Abstract: A method for performing a diagnostic test comprises generating a first visual trigger on a display screen of a mobile device responsive to activation of an application on the mobile device. An image of a diagnostic test presently viewed by a camera incorporated with the mobile device is displayed on the display screen of the mobile device. The diagnostic test has a second visual trigger thereon. The image and the first visual trigger are displayed on the display screen at a same time. An image is captured by the application of the diagnostic test responsive to alignment of the first visual trigger with the second visual trigger on the display screen of the mobile device. The captured image is processed to provide test results for the diagnostic test.

Abstract: A method and a system for establishing a light source information prediction model are provided. A plurality of training images are captured for a target object. A white object is attached on the target object. True light source information of the training images is obtained according to a color of the white object in each of the training images. A neural network model is trained according to the training images and the true light source information, and a plurality of pieces of predicted light source information is generated according to the neural network model during the training. A learning rate for training the neural network model is adaptively adjusted based on the predicted light source information.

Abstract: A number of embodiments can include a photographic chamber. In some embodiments, a photographic chamber can comprise one or more frames arranged in a convex shape and having an opening; one or more walls coupled to the one or more frames and having one or more recesses configured to receive photography equipment; and a door matching the one or more walls, wherein the door can be located in the opening when positioned in a closed configuration. Other embodiments are disclosed herein.

Abstract: A 6DoF video data processing method performed by a 6DoF video transmission device according to the present invention comprises the steps of: acquiring information on pictures of a 6DoF video and metadata of the 6DoF video; decoding the pictures on the basis of the metadata and the information of the pictures, and deriving an image of a user position; and processing the image on the basis of the metadata, and performing rendering into a 3D space around the user position, wherein the metadata includes stitching information and information of a camera having captured the 6DoF video.

Abstract: A method of mapping red-green-blue-green (RGBG) format data to red-green-blue (RGB) format data, the method including receiving three RGBG pixel values for mapping to four RGB pixel values, the RGBG and RGB pixel values including red color components, green color components, and blue color components, mapping the red and blue color components and first three green color components of the RGBG pixel values to first three RGB pixel values, and mapping last three green color components of the RGBG pixel values to the red, green, and blue color components of a fourth RGB pixel value according to a mapping pattern.

Abstract: Decompressing a compressed image to obtain a decompressed image includes receiving, in a compressed stream, compressed pixel values of the compressed image; decompressing, from the compressed stream, a first compressed pixel value of the compressed pixel values using a lossy floating-point decompression scheme to obtain a floating-point pixel value; rounding the floating-point pixel value to a nearest integer to obtain a pixel value of the decompressed image; and displaying or storing the decompressed image.

Abstract: Systems, methods, and software described herein to manage preferences for video search. In one implementation, a video processing service may receive a request for video data that satisfies one or more criteria, wherein the one or more criteria includes at least one color for an object. In response to the request, the video processing service may identify one or more secondary colors associated with the at least one color and update the one or more criteria to include the one or more secondary colors. The video processing service may then select the video data from a video source that satisfies the updated one or more criteria.

Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Abstract: In various embodiments, a computer-implemented method of training a neural network for creating an output signal of different modality from an input signal is described. In embodiments, the first modality may be a sound signal or a visual image and where the output signal would be a visual image or a sound signal, respectively. In embodiments a model is trained using a first pair of visual and audio networks to train a set of codebooks using known visual signals and the audio signals and using a second pair of visual and audio networks to further train the set of codebooks using the augmented visual signals and the augmented audio signals. Further, the first and the second visual networks are equally weighted and where the first and the second audio networks are equally weighted. In aspects of the present disclosure, the set of codebooks comprise a visual codebook, an audio codebook and a correlation codebook.

Abstract: Systems and methods are disclosed for generating a 3D view of an object. At least a 360 degree view of an object is recorded by rotating the object or moving a camera around an object. The data can be used to generate a 3D view that allows users to rotate an item to see the corresponding images.

Abstract: Machine-based risk prediction or assistance is provided for peri-procedural complication, such as peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in PCI. Various combinations of types or modes of information are used in the prediction, such as both imaging and non-imaging data. The prediction may be made prior to, during, and/or after PCI using the machine-learned model to more quickly reduce the chance of PMI. The workflows for prior, during, and/or post PCI incorporate the risk prediction and/or risk-based recommendations to reduce PMI for patients.

Abstract: According to an embodiment, an image coding method is for coding an image including a luminance component and color difference components. The method includes acquiring a reference image; and generating a predicted image by interpolating the luminance component and the color difference components in the reference image according to a motion vector. If a size of a block, which is designated as a unit of the interpolation, is equal to or smaller than a predetermined first threshold value, the generating includes inhibiting a bi-directional prediction, and performing only a uni-directional prediction to generate the predicted image according to the motion vector.

Abstract: A method and compression unit for compressing a block of image data to satisfy a target level of compression, wherein the block of image data comprises a plurality of image element values, each image element value comprising one or more data values relating to a respective channel. For each of the channels: (i) an origin value for the channel for the block is determined, (ii) difference values are determined representing differences between the data values and the determined origin value for the channel for the block, and (iii) a first number of bits for losslessly representing a maximum difference value of the difference values for the channel for the block is determined.

Abstract: A decompression method determines image element values from compressed data representing a block of image element values relating to a respective one or more channels. For each of the channels, an indication of a first number of bits representing difference values between the data values and an origin value for the channel is read from the compressed data. For each of the channels, a second number of bits is obtained, wherein representations of the difference values for each of the channels are included in the compressed data using the second number of bits for that channel. The obtained second numbers of bits for the respective channels are used to read the representations of the difference values for the image element values being decompressed. Based on the representations of the difference values, a difference value is determined in accordance with the first number of bits for the channel.

Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps.

Abstract: This disclosure relates to advanced image signal processing technology including encoded signals and digital watermarking. One implementation is directed to a printed object comprising: a white substrate or background comprising a first area; an ink mixture printed at a first plurality of spatial locations within the first area, the ink mixture printed such that the first area comprises a second plurality of spatial locations without the ink mixture, the ink mixture comprising extender white and Green 7 ink, the ink mixture comprising a volume or weight ratio of 97.5% to 99.75% white extender and 2.5%-0.25% Green 7 ink; in which the first plurality of spatial locations is arranged in a pattern conveying an encoded signal, and in which the white substrate or background and the ink mixture comprise a spectral reflectivity difference at or around 660 nm in a difference range of 8%-30%. Of course, other implementations, methods, packages, systems and apparatus are described in this patent document.

Abstract: A traffic light image processing method and device, a computer system, a roadside device, and a storage medium are provided. The method includes: acquiring image data of a traffic light from a roadside sensor, the image data of the traffic light including a sequence of image frames over time, and the traffic light including at least two light heads; performing difference processing on two temporally adjacent image frames to obtain a difference image, a pixel value of each pixel point of the difference image being equal to a difference value of corresponding pixel points of the two temporally adjacent image frames; performing thresholding processing on the difference image to obtain a thresholding processed difference image; and determining a switched-on light head in the traffic light based on the thresholding processed difference image.

Abstract: Methods and decompression units for decompressing data from a compressed block of image data, the compressed block of image data representing a block of image data comprising a plurality of image element values, the image element values being divisible into at least a first value and a second value such that the block of image data comprises a two-dimensional block of first values.

Abstract: The image coding method includes: determining a context in a current block in the image, from among a plurality of contexts; and performing arithmetic coding on the control parameter for the current block to generate a bitstream corresponding to the current block, wherein in the determining: the context is determined under a condition that control parameters of neighboring blocks of the current block are used, when the signal type is a first type, the neighboring blocks being a left block and an upper block of the current block; and the context is determined under a condition that the control parameter of the upper block is not used, when the signal type is a second type, and the second type is (i) “merge_flag”, (ii) “ref_idx_l0” or “ref_idx_l1”, (iii) “inter_pred_flag”, (iv) “mvd_l0” or “mvd_l1”, (v) “no_residual_data_flag”, (vi) “intra_chroma_pred_mode”, (vii) “cbf_luma”, and (viii) “cbf_cb” or “cbf_cr”.

Abstract: A computing system that can receive an object search request from a user indicating a request to search for a specific object in an area traversed by one or more autonomous vehicle. The object search request can include a set of physical characteristics of the specific object. The computing system can then transmit a signal to an autonomous vehicle indicating a request for the autonomous vehicle to search for the specific object. The signal can cause the autonomous vehicle to transmit an image, selected based on a physical characteristic of the object, to the computing system. The computing system can then generate a score indicative of a difference between one or more physical characteristic of the object in the image and the specific object. The computing system can then selectively transmit the image to a mobile device operated by the user based on the score.