Abstract: A detection and positioning method for a train water injection port includes: acquiring train water injection port video images, and performing threshold segmentation on the train water injection port video images to obtain binarized train water injection port video images; processing the binarized train water injection port video images and matching the processed binarized train water injection port video images with a train water injection port template image; detecting a position of the water injection port in the train water injection port video image and comparing the position with a pre-set position range where the water injection port is located; and if the position and the pre-set position range have been matched, then transmitting a matching valid signal to a mechanical device control module to control a mechanical device to move or stop and start or stop water injection.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: An imaging data set (22) comprising detected counts along lines of response (LORs) is reconstructed (24) to generate a full-volume image at a standard resolution. A region selection graphical user interface (GUI) (26) is provided via which a user-chosen region of interest (ROI) is defined in the full-volume image, and this is automatically adjusted by identifying an anatomical feature corresponding to the user-chosen ROI and adjusting the user-chosen ROI to improve alignment with that feature. A sub-set (32) of the counts of the imaging data set is selected (30) for reconstructing the ROI, and only the selected sub-set is reconstructed (34) to generate a ROI image (36) representing the ROI at a higher resolution than the standard resolution. A fraction of the sub-set of counts may be reconstructed using different reconstruction algorithms (40) to generate corresponding sample ROI images, and a reconstruction algorithm selection graphical user interface (42) employs these sample ROI images.

Abstract: A method of generating an elemental map includes: acquiring a plurality of correction channel images by scanning a surface of a standard specimen having a uniform elemental concentration with a primary beam and generating a correction channel image for each channel; generating correction information for each pixel of each correction channel image among the plurality of correction channel images based on a brightness value of the pixel; acquiring a plurality of analysis channel images by scanning a surface of a specimen to be analyzed with the primary beam and generating an analysis channel image for each channel; correcting brightness values of pixels constituting an analysis channel image among the plurality of analysis channel images based on the correction information; and generating an elemental map of the specimen to be analyzed based on the plurality of analysis channel images having pixels with corrected brightness values.

Abstract: The present disclosure relates to an apparatus and a method for information processing enabling alignment between imaged images to be more accurately performed. With respect to a detection result of a correspondence point between an imaged image including a pattern irradiated for alignment with other imaged image, and the other imaged image including the pattern, an evaluation value with which an occurrence rate of an error in the alignment when the imaged image and the other imaged image are composed with each other is evaluated is calculated; and an irradiated position of the pattern is updated on the basis of the evaluation value calculated. The present disclosure, for example, can be applied to an information processing apparatus, an irradiation device, an imaging apparatus, an irradiation imaging apparatus, a controller, an imaging system or the like.

Abstract: A type of glasses worn on a human face in a to-be-processed picture is detected, and a lens area of the glasses is determined; and a deformation model of the lens area is determined based on the type of the glasses, where the deformation model is used to indicate deformation caused by the glasses on an image in the lens area, and then the image in the lens area is restored based on the deformation model.

Abstract: The present disclosure provides an unsupervised exception access detection method and apparatus based on one-hot encoding mechanism. The method includes: encoding each test URL sample by using one-hot encoding mechanism, to obtain a high-dimensional vector; inputting the high-dimensional vector into a pre-built deep autoencoder network for compression and dimension reduction processing, to obtain a two-dimensional vector; performing visualization operation on the two-dimensional vectors by using a two-dimensional coordinate system, to obtain visualized test URL samples; performing a cluster analysis on all visualized test URL samples by using a K-means algorithm, to divide the test URL sample set into a first type and a second type of URL sets; comparing sample sizes of the first type and second type of URL sets, determining the URL set with a larger sample size as a normal URL set, and determining the URL set with a smaller sample size as an abnormal URL set.

Abstract: A panorama image alignment method method comprises: obtaining multipath spherical images; calculating rotation Euler angles between each spherical image and a middle portion, a left portion and a right portion of an adjacent spherical image according to a middle portion, a left portion and a right portion of each spherical image to obtain a first left portion rotation matrix and a second right portion rotation matrix; obtaining a first left panorama image, a first right panorama image, a second left panorama image and a second right panorama image; aligning the second left panorama image to the first left panorama image, obtaining a second left portion rotation matrix by means of calculation, and then obtaining a rotation matrix of a left panorama; aligning the second right panorama image to the first right panorama image, obtaining a second right portion rotation matrix by means of calculation, and obtaining a rotation matrix of a right panorama.

Abstract: An occupant state recognition apparatus including an eyelid opening recognition unit configured to recognize an eyelid opening of a driver and maximum and minimum values of the eyelid opening; an eye state determination unit configured to determine that the eye is in an eye open state if the eyelid opening becomes greater than or equal to a preset threshold value, and to determine that the eye is in an eye closed state if the eyelid opening becomes less than the threshold value; and a threshold value resetting unit configured to reset the threshold value to a value between the maximum value and the minimum value of the eyelid opening, if the maximum value (or the minimum value) has not become greater (or less) than or equal to the threshold value for a predetermined period or a period corresponding to a predetermined number of times of eyelid opening and closing.

Abstract: A surgical navigation system is provided. The surgical navigation system comprises a camera system comprising at least one camera. The camera system is capable of capturing images of light that is filtered through at least three different polarization angles. The surgical navigation system further comprises a processor having access to three dimensional image data of a surgical object. The processor is configured to determine first surface normals from the three dimensional image data of the surgical object. The processor is further configured to determine second surface normals from at least three images captured by the camera system of the surgical object under different polarization angles. The processor is configured to align the first surface normals with the second surface normals. Furthermore, a method for operating a surgical navigation system is provided.

Abstract: Embodiments of the present application provide an image fusion apparatus and an image fusion method. The image fusion apparatus includes: a light acquisition device, an image processor, and an image sensor having four types of photosensitive channels. The four types of photosensitive channels include red, green and blue RGB channels and an infrared IR channel. The light acquisition device is configured to filter a spectrum component of a first predetermined wavelength range from incident light to obtain target light. The first predetermined wavelength range is a spectrum wavelength range in which a difference between the responsivities of the RGB channels and the responsivity of the IR channel in the image sensor in the infrared band is higher than a first predetermined threshold. The image sensor is configured to convert the target light into an image signal through the RGB channels and the IR channel.

Abstract: A method for learning a recurrent neural network to check an autonomous driving safety to be used for switching a driving mode of an autonomous vehicle is provided. The method includes steps of: a learning device (a) if training images corresponding to a front and a rear cameras of the autonomous vehicle are acquired, inputting each pair of the training images into corresponding CNNs, to concatenate the training images and generate feature maps for training, (b) inputting the feature maps for training into long short-term memory models corresponding to sequences of a forward RNN, and into those corresponding to the sequences of a backward RNN, to generate updated feature maps for training and inputting feature vectors for training into an attention layer, to generate an autonomous-driving mode value for training, and (c) allowing a loss layer to calculate losses and to learn the long short-term memory models.

Abstract: An index outputter (120) of a learning processor (100) outputs an index for selection of an analysis procedure based on attributes of an analysis target. The attributes to be inputted include features of the analysis target itself and features of the environment surrounding the analysis target. The analysis procedure selector (170) selects an analysis procedure among analysis procedures as an analysis procedure for which diagnosis target data to be outputted by the analysis target is to be analyzed. The selected analysis procedure is a procedure in which an evaluation value of a property of data analysis satisfies a preset condition in connection with the index.

Abstract: In an illustrative embodiment, methods and systems for automatically assessing features of a property location may include applying first machine learning analysis to at least one image to determine a set of characteristics of the property features, and applying second machine learning analysis to the at least one image to classify a condition of each property feature.

Abstract: A portable electronic device may include an image signal processor that includes a clipping circuit, a pyramid generator circuit, and an image fusion processor. The clipping circuit clips pixel values that are under-exposed or over-exposed. The pyramid generator circuit applies a filter to the pixels of the image to generate a filtered image. Some of the filtered pixels may be generated from one or more clipped pixel values. The pyramid generator circuit identifies those filtered pixels that are generated from one or more clipped pixel values and marks the identified filtered pixels with a tag. The pyramid generator circuit decimates the filtered image to generate a downscaled image, which may include one or more filtered pixels that are marked with the tags. The image fusion processor fuses the downscaled image with another image. The pixels that are marked with the tags may be disregarded in the fusion process.

Abstract: The present document describes methods and systems for the automatic inspection of material quality. A set of lights with a geometric pattern is cast on a material to be analyzed. Depending on the material being inspected, same may act as a mirror and the reflected image is captured by a capture device, or the light passes through the material being inspected and the image is captured by a capture device. Defects in the material can be detected by the distortion caused by same in the pattern of the reflected image or passing through. Finally, software is used to identify and locate these distortions, and consequently the defects in the material. This classification of defects is carried out using artificial intelligence techniques.

Abstract: A computing system automatically detects, within a digital video frame, a video frame region that depicts a textual expression of a scoreboard. The computing system (a) engages in an edge-detection process to detect edges of at least scoreboard image elements depicted by the digital video frame, with at least some of these edges being of the textual expression and defining alphanumeric shapes; (b) applies pattern-recognition to identify the alphanumeric shapes; (c) establishes a plurality of minimum bounding rectangles each bounding a respective one of the identified alphanumeric shapes; (d) establishes, based on at least two of the minimum bounding rectangles, a composite shape that encompasses the identified alphanumeric shapes that were bounded by the at least two minimum bounding rectangles; and (e) based on the composite shape occupying a particular region, deems the particular region to be the video frame region that depicts the textual expression.

Abstract: Disclosed is system comprising data processing arrangement including processors configured to receive sentences from unlabeled training data set; tokenize, using tokenizer module, sentences to obtain tokens; generate character level features for character of tokens of sentences; generate token level feature for each token of the sentences, wherein token level feature of token in sentence is identified using token coordinates of token and token coordinates of tokens neighboring token in sentence; train artificial neural network adapted to identify entities in sentences to determine first trend set, wherein training is based on received sentences, character level features for each character of tokens of sentences and token level feature for tokens of sentences; train the artificial neural network on set of labelled data to determine second trend set; identify, using identifier module, entity in text content, wherein identifier module uses first trend set and second trend set determined by artificial neural netw

Abstract: An optimized method and system to compute the dose to a patient (2) given a captured integrated exit-transit image (5) of the radiation rays (4) traveling from the source of x-rays (1) through the patient (2) to the imaging device (3) to product the exit-transit image (5). Each radiation field image (5) is transformed (6,8,10,12) to multiple images (7,9,11,13) for each phantom thickness (26) that was measured with the imaging device (3) for a range of field sizes (21). Given the water equivalent path (22) through the patient for a ray (4) reaching a pixel (15, 16), the final pixel value (19) is interpolated from the images (9, 11) that bracket the water equivalent path through the patient (22). Adaptive feedback calibration measures are carried out effectively ameliorate the detrimental effects of off axis attenuation of pixel values.

Abstract: An obstacle avoidance reminding method includes: performing ground detection based on acquired image data to acquire ground information of a road; performing passability detection based on the acquired ground information, and determining a traffic state of the road; if it is determined that the road is impassable, performing road condition detection for the road to acquire a first detection result, and performing obstacle detection for the road to acquire a second detection result; and determining obstacle avoidance reminding information based on the first detection result and the second detection result.