Abstract: A message can be encoded in an image file by mapping at least one bit of the message onto each sub-unit of the image file, and adjusting a distinguishable characteristic of each sub-unit according to the corresponding bit to produce a modified image file. The message can be decoded from the message file by comparing each sub-unit of the modified image file with a corresponding sub-unit of the original image file, and identifying at least one bit of the message based on each comparison.

Abstract: Systems and methods that allow transfer criteria to be defined based on one or more of several attributes, such as a particular user, site, or device, as well as whether individual images and/or image series are classified as thin slices, and applied to medical images in order to determine which images are downloaded, viewed, stored, and/or any number of other actions that might be performed with respect to particular images.

Abstract: This disclosure provides systems, methods, and apparatus for analyzing a video are disclosed. An input video signal can be received and a quality metric associated with the input video signal can be determined. A parameter to be used in a first object tracking process can be selected, based in part on the quality metric associated with the input video signal. A background model can be extracted from the input video signal based on an online median estimate of the intensity values of the pixels in the image frames. The online median estimate can be calculated using a quantile function.

Abstract: A method can include providing borehole data organized with respect to a cylindrical surface, defining one or more bedding planes based at least in part on the borehole data, and transforming at least a portion of the borehole data to a planar slab format for a plane interior to the cylindrical surface. Various other apparatuses, systems, methods, etc., are also disclosed.

Abstract: Provided are a medical imaging apparatus and a method of reconstructing an image capable of selecting an image reconstruction mode. The method of reconstructing an image using a medical imaging apparatus may include displaying a user interface indicating at least one of a first mode for reducing time required for reconstruction of the image and a second mode for acquiring the image with high resolution, receiving an input selecting one of the first mode and the second mode as a selected mode, displaying at least one reconstruction option corresponding to the selected mode, receiving another input selecting at least one reconstruction option among the at least one displayed reconstruction option, and reconstructing the image according to the at least one selected reconstruction option and selected mode.

Abstract: A system and method for detecting a distress condition of a person in a monitored location. The system is configured to receive an image stream of the monitored location, and detect a human body or body part within the monitored location. The system maintains and updates a list of areas in which the lack of movement is permitted, e.g., bed, sofa, chairs. Upon detecting that the person is no longer moving and exists in a new area that is not in the list of areas, the system enters into an acknowledgement session in which the system asks the person to perform a certain action if everything is fine. If the given action is detected within a pre-determined period the system updates the list of areas to add the new area therein, otherwise the system would execute a pre-defined function representing a response to the distress condition, e.g., call 911.

Abstract: An apparatus includes a hardware sensor array including a plurality of pixels arranged along at least a first dimension and a second dimension of the array, each of the pixels capable of generating a sensor reading. A hardware scanning window array includes a plurality of storage elements arranged along at least a first dimension and a second dimension of the hardware scanning window array, each of the storage elements capable of storing a pixel value based on one or more sensor readings. Peripheral circuitry for systematically transfers pixel values, based on sensor readings, into the hardware scanning window array, to cause different windows of pixel values to be stored in the hardware scanning window array at different times. Control logic coupled to the hardware sensor array, the hardware scanning window array, and the peripheral circuitry, provides control signals to the peripheral circuitry to control the transfer of pixel values.

Abstract: Methods for computed tomography data-based cycle estimation and four-dimensional reconstruction are provided. A gated reconstruction is derived from CT data acquired without gating using an added artificial trigger. The resulting images for different slices are used to determine local or slice variations over time. The local variations over time for the various slices are combined to create a respiratory cycle signal. This respiratory cycle signal is used to bin the images for different phases, allowing four-dimensional CT reconstruction.

Abstract: An image processor includes an LSRAM accessible with a higher speed than a frame memory and configured to hold a second image in a predetermined range of a first image, an image production unit configured to read an image in a predetermined range of the second image from the LSRAM and produce a third image for rough search based on the read image, an MSRAM accessible with a higher speed than the frame memory and configured to hold the third image produced by the image production unit, a search unit configured to read the third image from the MSRAM and perform first motion search based on the third image, and a search unit configured to read a fourth image in a predetermined range of the second image from the LSRAM based on a search result by the search unit and perform second motion search that is more detailed than the first motion search based on the fourth image.

Abstract: A motion determination system is disclosed. The system may receive a first camera image and a second camera image. The system may receive a first range image corresponding to the first camera image. The system may generate a first range map by fusing the first camera image and the first range image. The system may iteratively process a plurality of first features in the first range map to determine a change in position of the machine. The plurality of second features may correspond to the plurality of first features, and each of the plurality of first and second features is denoted by feature points in an image space of the camera.

Abstract: An image processor includes an LSRAM accessible with a higher speed than a frame memory and configured to hold a second image in a predetermined range of a first image, an image production unit configured to read an image in a predetermined range of the second image and produce a third image for rough search based on the read image, an MSRAM accessible with a higher speed than the frame memory and configured to hold the third image, a first search unit configured to read the third image and perform first motion search based on the third image, and a second search unit configured to read a fourth image in a predetermined range of the second image based on a search result by the first search unit and perform second motion search that is more detailed than the first motion search based on the fourth image.

Abstract: A system and method for mapping, localization and pose correction including, determining a current position of a vehicle along a travel route and a set of currently observable landmarks along the travel route relative to the current position, the set of currently observable landmarks extracted from one or more stereo images obtained from an imaging device, and querying a survey landmark database to identify a subset of surveyed landmarks relative to the current position of the vehicle. The method including determining one or more two-dimensional transform estimates between the set of currently observable landmarks and the subset of surveyed landmarks and identifying a best transform estimate from the one or more two-dimensional transform estimates that minimizes distances between the set of currently observable landmarks and the subset of surveyed landmarks. The method including correcting a pose of the vehicle based on the best transform estimate.

Abstract: A technique is proposed for enabling stable detection of an object even when the contrast of an image is lowered overall or partially. A preprocessing section 12 is added to the upstream stage of an object detecting processing section 13, and provides an obtained corrected image (image obtained by subjecting an input image to contrast enhancement) as an input image for the object detecting processing section 13. This facilitates the detection of the object even when the contrast of the entire screen is lowered due to fog or rain. Furthermore, the object detecting processing section 13 detects the object by using not only the corrected image from the preprocessing section 12 but also correction characteristic information 23 containing image characteristics regarding the correction (contrast enhancement). This attempts to reduce an influence of noises enhanced by the contrast enhancement.

Abstract: An exemplary method for surfacing feature changes occurring over time at a location is provided. The method includes identifying sets of images captured at a location over a period of time. The sets of images depict objects at the location. Visual changes at the location over the period of time are determined for a given physical feature of the objects depicted in images from the sets of images based on at least a comparison of features in corresponding areas of a first and a second 3D geometry of the location associated with the images. The first and second 3D geometries depict physical features of objects at the location at different times. The sets of images may be ranked based on the determined visual changes for each set. A given set of images from the sets of images may be selected for display on a user interface based on the ranking.

Abstract: Disclosed is a system and method for detecting a human in an image, and a corresponding activity. The image is captured, wherein the image comprises a plurality of pixels having gray scale information and a depth information. The image is segmented into a plurality of segments based upon the depth information. A connected component analysis is performed on a segment in order to segregate the one or more objects into noisy objects and candidate objects, the noisy objects are eliminated from the segment. A plurality of features are extracted from the candidate objects, and are evaluated using a Hidden Markov Model (HMM) model in order to determine the candidate objects as one of the human or non-human. The corresponding activity associated with the human is detected based on a depth value associated with each pixel corresponding to the candidate object in the image.

Abstract: Systems and methods relating to a cell-based pattern recognition tool for microscopy images from tissue sections are described, wherein cell features are extracted and a classifier is built in accordance with a particular application using an interactive training tied to a computerized platform, the result is an application-specific classifier that further processes images in accordance with the specific application, thereby tuning an automated process for cell based pattern recognition.

Abstract: Medical image data of at least one medical imaging apparatus are prepared in a computerized procedure wherein the medical image data are received by a reception unit, a first subset of the medical image data is selected by at least one first filter unit, the first subset of the medical image data is processed by at least one second filter unit, with the processing leading to a second subset, the second subset of the medical image data are sorted and/or grouped by the processing unit, a presentation type for the second subset of the medical image data is determined by means of the processing unit, and the second subset of the medical image data is presented as an output according to a defined presentation type by an output unit.

Abstract: A behavioral recognition system may include both a computer vision engine and a machine learning engine configured to observe and learn patterns of behavior in video data. Certain embodiments may be configured to detect and evaluate the presence of sea-surface oil on the water surrounding an offshore oil platform. The computer vision engine may be configured to segment image data into detected patches or blobs of surface oil (foreground) present in the field of view of an infrared camera (or cameras). A machine learning engine may evaluate the detected patches of surface oil to learn to distinguish between sea-surface oil incident to the operation of an offshore platform and the appearance of surface oil that should be investigated by platform personnel.

Abstract: A method for a continuous scoring scheme used for the assessment of biomarker expressions in the analysis of tissue sections, and digital images thereof, is based on cell classifications.

Abstract: A computing device can be controlled based on changes in the angle of a user's head with respect to the device, such as due to the user tilting the device and/or the user tilting his head with respect to the device. Such control based on the angle of the user's head can be achieved even when the user is operating the device “off-axis” or when the device is not orthogonal and/or not centered with respect to the user. This can be accomplished by using an elastic reference point that dynamically adjusts to a detected angle of the user's head with respect to the device. Such an approach can account for differences between when the user is changing his natural resting position and/or the resting position of the device and when the user is intending to perform a gesture based on the angle of the user's head relative to the device.