Abstract: A spring clamp marker assembly is composed of interconnecting spring clamp hose markers that provide a positive and easily changeable means for marking hoses used to maintain HVAC equipment and for any other suitable application. The interconnecting spring clamp hose markers may also be used with power cables or other linear items with a generally circular cross section.

Abstract: The present disclosure provides a determination method, an elimination method and an apparatus for an electron microscope aberration. The determination method comprises: training a neural network for image recognition using a plurality of electron microscope simulation images to obtain an electron microscope image recognition model; recognizing an electron microscope image of an experimental sample using the electron microscope image recognition model to obtain the electron microscope simulation image corresponding to the electron microscope image of the experimental sample; and obtaining the corresponding set aberration as an imaging aberration of the electron microscope image of the experimental sample according to the electron microscope simulation image corresponding to the electron microscope image of the experimental sample.

Abstract: Methods, systems, and apparatus for motion-based human video detection are disclosed. A method includes generating a representation of a difference between two frames of a video; providing, to an object detector, a particular frame of the two frames and the representation of the difference between two frames of the video; receiving an indication that the object detector detected an object in the particular frame; determining that detection of the object in the particular frame was a false positive detection; determining an amount of motion energy where the object was detected in the particular frame; and training the object detector based on penalization of the false positive detection in accordance with the amount of motion energy where the object was detected in the particular frame.

Abstract: An information processing apparatus (100) include a memory to store a plurality of background images; a sensor to acquire three-dimensional information including a plurality of images; and processing circuitry. The processing circuitry selects a recognition target from the three-dimensional information; cuts an image of a recognition target range corresponding to the selected recognition target from each of the plurality of images to generate a plurality of foreground images from the plurality of images; synthesizes each of the plurality of foreground images with each of the plurality of background images to generate a plurality of synthesized images. The processing circuitry generates recognition target range information indicating the recognition target range with three-dimensional positions of a plurality of points. The processing circuitry projects the recognition target range onto each of the plurality of images to specify the recognition target range in each of the plurality of images.

Abstract: A system and method for detection-guided tracking of human-dynamics is provided. The system receives an input human-dynamics sequence including geometry information and an RGB video of a human object. The system inputs the RGB video to the neural network and estimates a pose of the human object in each frame of the RGB video based on output of the neural network for the input. The system selects, from the input human-dynamics sequence, a key-frame for which the estimated pose is closest to a reference human pose. From the selected key-frame and up to a number of frames of the input human-dynamics sequence, the system generates a tracking sequence for a 3D human mesh of the human object. The generated tracking sequence includes final values of parameters of articulate motion and non-rigid motion of the 3D human mesh. Based on the generated tracking sequence, the system generates a free-viewpoint video.

Abstract: Techniques are described for inpainting of image data with a missing region. In an embodiment, at each iteration, the process determines a corresponding missing boundary region of the missing region and generates a collection of boundary patches for the missing boundary region. Based on comparing a boundary patch from the collection to source patches from a known source region of image data, the process generates replacement patches for the missing boundary region. When a boundary pixel data unit corresponds to multiple replacement pixel data units from different replacement patches, the process aggregates the multiple replacement pixel data units to generate an updated boundary pixel data unit. In an embodiment, the process performs convolution using the updated and previously known region of the image data.

Abstract: A securement and release device for webbing (SRDFW) comprised of two design configurations. Both configurations of the SRDFW allow a length of webbing to be quickly and securely attached to or removed from a structure. Both designs essentially function as a quick secure/release device into which webbing is inserted. Both configurations of the SRDFW are comprised of a securing and release assembly having a first side member, a second side member, an upper jaw, a center wedge and a lower jaw. Pivot rods extend outward from the jaws and the wedge. The pivot rods allow the jaws and the wedge to pivot into an open configuration to allow webbing to be inserted into/through the first assembly, and then to pivot into a closed position that locks the webbing within the assembly. The jaws and wedge can then pivot to remove the webbing.

Abstract: The present invention relates to the curation of map data. More particularly, the present invention relates to a method for preparing map data to present to a data curator for substantially optimal quality assurance. Further, the present invention relates to a tool for a data curator to verify map data. According to a first aspect, there is provided a method comprising: generating a plurality of interdependent map portions from a global map; determining, from the plurality of interdependent map portions, at least one interdependent map portion that requires validation; creating at least one group of interdependent map portions, the group of interdependent map portions comprising: the determined at least one interdependent map portion that requires validation; and at least one additional interdependent map portion; and outputting the at least one group of interdependent map portions for validation.

Abstract: The present application relates to an image processing method and system. The method may include: acquiring a sequence of input images containing a target object; and performing multi-resolution fusion on the sequence of input images to generate a single fused image, where pixels of the fused image may include a pixel at a corresponding position of an input image in the sequence of input images, and each pixel of the fused image containing the target object may include a pixel at a corresponding position of an input image in the sequence of input images in which part of the target object is focused.

Abstract: Embodiments relate to a method for real-time facial animation, and a processing device for real-time facial animation. The method includes providing a dynamic expression model, receiving tracking data corresponding to a facial expression of a user, estimating tracking parameters based on the dynamic expression model and the tracking data, and refining the dynamic expression model based on the tracking data and estimated tracking parameters. The method may further include generating a graphical representation corresponding to the facial expression of the user based on the tracking parameters. Embodiments pertain to a real-time facial animation system.

Abstract: A method for automatically determining quality of registration of landmarks includes training an artificial intelligence (AI) system to detect inaccurate registration of landmarks. Training the AI system uses training data that includes scans of an environment captured by a 3D measuring device from corresponding scan points. A first scan is registered with at least a second scan based on one or more landmarks captured in the first scan and the second scan. Further, a model is created to identify incorrect registration by analyzing the training data. The analysis detects a mismatch in a first instance of a landmark in the first scan and a second instance of said landmark in the second scan. The model is then used to evaluate registration of landmarks in live data, the live data including a set of scans, the result identifying accuracy level of the registration of landmarks.

Abstract: In an approach to non-destructive inspection of IC devices, one or more images of a Device Under Test (DUT) are received from one or more imaging devices. Observed features are detected in the one or more images and producing a first synthetic representation of a part design of the DUT that includes the observed features. The presence of one or more first unobserved features are inferred, where the one or more first unobserved features are inferred using a mapping and inference model (MIM). The one or more first unobserved features are added to the first synthetic representation of the part design of the DUT.

Abstract: In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective property rankings.

Abstract: An automatic system for detecting a damage to a vehicle, comprising: (a) at least one camera for capturing “handover” and “return” images irrespective of the vehicle's movement or orientation relative to the camera; (b) a memory for storing images of basic-parts (c) a first determination unit for, based on the basic-parts images, determine the location of one or more basic-parts within the “handover” and “return” images; (d) a second determination unit configured to, based on the determined locations of the basic parts within the images, determine the locations of “other parts” within the “handover” and “return” images, thereby to form “handover” and “return” part-images, respectively; (e) a transformation unit configured to separately transform each pair of “handover” and “return” part-images, respectively, to a same plane; (f) a comparison unit configured to separately compare pixel-by-pixel each transformed pairs of “handover” and “return” part-images, to detect a difference above a predefined threshold.

Abstract: The present invention makes an efficient diagnosis of an object. A general server 3 of a diagnostic assistance system 1 provides data (a large volume of photographic images) concerning an object managed by each client, to said client and a plurality of analysts and experts, and allows such diagnosers to diagnose the object and enables sharing of diagnosis results among the diagnosers. The plurality of analysts each partially contribute in viewing the large volume of photographic images so as to find an abnormal site in the object. The client and the experts then conduct a more detailed diagnosis on the abnormal site discovered by the analysts. An AI server 5 of the diagnostic assistance system 1 creates training data from the diagnosis results provided by the plurality of diagnosers, performs machine learning on the diagnosis results, and carries out automated diagnosis using the learned method.

Abstract: Techniques for synthesizing intermediary frames for long exposure images are disclosed. Video stream data, comprising a plurality of arriving frames, is received. An indication that the video stream data should be processed into a long exposure image that incorporates one or more synthesized frames is received. A set of arriving frames is used to generate at least one synthesized frame. At least one received frame is blended with the at least one generated synthesized frame to form a long exposure image. The long exposure image is provided as output.

Abstract: The invention relates to a system for identifying at least one object at least partially immerged in a water area, said system comprising a capturing module comprising at least one camera, said at least one camera being configured to generate at least one sequence of images of said water area, and a processing module being configured to receive at least one sequence of images from said at least one camera and comprising at least one artificial neural network, said at least one artificial neural network being configured to detect at least one object in said at least one received sequence of images, extract a set of features from said at least one detected object, compare said extracted set of features with at least one predetermined set of features associated with a predefined object, identify the at least one detected object when the extracted set of features matches with the at least one predetermined set of features.

Abstract: An image processing device receives a caption for an image before, during, or after capture of the image by an image capture device. The image processing device generates image processing settings based on the caption, for instance based on a mood indicated in the caption or an object identified in the caption. If the caption is received before image capture, the image processing settings may include image capture settings that the image capture device may use to alter exposure or focus during image capture. Once the image is captured, the image processing device may process the image based on the image processing settings, for instance by applying filters or adjusting gain, brightness, contrast, saturation, or colors. For instance, brightness and saturation may be altered if the caption indicates a happy or sad mood, and focus may be altered to focus on an object identified in a caption.

Abstract: Disclosed herein are systems, devices, and processes for gesture detection. A method includes capturing a series of images. The method includes generating motion isolation information based on the series of images. The method includes generating a composite image based on the motion isolation information. The method includes determining a gesture based on the composite image. The processes described herein may include the use of convolutional neural networks on a series of time-related images to perform gesture detection on embedded systems or devices.

Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.