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: The invention relates to a new approach to characterize, model, and find Buckshot anomalies within LiDAR point cloud dataset collected with Geiger-mode Avalanche Photodiode (GmAPD) LiDAR platforms.

Abstract: A method of anatomic classification of blood vessel anatomy in aneurysms includes the steps of accessing an image of a blood vessel, analyzing the image to identify a point of divergence of the blood vessel and one or more additional points of reference of the blood vessel. One or more distances are then measured between the point of divergence of the blood vessel and at least one of the one or more additional points of reference. The aneurysm is then classified based upon the measured distances. A method of treatment of an aneurysm in a blood vessel further includes implanting one or more devices within the blood vessel for treatment of the aneurysm.

Abstract: The present invention addresses the problem of providing an image processing system that can accurately and efficiently detect a target object even when the positional relationship between an image capturing device and the target object differs between the time of teaching and the time of detection.

Abstract: A method for performing a procedure on a patient includes acquiring a three-dimensional image of a location of interest on the patient and a two-dimensional image of the location of interest can be acquired. A computer system can relate the three-dimensional image with the two-dimensional image to form a holographic image dataset. The computer system can register the holographic image dataset with the patient. The augmented reality system can render a hologram based on the holographic image dataset from the patient. The hologram can include a projection of the three-dimensional image and a projection of the two-dimensional image. The practitioner can view the hologram with the augmented reality system and perform the procedure on the patient. The practitioner can employ the augmented reality system to visualize a point on the projection of the three-dimensional image and a corresponding point on the projection of the two-dimensional image during the procedure.

Abstract: Blockchain environments may mix-and-match different encryption, difficulty, and/or proof-of-work schemes when mining blockchain transactions. Each encryption, difficulty, and/or proof-of-work scheme may be separate, stand-alone programs, files, or third-party services. Blockchain miners may be agnostic to a particular coin's or network's encryption, difficulty, and/or proof-of-work schemes, thus allowing any blockchain miner to process or mine data in multiple blockchains. GPUs, ASICs, and other specialized processing hardware components may be deterred by forcing cache misses, cache latencies, and processor stalls. Hashing, difficulty, and/or proof-of-work schemes require less programming code, consume less storage space/usage in bytes, and execute faster. Blockchain mining schemes may further randomize byte or memory block access, further improve cryptographic security.

Abstract: A method of compiling a deep learning model includes reading metadata from a compiled result, the metadata indicating a structure of the deep learning model corresponding to a low-level IR, receiving shape information of an input tensor of the deep learning model, determining a shape of an output tensor of a first computation operation of the computation operations based on the shape information of the input tensor of the deep learning model and the structure of the deep learning model, tiling the output tensor of the first computation operation into one or more tiles according to the shape of the output tensor of the first computation operation and hardware limitations of a processor executing the deep learning model, and patching one or more copies of a templated hardware command into executable hardware commands.

Abstract: Blockchain environments may mix-and-match different encryption, difficulty, and/or proof-of-work schemes when mining blockchain transactions. Each encryption, difficulty, and/or proof-of-work scheme may be separate, stand-alone programs, files, or third-party services. Blockchain miners may be agnostic to a particular coin's or network's encryption, difficulty, and/or proof-of-work schemes, thus allowing any blockchain miner to process or mine data in multiple blockchains. GPUs, ASICs, and other specialized processing hardware components may be deterred by forcing cache misses, cache latencies, and processor stalls. Hashing, difficulty, and/or proof-of-work schemes require less programming code, consume less storage space/usage in bytes, and execute faster. Blockchain mining schemes may further randomize byte or memory block access, further improve cryptographic security.

Abstract: The disclosure includes a system and method for detecting a synthetically generated image including receiving a first image; generating, based on the first image, a first image attribute distribution; and determining, based on the first image attribute distribution, whether the first image is synthetically generated; and issuing, responsive to determining that the first image is synthetically generated, a rejection.

Abstract: In one embodiment, systems and methods include using an inspection and projection system to measure the thickness of a coating and provide visual guidance for secondary operations. The inspection and projection system comprises a robotic arm operable to rotate about a plurality of axes, wherein an end effector is disposed at a distal end of the robotic arm. The inspection and projection system further comprises a linear rail system, wherein the robotic arm is coupled to the linear rail system, and wherein the robotic arm is operable to translate along the linear rail system. The inspection and projection system further comprises a frame, wherein the linear rail system is disposed on top of the frame, and an information handling system coupled to the frame, wherein the information handling system is operable to actuate the robotic arm and the linear rail system.

Abstract: An agricultural harvester includes a plurality of controllable subsystems and a forward-looking crop sensor that detects a characteristic of the crop in front of the harvester. The forward-looking sensor generates a first sensor signal indicative of the detected characteristic. The harvester further includes a component sensor that detects a characteristic of a component of the agricultural harvesting machine and generates a second sensor signal indicative of the detected characteristic. Adaptation logic receives the first and second sensor signals and determines a sensor conversion factor intermittently during operation of the agricultural harvester. Recommendation logic receives the conversion factor and generates a recommendation to change operation of a controllable subsystem, based in part on the calculated conversion factor and a value received from the forward-looking crop sensor. A control system controls the controllable subsystem based on the generated recommendation.

Abstract: A radiological imaging method including: 2 radiation sources with imaging directions orthogonal to each other, performing vertical scanning of a standing patient along a vertical scanning direction, wherein the radiological method includes at least one operating mode in which: a frontal scout view is made so as to identify a specific bone(s) localization within the frontal scout view, both driving current intensity and voltage intensity modulations of the frontal radiation source, depending on patient thickness and on the identified specific bone(s) localization along the vertical scanning direction, are performed simultaneously, preferably synchronously, and automatically, so as to improve a compromise between: lowering the global radiation dose received by a patient during the vertical scanning, and increasing the local image contrasts of the identified specific bone(s) localization at different imaging positions along the vertical scanning direction, for the frontal image.

Abstract: A computer-implemented method for training a machine learning network includes receiving an input data from one or more sensors, selecting one or more batch samples from the input data, wherein the batch samples include one or more perturbed samples from a source class configured to be misclassified into a target class, identifying the one or more perturbed samples from the one or more batch samples, determining a trigger event in response to identification of a trigger pattern of the one or more batch samples, wherein the trigger pattern induces a pre-determined response on a classifier, outputting a classification in response to identification of the trigger pattern via the classifier, and outputting a set of trigger patterns extracted from the machine-learning network.

Abstract: An estimation step according to an embodiment causes a computer to execute: a calculation step of using a plurality of images obtained by a plurality of imaging devices imaging a three-dimensional space in which a plurality of objects reside, to calculate representative points of pixel regions representing the objects among pixel regions of the images; a position estimation step of estimating positions of the objects in the three-dimensional space, based on the representative points calculated by the calculation step; an extraction step of extracting predetermined feature amounts from image regions representing the objects; and an attitude estimation step of estimating attitudes of the objects in the three-dimensional space, through a preliminarily learned regression model, using the positions estimated by the position estimation step, and the feature amounts extracted by the extraction step.

Abstract: Various implementations disclosed herein include devices, systems, and methods that generate a combined 3D representation of a user. For example, a process may include obtaining a first three-dimensional (3D) representation of a first portion of a user. The process may further include obtaining a sequence of frame-specific second 3D representations in a period of time, each of the frame-specific second 3D representations represent a second portion of the user. The process may further include generating a combined 3D representation of the user for the period of time by modifying the first 3D representation with a respective frame-specific second 3D representation.

Abstract: A method for processing point cloud data according to embodiments may encode and transmit point cloud data. The method for processing point cloud data according to embodiments may receive and decode the point cloud data.

Abstract: Embodiments identify joints of a multi-limb body in an image. One such embodiment unifies depth of a plurality of multi-scale feature maps generated from an image of a multi-limb body to create a plurality of feature maps each having a same depth. In turn, for each of the plurality of feature maps having the same depth, an initial indication of one or more joints in the image is generated. The one or more joints are located at an interconnection of a limb to the multi-limb body or at an interconnection of a limb to another limb. To continue, a final indication of the one or more joints in the image is generated using each generated initial indication of the one or more joints.

Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving, by a processing device, image data including one or more image frames indicative of a current state of a drive-thru area. The processing device determines a vehicle disposed within the drive-thru area based on the image data. The processing device receives order data with a pending meal order. The processing device determines a first association between the vehicle and the pending meal order based on the image data. The processing devices determine a meal delivery procedure associated with the based on the association between the vehicle and the pending meal order. The processing device performs may perform the meal delivery procedure. The processing device may provide the meal delivery procedure for display on a graphical user interface (GUI).

Abstract: According to one embodiment, an estimation device includes an estimation unit, a control unit, and a SLAM unit. The estimation unit estimates whether motion of a moving object is pure rotation from a plurality of first monocular images acquired by a monocular camera mounted on the moving object. The control unit determines the translation amount for translating the monocular camera in a case in which the motion of the moving object is pure rotation. The SLAM unit estimates at least one of a location or an orientation of the monocular camera, and three-dimensional points of a subject of the monocular camera by monocular simultaneous localization and mapping (SLAM), from the first monocular images and a second monocular image acquired by the monocular camera that is translated based on the translation amount.

Abstract: Heart strain determination includes receiving a series of 2D-slice images as input. A pose estimation module estimates a slicing-pose of the inputted series of 2D-slice images in the heart. A 3D deformation estimation module estimates a 3D deformation field from the series of 2D-slice images and the estimated slicing-pose. A strain measurement module computes a heart strain measure from the 3D deformation field and a predefined definition for strain computation.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) receive image data captured from one or more devices, (ii) based on the image data, generate at least two batches of data corresponding to an area of a global map, wherein each batch of data comprises (a) a respective group of images from the received image data, and (b) one or more common images comprising one or more common visual features, (iii) generate a respective reconstruction of the area of the global map for each of the at least two batches of data, and (iv) fuse the respective reconstructions of the area of the global map using the one or more common visual features from the one or more common images.

Abstract: A computer vision system and method for detecting and modeling features of a building in a plurality of images is provided. The system includes at least one computer system in communication with a database of aerial imagery, and computer vision system code executed by the at last one computer system which automatically detects contours and infers interior roof features of the building. The system first processes the plurality of images to identify a plurality of two-dimensional (2D) line segments in each image. Then, the system processes the plurality of 2D line segments to generate a plurality of three-dimensional (3D) line segments. The plurality of 2D line segments are then processed to detect a contour of the structure, and the contour of the structure is utilized by the system to infer interior roof lines from the structure. A model of the roof of the structure is finally generated using the detected contour and interior roof lines.

Abstract: Disclosed are a training data generation method and apparatus for an artificial intelligence visual analysis model. The training data generation method includes: (a) receiving a background image and object-related information; (b) generating a target object image that meets a condition based on the object-related information; (c) applying the background image to a depth estimation model to estimate depths of each pixel location and generate a depth map; (d) determining an insertion location and scale at which the target object image is inserted based on the depth map to generate a local object insertion image and a mask image, respectively; (e) simply synthesizing the local object insertion image and the background image and then generating a synthesis image reflecting a scale feature of the background image through a trained multi-scale visual analysis model; and (f) generating a final training image using the background image, the synthesis image, and the mask image.

Abstract: An illustrative point cloud compression system accesses an input point cloud dataset representative of a point cloud comprising a plurality of points. The point cloud compression system identifies a first attribute dataset and a second attribute dataset within the input point cloud dataset. Based on an application of a transform algorithm to the first and second attribute datasets, respectively, the point cloud compression system generates 1) a first low-frequency component and a first high-frequency component of the first attribute dataset, and 2) a second low-frequency component and a second high-frequency component of the second attribute dataset. The point cloud compression system then generates an output point cloud dataset that prioritizes both the first and second low-frequency components above both the first and second high-frequency components. Corresponding methods and systems are also disclosed.

Abstract: A marker (40) for image guided surgery, consisting of a base (94), having a base axis, connecting to a clamp (42), and an alignment target (44). The alignment target includes a target region (120) having an alignment pattern formed thereon, and a socket (124) connected to the target region and configured to fit rotatably to the base, whereby the alignment target is rotatable about the base axis. The alignment target also includes an optical indicator (162) for the socket indicating an angle of orientation of the alignment target about the base axis.

Abstract: Compressing a frame of video includes receiving a frame of a video, identifying a three dimensional (3D) object in the frame, matching the 3D object to a stored 3D object, compressing the frame of the video using a color prediction scheme based on the 3D object and the stored 3D object, and storing the compressed frame with metadata, the metadata identifying the 3D object, indicating a position of the 3D object in the frame of the video and indicating an orientation of the 3D object in the frame of the video.

Abstract: The present disclosure relates to the field of artificial intelligence (AI) and neural rendering, and particularly to a method of generating a multi-layer representation of a scene using neural networks trained in an end-to-end fashion and to a computing device implementing the method. The method of generating a multi-layer representation of a scene includes: obtaining a pair of images of the scene, the pair of the images comprising a reference image and a source image; performing a reprojection operation on the pair of images to generate a plane-sweep volume; predicting, using a geometry network, a layered structure of the scene based on the plane-sweep volume; and estimating, using a coloring network, color values and opacity values for the predicted layered structure of the scene to obtain the multi-layer representation of the scene; wherein the geometry network and the coloring network are trained in end-to-end manner.

Abstract: According to one embodiment, an information processing system includes a processor configured to detect a first object in an image in time-series image data and output object information indicating a detection of the first object. The processor calculates a movement status of one or more pixels included in the image by using a plurality of images in the time-series image data, and then outputs movement information indicating the movement status of the one or more pixels. The processor then detects whether a second object moves in conjunction with the first object based on the object information and the movement information.

Abstract: An object amount calculation apparatus includes: a receiver configured to obtain a first three-dimensional model and a second three-dimensional model different from the first three-dimensional model, each of the first three-dimensional model and the second three-dimensional model representing a same space, each of the first three-dimensional model and the second three-dimensional model being constituted with regions having respective attributes; and a processor configured to: align the first three-dimensional model and the second three-dimensional model based on at least one attribute of the first three-dimensional model and the second three-dimensional model; calculate, for each of the attributes, a difference between the first three-dimensional model aligned and the second three-dimensional model aligned; and output (i) a total amount of differences corresponding to two or more attributes among the attributes and (ii) information on the two or more attributes.

Abstract: A method is provided for transposition of a detected object and its tracking from a 3D sensor to a different system in 6 degrees of freedom. The method includes receiving sensing data from the one or more 3D sensors, generating 3D cartesian coordinates of reflections and intensities with respect to the respective 3D sensors, searching, detecting, classifying, segmenting, tracking and forecasting motion of objects in the 3D surrounding keeping the respective 3D sensor as origin, transposing the origin for the coordinates of the searched, detected, classified and tracked object in real time to one or more secondary devices, and providing the real time detection, classification and tracking of the object to a user context application with the one or more secondary devices as origin, without any delay real time covering all 6 Degrees of Freedom.

Abstract: A visibility improvement method using gaze tracking includes detecting a gaze of a user using a camera; determining a focus object at which the user gazes from among at least one object viewed through a display unit of an electronic device; and displaying, on the display unit, an image with high visibility that corresponds to the focus object and has higher visibility than the focus object.

Abstract: Provided is a method of simultaneous localization and mapping (SLAM), the method including obtaining a structure of a current image frame, obtaining a structure re-identification constraint based on the structure of the current image frame, and obtaining a SLAM result based on the structure re-identification constraint. By introducing the structure re-identification constraint in a bundle adjustment process to obtain the SLAM result, error accumulation may be more easily controlled, and accuracy and robustness in obtaining the SLAM result may be improved.

Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.

Abstract: An X-ray imaging apparatus includes an X-ray generator including a plurality of X-ray sources, an X-ray detector configured to detect X-rays radiated from the plurality of X-ray sources and generate a plurality of pieces of projection data, and a processor configured to apply log projection to each of the plurality of pieces of projection data, to apply weighted projection to the log-projected projection data, to apply a bidirectional ramp filter to the weighted-projected projection data, and to generate a tomographic image reconstructed based on each of the projection data to which the bidirectional ramp filter is applied.

Abstract: The application relates to a device for producing three-dimensional workpieces, the device including: a structural surface designed to receive a molding compound; and an irradiation arrangement designed to selectively irradiate the molding compound on the structural surface with electromagnetic radiation, in order to produce a workpiece by means of generative layer construction, where the irradiation device comprises a plurality of irradiation units, the irradiation units being designed to irradiate an individual region of the structural surface respectively associated with the irradiation units, and where the beams emitted by the irradiation units respectively have a cross-sectional surface corresponding to between approx. 2% and approx. 170% of the surface of the respectively associated individual region. The application also relates to the use of such a device and to a method for producing three-dimensional workpieces by means of such a device.

Abstract: Systems and methods for controlling an Internet of Things (IoT) device through interaction with an augmented reality (AR) object includes pairing an AR object with an IoT device, presenting the AR object on a display of an AR camera device of a user, receiving an interaction signal indicating that the user has interacted with the AR object on the display of the AR camera device, and sending a control signal to the IoT device paired with the AR object in response to the interaction signal. A second user may request presentation of the AR object to the AR display of the AR camera device of the user when the user's AR camera device is located at particular world coordinates. Also, the control signal may be sent when a particular series of interactions with the AR object have been completed, as during game play.

Abstract: A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.

Abstract: The present invention provides a scanning system including a chamber configured for receiving reflected light from an object. The chamber includes a first prism and a second prism configured for refracting the reflected light, at least one beam splitter configured for splitting the reflected light into a first beam and a second beam, a camera configured for receiving the first beam so as to provide images of the object, a condenser lens configured for condensing the second beam, and a spectrometer configured for receiving the condensed second beam and provide a spectrum analysis of the second beam. The system further includes a particular computer configured to combine the images of the object produced by the camera with the spectrum analysis produced by the spectrometer to provide a hyperspectral image.

Abstract: A system and method for acquiring an image. The method includes dropping an object into free fall, detecting the dropping of the object, triggering a plurality cameras to simultaneously image the object in parallel, while the object drops into a bottom half of an imaging sphere at a center of a field of view of each of the plurality of cameras, upon detecting the dropping of the object, analyzing images of the imaged object in parallel, based on a trained machine learning model, and displaying a three dimensional image of a surface of the object based on the analysis of the images of the imaged object.

Abstract: Examples are disclosed herein relating to signal processing in a time of flight (ToF) system. One example provides, a method comprising emitting, via a light source, amplitude-modulated light toward an object, acquiring, via an image sensor comprising a plurality of pixels, a plurality of image frames capturing light emitted from the light source that is reflected by the object, wherein the plurality of image frames are acquired at two or more different frequencies of the amplitude-modulated light and collectively form a multifrequency frame, and for each pixel of the multifrequency frame, determining a brightness level, applying an adaptive denoising process by setting a kernel size based on the brightness level, and performing a phase unwrapping process to determine a depth value for the pixel.

Abstract: Disclosed are methods and systems for generating 3D avatars. The method comprises inputting a 2D image comprising an object of interest comprising a face; calculating a set of 2D parameters of the object of interest; inputting the set of 2D parameters into a trained neural network; the neural network outputting an estimated set of 3D avatar parameters, said parameters representing deviations with respect to 3D parameters of a benchmark 3D head model; and applying the set of 3D avatar parameters to the benchmark 3D head model to obtain the 3D avatar.

Abstract: A vehicle trajectory planning system includes a perception system of a host vehicle collecting information from multiple sources and communicating with a computer. A fusion module fuses scene information from a map and perception items identified by the perception system. A behavior planning module receives an output of the fusion module and produces a host vehicle baseline trajectory. A trajectory and motion planning module receives an output of the fusion module in parallel with the behavior planning module. The trajectory and motion planning module determines a reference trajectory and operation corridor for a host vehicle. A disturbance and reachability refiner module receives an output of the trajectory and motion planning module including the reference trajectory and operation corridor. An algorithm is applied to adjust and re-plan the host vehicle baseline trajectory to be robust to a range of inclement weather conditions.

Abstract: A method may include receiving a first image of a scene captured by a first camera at a first time step and plurality of other images of the scene captured by a plurality of other cameras at a plurality of time steps, determining a geometric relationship between the first camera at the first time step and each of the other cameras at the plurality of time steps, determining a cost volume for the first image captured by the first camera at the first time step based on the first image, the plurality of other images, and the geometric relationship between the first camera at the first time step and each of the other cameras at the plurality of time steps, and determining a depth map for the first image based on the cost volume, the depth map comprises a depth value of each pixel of the first image.

Abstract: A method for simulating, at an instant, the illumination of an indoor scene observed by a camera and being illuminated by outdoor light, the method comprising: a first preliminary phase including: obtaining a reflectance map of the scene elaborating normalized radiosity maps for the contribution of the sky and the ground, a second phase carried out within a first given duration from the instant including: obtaining the position of the sun, obtaining a normalized radiosity map for the contribution of the sun, a third phase, including: acquiring an image of the scene, determining brightness scale parameters for the ground, the sky, and the sun. The disclosure also proposes tracking the camera position.

Abstract: The disclosure discloses a method for building a local point cloud map and a visual robot. According to the method for building the local point cloud map, point clouds which reflect a position of a large-range region around a robot are sampled according to a preset salient pose change condition; point clouds at different height positions are described in the form of a 3d histogram.

Abstract: A computer-implemented method comprises receiving, by an image processing system, a depth image captured by a stereo camera on an unmanned aerial vehicle (UAV). One or more pixels of the depth image are associated with corresponding depth values indicative of distances of one or more objects to the stereo camera. The image processing system determines that one or more pixels of the depth image are associated with invalid depth values. The image processing system infers, based on a distribution of the one or more pixels of the depth image that are associated with invalid depth values, a presence of a potential obstacle in an environment of the UAV. The UAV is controlled based on the inferred presence of the potential obstacle.

Abstract: Implementations are disclosed for dynamically allocating aspects of platform-independent machine-learning based agricultural state machines among edge and cloud computing resources. In various implementations, a GUI may include a working canvas on which graphical elements corresponding to platform-independent logical routines are manipulable to define a platform-independent agricultural state machine. Some of the platform-independent logical routines may include logical operations that process agricultural data using phenotyping machine learning model(s). Edge computing resource(s) available to a user for which the agricultural state machine is to be implemented may be identified. Constraint(s) imposed by the user on implementation of the agricultural state machine may be ascertained.

Abstract: Systems and methods for generating a prediction of a body composition of a user using an image capturing device are disclosed. The systems and methods can be used to predict body compositions such as body fat percentage, water content percentage, muscle mass, bone mass, and so on, from a single user image. The methods include the steps of receiving one or more user images and one or more user parameters, generating one or more key points based on the one or more user images, and generating a prediction of the body composition of the user based on the one or more key points and the one or more user parameters, using a body composition deep learning network (DLN). In one embodiment, the body composition DLN comprises a face image DLN, a body feature DLN, and an output DLN.

Abstract: There is provided with an image capturing control apparatus. A first storing unit configured to store, in advance, first signal information that is obtained by processing a captured first image. A first processing unit configured to send the first image to a second storing unit that is different from the first storing unit. A second processing unit configured to process the first image stored in the second storing unit. A determination unit configured to determine whether or not at least one of the second processing unit and the second storing unit is anomalous based on whether the first signal information matches second signal information obtained by the second processing unit processing the first image.

Abstract: A photo filter (e.g., artistic) light field effect system comprises an eyewear device that includes a frame, a temple connected to a lateral side of the frame, and a depth-capturing camera. Execution of programming by a processor configures the photo filter light field effect system to apply a photo filter selection to: (i) a left raw image or a left processed image to create a left photo filter image, and (ii) a right raw image or a right processed image to create a right photo filter image. The photo filter light field effect system generates, a photo filter light field effect image with an appearance of a spatial rotation or movement, by blending together the left photo filter image and the right photo filter image based on a left image disparity map and a right image disparity map.