second-resolution reconstructed image associated with the second fra

Abstract: A method of monitoring a user performing a handwashing procedure, including: recognizing a user based upon biometric information obtained at a handwashing station, a user; detecting use of a component of the handwashing station; capturing image data of the user at the handwashing station; capturing data describing components and consumables used at the handwashing station; analyzing, using a processor, the image data; determining compliance with a handwashing procedure based at least in part on the analysis of the image data and the use of the component; and communicating compliance information.

Abstract: A method for acquiring a sample image set, includes: acquiring a plurality of frame images continuously shot for a shooting scene; determining, as a reference image, one frame of a plurality of frame images, and determining, as non-reference images, remaining ones of the plurality of frame images other than the one determined as the reference image; performing a format conversion on the reference image and the non-reference images to obtain a format-converted reference image and format-converted non-reference images, and performing a noise addition processing on the format-converted reference image and each non-reference image respectively; aligning each noise-added non-reference image with a noise-added reference image to obtain an aligned non-reference image; annotating the format-converted reference image with pixel information to obtain a non-noise-added reference image; and acquiring the sample image set, the sample image set including the aligned non-reference image, the noise-added reference image, and

Abstract: An image processing method includes a matching cost calculating process of calculating matching costs in a unit of sub-pixels having higher resolution than first and second images by using an image of a reference area contained in the first image in which a target object is imaged and images of a plurality of comparison areas contained in the second image in which the target object is imaged, and a synthesized cost calculating process of calculating synthesized costs related to the reference area based on comparison results of values related to the plurality of matching costs calculated in the matching cost calculating process.