Abstract: The present solution can segment tracts by performing two-pass tractography. The system can first perform deterministic tractography and then probabilistic tractography. The system can use the result from the deterministic tractography to update and refine initial identified regions of interest. The refined regions of interest can be used to filter and select streamlines identified through the probabilistic tractography.

Abstract: Aspects of the present disclosure provide for a system. In some examples, the system includes a computing device configured to capture an image depicting dental characteristics of an animal and a server. The server is configured to receive the image from the computing device, determine a plurality of teeth depicted in the image, determine at least one feature associated with at least one of the plurality of teeth, determine an estimate age of the animal based at least partially on the determined plurality of teeth and the at least one feature, and provide the estimated age of the animal.

Abstract: There is disclosed a system, apparatus and methods for optimizing photo selection. When a photographer takes photos as requested in a shot list, the photos are automatically assigned a quality score which correlates to how prominently the photo would be displayed in an online search. The photos and the quality scores are displayed to the photographer so that when the photographer has shot a sufficiently high quality photo then the photographer can stop shooting. Photos with the highest quality scores are optimal. The shot lists include reference photos, and if a new photo has a higher quality score than the corresponding reference photo, the new photo becomes the reference photo.

Abstract: Aerial imagery may be captured from an unmanned aircraft or other aerial vehicle. The use of aerial vehicle imagery provides greater control over distance from target and time of image capture, and reduces or eliminates imagery interference caused by clouds or other obstacles. Images captured by the aerial vehicle may be analyzed to provide various agricultural information, such as vegetative health, plant counts, population counts, plant presence estimation, weed presence, disease presence, chemical damage, wind damage, standing water presence, nutrient deficiency, or other agricultural or non-agricultural information. Georeference-based mosaicking may be used to process and combine raw image files into a direct georeferenced mosaic image.

Abstract: An IoT based apparatus to assess the quality of food or agricultural produce is disclosed. The apparatus comprises a frame having an enclosure, a movable tray disposed at a middle portion of the enclosure, one or more camera installed within the enclosure, one or more light source strategically mounted within the enclosure to avoid reflection and hot spots, at least one storage unit, a touch screen display and a single board computer coupled to the camera, light source, storage unit and touch screen display. The enclosure is illuminated by the light source and the camera is configured to capture the image of the produce from both the top and bottom side of the produce. A pre-trained deep learning model is used on both the top and bottom view images to identify defects in the agricultural produce. The identified defects are displayed to the user via the touch screen display.

Abstract: There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.

Abstract: P feature encoding values are obtained for each of the Q frames in a video clip by image transformations of each frame along with performing computations of a specific succession of convolution and pooling layers of a CNN based deep learning model followed with operations of a nested invariance pooling layer. Each feature encoding value is then converted from real number to a corresponding integer value within a range designated for color display intensity according to a quantization scheme. A 2-D graphical symbol that contains N×N pixels is formed by placing respective color display intensities into the N×N pixels according to a data arrangement pattern for representing all frames of the video clip in form of P×Q feature encoding values, such that the 2-D graphical symbol possesses a semantic meaning of the video clip that can be recognized via image classification task using another trained CNN based deep learning model.

Abstract: Human biological change information in data form is obtained by one or more computer processing devices that utilize human biological change data from event data stream(s) and allows for ranking one or more changes in the biological change data. The data then correlates biological change data with ranking statistics from an event such that the correlation identifies and measures a magnitude of change of human biological responses from data acquired from event data stream(s). The correlation of human biological change data with ranking statistics creates one or more objects for storage and analysis within in one or more databases, wherein the objects possess time synchronized temporal human biological responses before, during or after an event. These objects also possess biological change data that utilizes correlation of biological change data with ranking statistics and results in objects that also contain one or more ranked values of the human biological responses.

Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.

Abstract: An image analysis apparatus including a processor configured to: acquire a first fluorescence image indicating an observation target including a plurality of types of cells, each of which has a first region stained by first staining, and a second fluorescence image indicating the observation target in which a second region of a specific cell among the plurality of types of cells is stained by second staining different from the first staining; determine whether or not the first region is included in the second region in a superimposed image obtained by superimposing the first fluorescence image and the second fluorescence image and acquire a first determination result for each of the first regions; and determine the type of cell included in the observation target on the basis of the first determination result.

Abstract: A method is used to generate a report presenting parameter values corresponding to a structured illumination microscopy (SIM) optical system. The parameter values are based at least in part on the performed modulation calculation corresponding to an image set captured with the SIM optical system. A minimum FWHM slice is identified, based at least in part on an average FWHM value across the images in the first image set. Parameter estimation is performed on the identified minimum FWHM slice. Best in-focus parameters are identified based at least in part on the performed estimation. A phase estimate is performed for each image in the set. A modulation calculation is performed based at least in part on the identified best in-focus parameters. The report is based at least in part on the performed modulation calculation.

Abstract: Embodiments discussed herein facilitate predicting response to therapy in Crohn's disease. A first set of embodiments discussed herein relates to accessing a radiological image of a region of tissue demonstrating Crohn's disease associated with a patient; defining a mesenteric fat region by segmenting mesenteric fat represented in the radiological image; extracting a set of radiomic features from the mesenteric fat region; providing the set of radiomic features to a machine learning classifier configured to compute a probability of response to therapy in Crohn's disease based, at least in part, on the set of radiomic features; receiving, from the machine learning classifier, a probability that the region of tissue will respond to therapy; generating a classification of the patient as a responder or non-responder based, at least in part, on the probability; and displaying the classification.

Abstract: The method for determining a geographic identifier including: determining a location description; determining parcel data; determining a georeferenced image based on the location description; generating a set of image features using the georeferenced image; optionally determining a built structure class; identifying a location of interest within the georeferenced image based on the set of features; determining a geographic identifier associated with the location of interest based on the image georeference; associating the geographic identifier with the location description; optionally returning the geographic identifier in response to the location description comprising an address; and optionally returning an address in response to the location description comprising a geographic coordinates.

Abstract: Various embodiments are directed to performing identity verification using biometrics and open data, such as publicly available data on the Internet. A person may provide various types of information about the person, including a name and an image of the person. An Internet search may be performed on the provided name and one or more publicly-available images corresponding to that name may be acquired. Biometric analyses may be performed on both the image provided by the person and the acquired one or more images to determine whether any of them match the person. Metadata may be extracted from a matching image. Moreover, data relating to the person may be acquired from the source of the matched image. The metadata and the data from the source may be compared with the information provided by the person to validate the identity of the person.

Abstract: Methods, apparatus, systems, and computer-readable media are provided that relate to using one or more vision sensors to capture images of a loaded pallet in association with application of stretch wrap to the loaded pallet by an automated pallet wrapping machine. The images are used to generate an image-based identifier for the loaded pallet that is then used for pallet identification by mobile robots and/or other automated agents in a warehouse or other environment. In some implementations, the images are captured by the vision sensor when the pallet is in the wrapping area of the automated pallet wrapping machine and while the vision sensor and/or the pallet are rotating. In some implementations, the image-based identifier may be assigned to pallet attributes and/or a de-palletizing scheme of the loaded pallet.

Abstract: The exemplary embodiments of the present disclosure are described and illustrated below to encompass methods and devices for designing patient specific prosthetic cutting jigs and, more specifically, to devices and methods for segmenting bone of the knee and the resulting cutting guides themselves. Moreover, the present disclosure relates to systems and methods for manufacturing customized surgical devices, more specifically, the present disclosure relates to automated systems and methods of arthroplasty cutting guides, systems and methods for image segmentation in generating computer models of knee joint.

Abstract: The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.

Abstract: The present technology relates to a medical image processing apparatus, a medical image processing method, and a program that permit superimposition of images at an appropriate mixing ratio without deletion of detailed information. An acquisition section, a superimposition ratio calculation section, and a superimposition processing section are included. The acquisition section acquires a normal frame captured with normal light irradiated on a subject and a special frame captured with special light irradiated on the subject. The superimposition ratio calculation section calculates, on the basis of an intensity value of the special frame, a superimposition ratio indicating a ratio at which the normal frame and the special frame are superimposed. The superimposition processing section performs a superimposition process of superimposing the normal frame and the special frame on the basis of the superimposition ratio. The present technology is applicable to an image processing apparatus for medical use.

Abstract: A plurality of captured images is acquired while changing a light illumination state. Each captured image is compared with a corresponding reference image to acquire a region where the captured image is darker than the reference image as a dark defect candidate region. From each of a plurality of captured images, a region where the captured image is lighter than the reference image is acquired as a lightness/darkness inverted region. Among the dark defect candidate regions, those that do not overlap by a prescribed criterion or more with any of the lightness/darkness inverted regions are excluded from defect candidates, and then the presence of a defect is acquired on the basis of the defect candidate regions. This suppresses over-detection of defects arising from, for example, grime on the surface during external appearance inspection.

Abstract: Embodiments of the disclosure disclose a system to mitigate against adversarial input samples for machine learning (ML)/artificial intelligence (AI) models. According to one embodiment, a system receives a query from a client for a ML service. The system calculates a similarity score for the query based on a number of prior queries received from the client, the similarity score representing a similarity between the received query and the prior queries. The system determines that the query is an adversarial query in response to determining that the similarity score is above a predetermined threshold.