Abstract: A method to automatically remove obstructions, such as electrical cords or wires, from robotic floor-cleaning devices after becoming entangled with wheels of the robotic device during operation. Upon sensing entanglement with an obstruction, the robotic floor-cleaning device is configured to retract one or more wheels into a wheel housing within the main housing of the robotic device. During retraction, the obstruction entangled around the one or more wheels is forcibly separated from the wheel by the corresponding wheel housing.

Abstract: Examples disclosed herein relate to determining image capture position information based on a quasi-periodic pattern. For example, a processor may determine whether a target area is within a captured image based on the detection of a quasi-periodic pattern in a first detection area and in a second detection area of the captured image.

Abstract: A local connectivity feature transform (LCFT) is applied to binary document images containing text characters, to generate transformed document images which are then input into a bi-directional Long Short Term Memory (LSTM) neural network to perform character/word recognition. The LCFT transformed image is a gray scale image where the pixel values encode local pixel connectivity information of corresponding pixels in the original binary image. The transform is one that provides a unique transform score for every possible shape represented as a 3×3 block. In one example, the transform is computed using a 3×3 weight matrix that combines bit coding with a zigzag pattern to assign weights to each element of the 3×3 block, and by summing up the weights for the non-zero elements of the 3×3 block shape.

Abstract: Aspects of the disclosure generally relate to determining the location and orientation of panoramic images by a computing apparatus. One or more computing devices may receive alignment data between a first panoramic image and second panoramic image and original location data for the first panoramic image and the second panoramic image. The one or more computing devices may determine relative orientations between the pair of panoramic images based on the alignment data and calculate a heading from the first panoramic image to the second panoramic image based on the original location data. The location data and alignment data may be optimized by the one or more computing devices based on the relative orientations between the pair of panoramic images and the original location data. The one or more computing devices may replace the original location data and relative orientations with the optimized relative orientations and optimized location data.

Abstract: A method for computing patient-specific hemodynamics. The method includes receiving three dimensional imaging data of a patent, extracting anatomical data from the three dimensional imaging data, calculating velocity and pressure fields corresponding to the extracted anatomical data, and calculating displacement and velocity of extracted solid particles corresponding to the anatomical data. The anatomical data comprises an anatomical boundary.

Abstract: Described herein are systems and methods for marking a dental aligner. A method for marking a dental aligner includes receiving a digital model corresponding to a dental aligner. The digital model includes a dental arch comprising a plurality of teeth. The method further includes determining whether a section of teeth on a right side or a left side of the dental arch includes flatter occlusal surfaces and selecting the section with the flatter occlusal surfaces, identifying surfaces on the teeth of the selected section that are flat relative to other surfaces on the teeth of the selected section, determining a line of best fit between the flat surfaces, and marking the dental aligner with a marking based on the line of best fit.

Abstract: An imaging system is provided that includes at least one detector configured to acquire imaging information, a processing unit, and a display unit. The processing unit is operably coupled to the at least one detector, and is configured to reconstruct an image using the imaging information. The image is organized into voxels having non-uniform dimensions. The processing unit is configured to perform a penalized likelihood (PL) image reconstruction using the imaging information. The PL image reconstruction includes a penalty function. Performing the penalty function includes interpolating a voxel size in at least one dimension from an original size to an interpolated size before determining a penalty function, determining the penalty function using the interpolated size to provide an initial penalty, interpolating the initial penalty to the original size to provide a modified penalty, and applying the modified penalty in the PL image reconstruction.

Abstract: A positioning and measuring system includes: an image capturing device performing image capturing operations on an object; a driving mechanism mechanically connected to one or both of the image capturing device and the object to cause a relative movement between the image capturing device and the object; and a processor electrically connected to the image capturing device and the driving mechanism and performing steps of: controlling the image capturing device to capture N portions of the object to generate N images before and after controlling the driving mechanism to cause M relative movements between the object and the image capturing device; and extracting feature points of each of the N images corresponding to unique surface morphology of the object and respectively performing cross-image feature point matching according to the feature points of every neighboring two of the N images to obtain information of the M relative movements.

Abstract: A method for adjusting grayscale values of an image includes: for each pixel in the image, calculating a first mapping value for a grayscale value of the pixel in the image through a histogram equalization mapping function; calculating a second mapping value by subtracting a first preset value from the grayscale value of the pixel through the histogram equalization mapping function, and when the result exceeds a preset grayscale range, selecting a minimum value of the preset grayscale range for calculating the second mapping value; calculating a third mapping value by adding a second preset value to the grayscale value of the pixel through the histogram equalization mapping function and when the result exceeds the preset grayscale range, selecting a maximum value of the preset grayscale range; and calculating an output grayscale value of the pixel based on the first, second, and third mapping value and respective weights.

Abstract: An object recognition processing apparatus includes: an object fusion processing unit 6 for fusing detection information signals of objects existing around a vehicle, which are detected by a plurality of object detection units to output object fusion signals; a same object fusion result combination candidate selection processing unit for selecting, for a combination of object fusion signals, candidates of the object fusion signals to be combined; a same object fusion result combination determination processing unit for determining whether or not a combination of the selected object fusion signals is a combination of object fusion signals of the same object; and a same object fusion combination integration processing unit for recognizing the objects existing around the vehicle on the basis of a result of the determination.

Abstract: The present disclosure generally relates to a device and a method of decoding a color picture from a bitstream.

Abstract: A method for pseudo coloring of a digital single-channel image is disclosed. The method comprises acquiring a single-channel image having an intensity distribution, forming a first channel of a multi-channel image by performing a first histogram equalization of the intensity distribution, forming a second channel of the multi-channel image by performing a second, different, histogram equalization of the intensity distribution, wherein the first channel and the second channel of the multi-channel image represent different components of a color model. An arrangement for providing a pseudo coloring of a digital single-channel image is also disclosed, and a monitoring thermal camera system comprising such an arrangement.

Abstract: [Object]To avoid an image quality deterioration due to image processing while maintaining a resolution of an image in a variable-rate-controlled stream. [Solving Means]An image processing apparatus includes a resolution acquisition section, an image enlargement and reduction section, and an image conversion section. The resolution acquisition section acquires a resolution of each of a plurality of temporally-consecutive images. An enlargement and reduction percentage generation section generates an enlargement and reduction percentage for each of the plurality of images based on the acquired resolution. The image enlargement and reduction section performs image enlargement and reduction processing on each of the plurality of images according to the generated enlargement and reduction percentage. The image conversion section performs image conversion processing on each of the plurality of images subjected to the image enlargement and reduction processing.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for assessing document image quality are disclosed. In one aspect, a method includes the actions of receiving one or more images of a document. The actions further include detecting an object that has a shape that is similar to an expected shape of the document. The actions further include determining a focus score that reflects a sharpness of the first image. The actions further include determining a border fit score that reflects a likelihood that the object corresponds to the document. The actions further include determining an expected shape score that reflects a correlation between the shape of the object and an expected shape of the document. The actions further include determining a quality score that reflects a quality of the first image. The actions further include determining whether to perform document authentication on the first image.

Abstract: An image processing method includes correcting a target image based on an initial distortion coefficient to obtain a first corrected target image, performing straight-line fitting on a first border line in the first corrected target image to calculate a first distortion metric value and a correction distortion coefficient, correcting the target image based on the correction distortion coefficient to obtain a second corrected target image, performing straight-line fitting on a second border line in the second corrected target image to calculate a second distortion metric value, detecting whether a preset correction condition is satisfied based on at least one of the first distortion metric value or the second distortion metric value, and configuring the correction distortion coefficient as the initial distortion coefficient if the preset correction condition is not satisfied.

Abstract: The correlation of optical images with SEM images includes acquiring a full optical image of a sample by scanning the sample with an optical inspection sub-system, storing the full optical image, identifying a location of a feature-of-interest present in the full optical image with an additional sources, acquiring an SEM image of a portion of the sample that includes the feature at the identified location with a SEM tool, acquiring an optical image portion at the location identified by the additional source, the image portions including a reference structure, correlating the image portion and the SEM image based on the presence of the feature-of-interest and the reference structure in both the image portions and the SEM image, and transferring a location of the feature-of-interest in the SEM image into the coordinate system of the image portion of the full optical image to form a corrected optical image.

Abstract: An electronic apparatus includes a compilation unit structured to receive a plurality of different 3-D models of a patient's face, to compare the different 3-D models of the patient's face and to determine additional information about the patient's face based on the comparison, wherein the additional information includes at least one of a location of hard tissue, a depth of soft tissue, and a compliance of soft tissue, and wherein the patient's face is manipulated between the different 3-D models.

Abstract: Examples herein disclose capturing an image of printed text and a handwritten annotation and determining a topic as related to the printed text in the captured image. The examples disclose identifying a resource based on the handwritten annotation.

Abstract: Original or compressed Auxiliary Data, including possibly major depth discontinuities in the form of shape images, partial occlusion data, associated tuned and control parameters, and depth information of the original video(s), are used to facilitate the interactive display and generation of new views (view synthesis) of conventional 2D, stereo, and multi-view videos in conventional 2D, 3D (stereo) and multi-view or autostereoscopic displays with reduced artifacts. The partial or full occlusion data includes image, depth and opacity data of possibly partially occluded areas to facilitate the reduction of artifacts in the synthesized view. An efficient method is used for extracting objects at partially occluded regions as defined by the auxiliary data from the texture videos to facilitate view synthesis with reduced artifacts.

Abstract: The present disclosure generally relates to a method and device of encoding a color picture having color components (Ec) comprising obtaining (11) a luminance component (L) and two chrominance components (C1, C2) from the color picture to be encoded. The method for encoding a color picture having color components comprising obtaining at least one chrominance component from the color picture to be encoded, the method further comprises: ?—determining a first factor based on the value of each pixel (i) of said luminance component; ?—obtaining at least one final chrominance component by scaling said at least one chrominance component by said first factor; and ?—encoding (13) said at least one final chrominance component.