Abstract: Computer-implemented method, systems, computer program products include a processor(s) that obtains input data comprising an image and image data: coordinates (points) defining a region of interest in the image. The processor(s) determines a non-duplicative sequential order for navigating the coordinates. The processor(s) defines a set of points bordering the region of interest: points in the image and points comprising intersection points between two coordinates with an edge of a pixel in the image. The processor(s) determines a combined area of sub pixel subregions in each pixel that includes points in the set of points by: identifying the points of the set, generating one or more sub pixel subregions, and utilizing the sub pixel subregions to determine the combined area of sub pixel subregions.

Abstract: The present invention relates to a device (1) and a corresponding method for determining residual blood in a dialyzer (2) comprising a holding device (6) configured to accommodate a dialyzer (2), an image capturing device (7) configured to capture at least one image of a dialyzer (2) accommodated by the holding device (6), in particular a filter (4) located in the dialyzer (2), and generate corresponding image data having a plurality of image points, each of which assigned at least one respective intensity value, in particular a color value, and a control device (9) configured to determine residual blood information characterizing the presence and/or content of residual blood in the dialyzer (2) on the basis of a statistical frequency of at least a portion of the intensity values in the at least one image. The invention further relates to a dialysis system having such a device (1).

Abstract: Described herein are systems, apparatus, methods and computer program products configured for image detection/recognition of products. The disclosed systems and techniques utilize video data to provide the necessary number of images and view angles needed to train a machine learning product detection/recognition system to recognize a specific product within later provided images. In various embodiments, a user may provide video data and the video data may be transformed in a manner that may aid in training of the machine learning system.

Abstract: A method for enhancing digital imagery. The method comprises receiving a linear, intensity-based image of an environment. A histogram of intensity values is generated for a plurality of pixels within the linear, intensity-based image. Based on the histogram of intensity values, local contrast enhancement is applied to the linear, intensity-based image to generate a contrast enhanced version of the linear, intensity-based image, and artificial colorization is applied to the linear, intensity-based image to generate an artificially colorized version of the linear, intensity-based image. A composite image of the environment is then generated based on at least a portion of the contrast enhanced version of the linear, intensity-based image and at least a portion of the artificially colorized version of the linear, intensity-based image.

Abstract: Example implementations include a method, apparatus and computer-readable medium for controlling a camera, comprising receiving a video sequence of a scene. The method includes determining one or more scene description metadata in the scene from the video sequence. The method includes identifying one or more scene object types in the scene based on the one or more scene description metadata. The method includes determining one or more rules based on one or both of the scene description metadata or the scene object types, each rule configured to generate an event based on a detected object following a rule-specific pattern of behavior. The method includes applying the one or more rules to operation of the camera.

Abstract: One or more computing devices, systems, and/or methods for synthetic data point generation are provided. In an example, error bounds for generating synthetic data points from a set of data points collected from mobile devices may be determined. Paths associated with groups of data points of the set of data points may be determined. Error bounds may be applied to limit the paths to generate limited path data. Synthetic data points may be generated based upon the limited path data.

Abstract: Video object and keypoint location detection techniques are presented. The system includes a detection system for generation locations of an object's keypoints along with probabilities associated with the locations, and a stability system for stabilizing keypoint locations of the detected objects. In some aspects, the generated probabilities are two-dimensional array correspond locations within input images, and stability system fits the generated probabilities to a two-dimensional probability distribution function.

Abstract: An image processing apparatus includes a separation unit configured to separate luminance information of each pixel in a processing area of an input image into an intrusion component and an object component, a determination unit configured to determine whether each of intrusion components is a first component or a second component, a processing unit configured to generate first information based on the first component, and an image generating unit configured to generate an output image based on the first information, the second component, and the object component.

Abstract: Systems, devices, methods and instructions are described for detecting GAN generated images. On embodiment involves receiving an images, generating co-occurrence matrices on color channels of the image, generating analysis of the image by using a convolutional neural network trained to analyze image features of the images based on the generated co-occurrence matrices and determining whether the image is a GAN generated image based on the analysis.

Abstract: Disclosed is an image analysis method including inputting analysis data, including information regarding an analysis target cell to a deep learning algorithm having a neural network structure, and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.

Abstract: A method for removing external light interference according to the present disclosure includes: performing the scan in the state where external light is turned on, performing the scan in the state where the external light and internal light are turned on together, and calculating a brightness value from an obtained image. Further, by subtracting the brightness value obtained in the brightness calculating step from a color value of the image through an operation, it is possible to obtain an image and a three-dimensional scan model with the influence of the external light being minimized.

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: A target detection method is provided for a computing device. The method includes obtaining a current frame and a previous key frame corresponding to the current frame in a video frame sequence, determining a flow feature map and a flow field between the previous key frame and the current frame, obtaining, in response to determining the current frame is a non-key frame according to the flow feature map, a key frame feature corresponding to the previous key frame, and performing affine transformation on the key frame feature according to the flow field to obtain an image feature corresponding to the current frame, and performing target detection on the current frame according to the image feature to obtain a target detection result of the current frame.

Abstract: A method for determining soil clod parameters within a field includes receiving, with a computing system, three-dimensional image data depicting an imaged portion of the field. The three-dimensional image data, in turn, includes a first two-dimensional image depicting the imaged portion of the field relative to a first position and a second two-dimensional image depicting the imaged portion of the field relative to a second position, with the first position being spaced apart from the second position. Furthermore, the method includes identifying, with the computing system, a soil clod depicted with the received three-dimensional image data. Additionally, the method includes comparing, with the computing system, the first and second two-dimensional images to identify a shadow surrounding at least a portion of the identified soil clod. Moreover, the method includes determining, with the computing system, a soil clod parameter associated with the identified soil clod based on the identified shadow.

Abstract: Techniques for modeling the probability distribution of errors in perception systems are discussed herein. For example, techniques may include modeling error distribution for attributes such as position, size, pose, and velocity of objects detected in an environment, and training a mixture model to output specific error probability distributions based on input features such as object classification, distance to the object, and occlusion. The output of the trained model may be used to control the operation of a vehicle in an environment, generate simulations, perform collision probability analyses, and to mine log data to detect collision risks.

Abstract: Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.

Abstract: A method includes obtaining, by the treatment system configured to implement a machine learning (ML) algorithm, one or more images of a region of an agricultural environment near the treatment system, wherein the one or more images are captured from the region of a real-world where agricultural target objects are expected to be present, determining one or more parameters for use with the ML algorithm, wherein at least one of the one or more parameters is based on one or more ML models related to identification of an agricultural object, determining a real-world target in the one or more images using the ML algorithm, wherein the ML algorithm is at least partly implemented using the one or more processors of the treatment system, and applying a treatment to the target by selectively activating the treatment mechanism based on a result of the determining the target.

Abstract: A method for detecting the presence of on-body concealed objects includes receiving a visible-domain camera image for a scene, determining, using the visible-domain camera image, a region of interest where a subject is present, receiving an infrared-domain camera image and a millimeter-wave (mmwave) radar image that each cover the region of interest, determining emissivity information for the region of interest using the infrared-domain camera image, determining reflectivity information for the region of interest using the mmwave radar image and determining a concealed object classification for the subject based on the emissivity information and the reflectivity information. A corresponding system and computer program product for executing the above method are also disclosed herein.

Abstract: Methods and apparatus are disclosed to determine a consumer classification segment. A disclosed example method involves generating, with a processor, a list of consumer classification segments based on geographic indicators associated with a mobile device, associating, with the processor, the mobile device with a first consumer classification segment of the list of consumer classification segments if the geographic indicators were retrieved while a first application was executed, and associating, with the processor, the mobile device with a second consumer classification segment of the list of consumer classification segments if the geographic indicators were retrieved while a second application was executed.

Abstract: Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.