Abstract: In an approach for identifying a crop using satellite observation data and crop rotation data, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region from an optical satellite. A processor selects a plurality of points from the aerial image. A processor clips the aerial image into one or more smaller images to generate one or more observed signatures. A processor determines there is a change in a spatial distribution of one or more crops. A processor generates an actual signature from the one or more observed signatures. A processor cross-correlates the actual signature against a plurality of historical reference signatures to determine a degree of similarity. A processor ranks one or more results of the cross-correlation. A processor identifies the one or more crops. A processor calculates a first estimate of an amount of the one or more crops.

Abstract: An imaging system comprises an imaging platform, a camera operatively connected to the imaging platform, and a controller operatively connected to control the imaging platform and the camera. The controller includes machine readable instructions configured to cause the controller to perform a method.

Abstract: The disclosure provides an image clustering method and an image clustering apparatus. The method includes: obtaining new images, and clustering the new images to obtain a first cluster; determining a historical cluster similar to the first cluster as a second cluster from existing historical clusters; obtaining a distance between the first cluster and the second cluster; and generating a target cluster by fusing the first cluster and the second cluster based on the distance. In the image clustering method, with the image clustering apparatus of the disclosure, secondary clustering processing performed on the existing historical clusters based on newly added images is not required, new and old clusters are directly fused.

Abstract: An emotion determination device includes a memory and a processor connected to the memory. The processor is configured to acquire a facial image of a user, set an evaluation value for each of plural emotion classifications including a neutral state based on the acquired facial image, compute a correction value using an evaluation value set based on a neutral facial image acquired at a timing corresponding to a neutral state, and determine an emotion of the user by applying the computed correction value to the set evaluation values.

Abstract: An image data frame is received from an external source. An error concealment operation is performed on the received image data frame in response to determining that a first frame size of the received image data frame is erroneous. The first frame size of the image data frame is determined to be erroneous based on at least one frame synchronization signal associated with the image data frame. An image processing operation is performed on the received image data frame on which the error concealment operation has been performed, thereby enabling an image processing module to perform the image processing operation without entering into a deadlock state and thereby prevent a host processor from having to execute hardware resets of deadlocked modules.

Abstract: An apparatus for generating a map identifies a lane line and a feature other than a lane line on a road from a first image of a predetermined location of the road taken downward from the sky; identifies the lane line and the feature from a second image representing the predetermined location of the road and made based on images taken by a camera provided for a vehicle; aligning the first images with the second images, based on the predetermined location; deforms the second image so that the feature in the second image best fits the feature in the first image; further deforms the second image in a direction perpendicular to the front-back direction of the road so that the position of the feature remains unchanged and that the positions of the lane lines in the first and second images match; and combines the first and deformed second images.

Abstract: A method and device for behavior recognition are provided. According the present disclosure, an image is collected. It is detected whether the image includes a face. When the image includes a face, it is determined whether the face in the image is a face with closed eyes. When the face in the image is not with closed eyes, a distance between the face and an image collecting unit is computed. A maximum field of vision of a target to which the face belongs is determined according to the distance and a parameter of the image collecting unit. It is determined, according to the maximum field of vision, whether the target has a set behavior.

Abstract: An electronic data processor is configured to identify the component pixels of a harvestable plant component within the obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels. A user interface is configured to provide an aggregate, sectional yield, or per row yield based on a detected size of the harvestable plant component for the one or more target plants as an indicator of yield of the one or more plants or standing crop in the field.

Abstract: An image processing method, an image processing device, an electronic device, and a storage medium are provided. The image processing method includes: obtaining an input image, wherein the input image includes M character rows; performing global correction processing on the input image to obtain an intermediate corrected image; determining the M character row lower boundaries; determining the relative offset of all pixels in the intermediate corrected image according to the M character row lower boundaries, the first image boundary and the second image boundary of the intermediate corrected image; determining the local adjustment offset of all pixels in the intermediate corrected image according to the relative offsets of all pixels in the intermediate corrected image; and performing local adjustment on the intermediate corrected image according to the local adjustment offsets of all pixels in the intermediate corrected image to obtain the target corrected image.

Abstract: A system in which electrical characteristics of an element formed on a sample can be evaluated. The system includes an image acquisition tool and a computer system that includes one or more processors and is configured to be communicable with the image acquisition tool. Electrical characteristic are derived by the image acquisition tool by receiving information regarding two or more characteristics of a specific pattern that is included in a plurality of images acquired from the image acquisition tool under at least two different image acquisition conditions and by referring to, for the information, relation information between information regarding two or more characteristics and electrical characteristics of an element formed on a sample, the characteristics being extracted from at least two pieces of image data acquired from the image acquisition tool under at least two image acquisition conditions.

Abstract: Methods and systems for generating an image quality metric are described. A reference and a test image are first converted to the ITP color space. After calculating difference images ?I, ?T, and ?P, using the color channels of the two images, the difference images are convolved with low pass filters, one for the I channel and one for the chroma channels (I or P). The image quality metric is computed as a function of the sum of squares of filtered ?I, ?T, and ?P values. The chroma low-pass filter is designed to maximize matching the image quality metric with subjective results.

Abstract: Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle, identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs, determining a function for assigning an importance score to each of the plurality of unlabeled sensor data logs, using the one or more trends, using the function for assigning the importance score to each of the plurality of unlabeled sensor data logs, selecting a subset of the plurality of sensor data logs that have an importance score greater than a threshold, and using the subset of the plurality of sensor data logs for further training the machine learning model trained using the training dataset to generate an updated model.

Abstract: An analyzing apparatus extracts, from a food image, food information relating to types and served states of foods included in the food image and an edible portion of each food, calculates an area ratio between the edible portion of the same kind of food extracted from a plurality of food images acquired at different timings, stores conversion information for converting the area ratio into a volume ratio corresponding to each type and served state of food, and converts the area ratio of each food into a volume ratio using the stored conversion information corresponding to the food whose area ratio is to be converted from among the stored conversion information based on the food information.

Abstract: A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.

Abstract: A reconstructed volume of a region of patient anatomy is processed to reduce motion artifacts in the reconstructed volume. Autosegmentation of high-contrast structures present in an initial reconstructed volume is performed to generate a 3D representation of the high-contrast structures. 2D mask projections are generated by performing forward projection on the 3D representation, where each 2D mask projection includes location information indicating pixels that correspond to the high-contrast structures during the forward projection process. The acquired 2D projections are modified via in-painting to generate corrected 2D projections, where the acquired 2D projections are modified using information from the 2D mask projections. For example, pixels in the acquired 2D projections that are associated with high-contrast moving structures are replaced with low-contrast pixels.

Abstract: A vehicle exterior environment recognition apparatus includes a three-dimensional object region identification unit, a bicycle determination unit, and a bicycle identification unit. The three-dimensional object region identification unit identifies a three-dimensional object region including a three-dimensional object, in an image. The bicycle determination unit divides the three-dimensional object region into a plurality of divisions, to determine bicycle-likeliness of the three-dimensional object on the basis of a state of occupation of each of the plurality of the divisions by the three-dimensional object. The bicycle identification unit identifies the three-dimensional object as a bicycle on the basis of the bicycle-likeliness.

Abstract: A system including a scene detection engine (SDE) and a method for automatically detecting and marking logical scenes in media content using artificial intelligence are provided. The SDE detects boundaries of shots in media content. The SDE generates an image similarity matrix by extracting color features from a middle frame of each shot. The SDE generates an audio similarity matrix by extracting audio features of each shot. The SDE generates a resultant similarity matrix from the image and audio similarity matrices. The SDE executes a clustering algorithm using the resultant similarity matrix with reduced or no noise to generate an ordered sequence of shots that define a boundary of each scene of the media content. The SDE executes a linear traversal algorithm on the shots that define the boundary of each scene for correcting errors in the defined boundary of each scene.

Abstract: The disclosed technology provides solutions for detecting and localizing road signage, such as speed limit signs. A process of the disclosed technology can include steps for receiving a first set of sensor data comprising a signage feature, wherein the first set of sensor data is associated with a first location, receiving a second set of sensor data comprising the signage feature, wherein the second set of sensor data is associated with a second location, and determining a location of the signage feature based on the first location and the second location. Systems and machine-readable media are also provided.

Abstract: A method and system are provided. The method includes determining a difference map between a reference frame and a non-reference frame, determining a local variance of the reference frame, determining a detail power map based on a difference between the determined local variance and the determined difference map, and determining a detail grade map based on the determined detail power map.

Abstract: Systems and methods are provided for. One embodiment is a system that includes an interface configured to receive an image of media printed on with print data, and memory configured to store defect reference data of nozzles belonging to printheads of a printer. The system also includes a print defect controller configured to detect a nozzle defect in the image based on a comparison of the image with the print data, and to determine a type of the nozzle defect based on a comparison of the nozzle defect with the defect reference data.