Abstract: An image encoder includes a processor and a memory. The memory includes instructions configured to cause the processor to perform operations. In one example implementation, the operations may include determining whether a dictionary item is available for replacing a block of an image being encoded, the determining based on a hierarchical lookup mechanism, and encoding the image along with reference information of the dictionary item in response to determining that the dictionary item is available. In one more example implementation, the operations may include performing principal component analysis (PCA) on a block to generate a corresponding projected block, the block being associated with a group of images, comparing the projected block with a corresponding threshold, descending the block recursively based on the threshold until a condition is satisfied, and identifying a left over block as a cluster upon satisfying of the condition.

Abstract: The present subject matter discloses a system and method for detecting Large Vessel Occlusion (LVO) on a Computational Tomography Angiogram (CTA) automatically. the system comprises a vascular-territory-segmentation module, an ICV segmentation module, MCA-LVO classifier and ICA-LVO classifier. The vascular territory segmentation module is configured to receive a set of CTA images and to mark a territory of vascular segments in the ICV region for each slice of the ROI. The ICV segmentation module is configured to process each slice of the ROI. The processed slices of the ROI are combined to develop a CTA image after application of MIP and the developed CTA image is segmented into a Middle Cerebral Artery (MCA) region and an Internal Cerebral Artery (ICA) region. The MCA-LVO and ICA-LVO classifiers determine presence of the LVO on the received MCA and ICA region using Deep Learning techniques and accordingly the presence of the LVO is reported.

Abstract: The present disclosure relates to techniques for segmenting objects within medical images using a deep learning network that is localized with object detection based on a derived contrast mechanism. Particularly, aspects are directed to localizing an object of interest within a first medical image having a first characteristic, projecting a bounding box or segmentation mask of the object of interest onto a second medical image having a second characteristic to define a portion of the second medical image, and inputting the portion of the second medical image into a deep learning model that is constructed as a detector using a weighted loss function capable of segmenting the portion of the second medical image and generating a segmentation boundary around the object of interest. The segmentation boundary may be used to calculate a volume of the object of interest for determining a diagnosis and/or a prognosis of a subject.

Abstract: First and second predictors are used to identify regions of interest in a medical image. The first predictor is configured to maximize precision in identifying one or more abnormalities that it was trained to recognize, or at least to have greater precision than the second predictor. The second predictor is configured or selected to maximize sensitivity (i.e., recall) in identifying one or more abnormalities that it was trained to recognize, or at least to have greater sensitivity than the first predictor. The predictions of the first and second predictors may be used to create a digital map image showing the locations of regions of interest identified by the first and second predictors. Alternatively or in addition, the predictions may be used to rank images or image sets in terms of review priority.

Abstract: A device, apparatus, and method provide logic for processing information. In one implementation, a device may include an image acquisition Linn configured to acquire a image, and a transmission unit configured to transmit information associated with the image to an information processing apparatus, such as a server, The server may be associated with a first feature quantity dictionary. The device also may include a receiving unit configured to receive a second feature quantity dictionary from the server in response to the transmission. The second feature quantity dictionary may include less information than the first feature quantity dictionary, and the server may generate the second feature quantity dictionary based on the image information and the first feature quantity dictionary. The device may include an identification unit configured to identify an object within the image using the second feature quantity dictionary.

Abstract: Provided is an information processing device that enables a virtual object to be displayed at an appropriate position in the real world. The information processing device includes a position estimation unit that estimates a current position in a first coordinate system, a display position setting unit that sets a display position of a virtual object in a third coordinate system on the basis of an environment database, virtual object information including the display position of the virtual object in a second coordinate system, and an observed image captured near the current position, a meta information generation unit that generates observation meta information, and an environment database management unit that compares observation data with environment data of the environment database to determine whether to add the observation data to the environment database.

Abstract: The analysis method includes a step of measuring cytometry parameters for each biological cell contained in a biological sample; a step of determining, for each biological cell of the biological sample, a point in a N-dimensional space whose coordinates are defined depending on the cytometry parameters measured for the corresponding biological cell, where N is an integer greater than or equal to 3; a step of automatic clustering of the points into different cell clusters depending on the measured cytometry parameters, so as to define a sample cluster file; and a step of comparing the sample cluster files with reference cluster files, each of the reference cluster files being defined from cytometry parameters of a respective pathological or abnormal biological sample.

Abstract: A system and method for providing object recognition using artificial neural networks. The method includes capturing a plurality of reference images with a camera associated with an edge node on a communication network. The reference images are received by a centralized server on the communication network. The reference images are analyzed with a parent neural network of the centralized server to determine a subset of objects identified by the parent neural network in the reference images. One or more filters that are responsive to the subset of objects are selected from the parent neural network. A pruned neural network is created from only the one or more filters. The pruned neural network is deployed to the edge node. Real-time images are captured with the camera of the edge node and objects in the real-time images are identified with the pruned neural network.

Abstract: The present technique relates to an information processing apparatus, an information processing method, a program, and a mobile body which enable accuracy of detection processing related to a passage around a mobile body to be improved. The information processing apparatus includes: a transforming unit configured to respectively transform a plurality of photographed images with partially overlapping photographing ranges into bird's eye images; a compositing unit configured to generate a composite image by compositing a plurality of the bird's eye images; and a detecting unit configured to perform detection processing related to a passage around a mobile body based on the composite image. For example, the present technique can be applied to an apparatus that detects a lane marking of a road.

Abstract: A differentiation system for differentiating cells includes an input device configured to receive holographic image data of a microscopic particle in suspension, holographic image data processing logic for converting the holographic image data to the frequency domain by performing a Fourier transform of the holographic image data, and a recognizer configured to determine characterization features of the holographic image data of the microscopic particle in the frequency domain for characterization of the microscopic particle, the characterization features comprising rotationally invariant features.

Abstract: A data collection apparatus includes a processor and a storage. The processor is configured to: estimate an emotion of a person watching an own vehicle on the basis of an image data of a surrounding environment of the own vehicle; generate emotion data including an estimate of the emotion of the person watching the own vehicle; specify a state of the own vehicle viewed by the person watching the own vehicle; and correlate the emotion data with the specified state of the own vehicle. The storage is configured to be caused by the processor to store the emotion data correlated with the specified state of the own vehicle.

Abstract: A method includes obtaining, by a processor, a set of ultrasound frames showing a portion of a heart of a subject, identifying a subset of the frames, responsively to the subset having been acquired at one or more predefined phases of at least one physiological cycle of the subject, computing respective image-quality scores for at least the subset of the frames, each of the scores quantifying an image quality with which one or more anatomical portions of interest are shown in a respective one of the frames, and, based on the image-quality scores, selecting, for subsequent use, at least one frame from the subset of the frames. Other embodiments are also described.

Abstract: An example device is configured to capture an image of a patient. The devices includes a camera configured to capture the image of the patient. The device further: captures contextual data associated with the image; allows a caregiver to select the image to be stored; forwards the image and the contextual data to a remote server for processing using artificial intelligence; and receives a proposed diagnosis from the remote server based upon the image and the contextual data.

Abstract: An image of a sample collection device is captured. The image of the sample collection device is analyzed to determine whether the sample collection device includes a sufficient amount of fluid sample. The image of the sample collection device is validated based on a result of the image analysis.

Abstract: The present disclosure relates to systems and methods for positioning a region of interest (ROI). The systems may obtain an image of an object captured by an imaging device. The systems may extract image information of the image. The systems may obtain feature information of a region of interest (ROI) in the object. The systems may determine position information of the ROI in the image using a positioning model based on the image information of the image and the feature information of the ROI.

Abstract: An object is to improve character recognition accuracy of handwritten characters, originally a single continuous character string, described discontinuously. An image area corresponding to a handwritten character is separated from a document image obtained by scanning a document and a character block including characters having the same baseline is extracted. Then, in a case where a plurality of character blocks is extracted from the first image area, a single character block is generated by combining character blocks based on a position relationship of the plurality of character blocks.

Abstract: Systems and methods process images to determine a skin condition severity analysis and to visualize a skin analysis such as using a deep neural network (e.g. a convolutional neural network) where a problem was formulated as a regression task with integer-only labels. Auxiliary classification tasks (for example, comprising gender and ethnicity predictions) are introduced to improve performance. Scoring and other image processing techniques may be used (e.g. in assoc. with the model) to visualize results such as highlighting the analyzed image. It is demonstrated that the visualization of results, which highlight skin condition affected areas, can also provide perspicuous explanations for the model. A plurality (k) of data augmentations may be made to a source image to yield k augmented images for processing. Activation masks (e.g. heatmaps) produced from processing the k augmented images are used to define a final map to visualize the skin analysis.

Abstract: An inspection device having a plurality of functions is achieved. The performance of an inspection device is improved. A structure of an inspection device is simplified. A structure of an imaging device is simplified. The inspection device includes a light source having a function of emitting infrared light, a light source having a function of emitting visible light, and an imaging portion which are provided over a substrate having flexibility, and inspects a fruit or vegetable. A first image based on light including reflected light of the infrared light, and a second image and a third image based on tight including reflected light of the visible light are captured by the imaging portion. The inspection device has a function of detecting one or more of the sugar content, the acidity, and a physiological disorder of a fruit or vegetable on the basis of the first image.

Abstract: The present disclosure provides a system and method for medical imaging. The method may include obtaining a preliminary image and scanning data of a subject acquired using a scanner. The method may also include determining a regularization parameter for a regularization item of an objective function based at least in part on the scanning data, wherein the regularization parameter includes at least two of a first component characterizing quality of the scanning data, a second component characterizing the scanner, or a third component characterizing a feature of the subject. The method may further include generating an image of the subject by reconstructing the preliminary image based on the objective function.

Abstract: Systems and methods for determining a current state, of at least one traffic light, for use in controlling an autonomous vehicle are described herein. Implementations determine, based on a pose instance of the autonomous vehicle and a stored mapping of an environment of the autonomous vehicle, a region of the environment includes the traffic light and a configuration that is assigned to the traffic light. Further, those implementations process an image capturing the region, using a machine learning classifier, to generate predicted output associated with multiple candidate states of the traffic light, and determine a current state of the traffic light based on the predicted output. Processing the image using the machine learning classifier can be based on the configuration of the traffic light.