Abstract: Apparatus for plotting pathological diagnoses on anatomical diagrams is provided. The apparatus may include a mapping tool. The mapping tool may identify a plurality of biopsy marker records including a received criterion. The mapping tool may identify a body part image associated with a body part image ID. The mapping tool may section the body part image into a first quadrant and a second quadrant. The mapping tool may loop through the plurality of biopsy marker records to identify an X,Y coordinate associated with each of the plurality of biopsy marker records. For each X,Y coordinate identifying a location within the first quadrant, the mapping tool may iteratively tally a first count for the first quadrant. For each X,Y coordinate identifying a location within the second quadrant, the mapping tool may iteratively tally a second count for the second quadrant.

Abstract: There is provided a method for measuring anatomical features of a body, comprising obtaining a video having a plurality of frames; analyzing each frame of the video to detect the position of each anatomical feature; if necessary, calculating the distance travelled between frames of an anatomical feature using a predetermined reference object within the video (e.g., the true length between one's knee and hip); and if necessary, using the raw video and extracted features to predict a biomechanical output, and/or calculating summary metrics from the aggregate data from the video.

Abstract: TOOL TRACKING A system for determining the location of a toolpiece, wherein: the toolpiece is carried by a tool and the tool comprises an imaging device for capturing images of the environment around the tool; and the system comprises an image processor communicatively coupled to the imaging device for receiving images therefrom and having access to one or more reference images of an expected environment, the image processor being configured to compare an image captured by the imaging device with at least one reference image to identify a match therebetween and to determine in dependence on characteristics of that match the location of the toolpiece.

Abstract: Techniques described herein may involve audio settings based on an environment. An example implementation may involve a playback device playing back first audio content and during playback of at least a portion of the first audio content, detecting, via the at least one microphone, an audio signal. At least a portion of the detected audio signal may include a reflection of the first audio content played back by the playback device. The playback device may determine an equalization setting based on at least the determined one or more reflection characteristics and apply the determined equalization setting during playback of second audio content.

Abstract: Provided is a generation unit that generates an identification region map indicating whether or not each divided object data is visible from each position in a three-dimensional space by using information regarding orientation of a normal vector dividing an object in the three-dimensional space and information regarding an outline of the object.

Abstract: An excrement judgement system according to the embodiment including: an acquisition unit that acquires image information on an image in which excrement excreted in a defecation action per time is captured; a determination unit that determines properties of a plurality of stools included in the image information and stool amounts corresponding to the properties of the plurality of stools; and an outputting unit that associates the properties of the plurality of stools and the stool amounts corresponding to the properties of the plurality of stools that are determined by the determination unit, and outputs the associated properties and stool amounts to a device to be displayed.

Abstract: Soundfield signals such as e.g. Ambisonics carry a representation of a desired sound field. The Ambisonics format is based on spherical harmonic decomposition of the soundfield, and Higher Order Ambisonics (HOA) uses spherical harmonics of at least 2nd order. However, commonly used loudspeaker setups are irregular and lead to problems in decoder design. A method for improved decoding an audio soundfield representation for audio playback comprises calculating a panning function (W) using a geometrical method based on the positions of a plurality of loudspeakers and a plurality of source directions, calculating a mode matrix (?) from the loudspeaker positions, calculating a pseudo-inverse mode matrix (?+) and decoding the audio soundfield representation. The decoding is based on a decode matrix (D) that is obtained from the panning function (W) and the pseudo-inverse mode matrix (?+).

Abstract: A trailer angle determination system for a vehicle is provided. The trailer angle determination system has a sensing unit for sensing a trailer connected to the vehicle and an evaluation unit. The sensing unit is arranged outside of the center of the vehicle and toward the trailer, and the distance between the sensing unit and the center of the vehicle is known. The trailer has symmetrical feature pairs with respect to a longitudinal axis of symmetry of the trailer. The sensing unit is designed to sense the symmetrical feature pairs of the trailer. The evaluation unit is designed to determine the angle between the trailer and the vehicle based on the sensed symmetrical feature pairs of the trailer and the known distance between the sensing unit and the center of the vehicle.

Abstract: In one embodiment, a diagnosis support apparatus includes: an input circuit configured to acquire a first medical image; and processing circuitry configured to generate a second medical image from the first medical image in such a manner that information included in the second medical image is reduced from information included in the first medical image, extract auxiliary information from the first medical image, and perform inference of a disease by using the second medical image and the auxiliary information.

Abstract: A method of vertical planar surface detection includes identifying, at an electronic device, a plurality of feature points of an environment proximate the electronic device based on imagery captured at the electronic device and based on received non-visual pose information obtained from non-visual sensors. The electronic device queries the plurality of feature points to identify one or more planar surfaces in the imagery captured at the electronic device. Further, the electronic device computes a plurality of oriented point vectors associated with the one or more planar surfaces. A vertical planar surface is detected based at least in part on one or more of the plurality of oriented point vectors being aligned in a direction perpendicular to gravity within a predetermined threshold.

Abstract: Systems and techniques for automatic digital parameter adjustment are described that leverage insights learned from an image set to automatically predict parameter values for an input item of digital visual content. To do so, the automatic digital parameter adjustment techniques described herein captures visual and contextual features of digital visual content to determine balanced visual output in a range of visual scenes and settings. The visual and contextual features of digital visual content are used to train a parameter adjustment model through machine learning techniques that captures feature patterns and interactions. The parameter adjustment model exploits these feature interactions to determine visually pleasing parameter values for an input item of digital visual content. The predicted parameter values are output, allowing further adjustment to the parameter values.

Abstract: Systems and methods for determining object intentions through visual attributes are provided. A method can include determining, by a computing system, one or more regions of interest. The regions of interest can be associated with surrounding environment of a first vehicle. The method can include determining, by a computing system, spatial features and temporal features associated with the regions of interest. The spatial features can be indicative of a vehicle orientation associated with a vehicle of interest. The temporal features can be indicative of a semantic state associated with signal lights of the vehicle of interest. The method can include determining, by the computing system, a vehicle intention. The vehicle intention can be based on the spatial and temporal features. The method can include initiating, by the computing system, an action. The action can be based on the vehicle intention.

Abstract: A body information acquisition device includes: a skeleton point detection unit configured to detect a skeleton point of a person included in a captured image; a body information acquisition unit configured to acquire body information of the person based on detection of the skeleton point; and an imaging state determination unit configured to determine whether an imaging state of the person reflected in the captured image corresponds to a specific imaging state specified based on a predetermined evaluation index value, in which the body information acquisition unit does not acquire the body information based on the detection of the skeleton point when the imaging state corresponds to the specific imaging state.

Abstract: Methods, computer readable media, and devices for exceeding the limits of visual-linguistic multi-task learning are disclosed. One method may include identifying a multi-modal multi-task classification dataset including a plurality of data examples, creating a transformer machine learning model to predict a plurality of categorical attributes of a product, and training the transformer machine learning model based on the multi-modal multi-task classification dataset using an alpha decay schedule and dynamically allocating task-specific parameters for at least one of the plurality of task-specific classification heads based on task complexity.

Abstract: Using a combination of image recognition, deep learning, and expert rules, disclosed systems and methods automatically identify, assess and estimate damage to vehicles based on input vehicle images. Analysis includes determinations as to type of damage, extent of damage and absolute size of damage. Disclosed embodiments include techniques of homography, allometry and semantic segmentation combined with more conventional object recognition technologies such as R-CNN in order to provide greater precision, more detailed analysis at lower computational cost.

Abstract: For one embodiment of the present invention, a method of object detection and part segmentation is described. The method includes generating a three-dimensional (3D) point cloud including a plurality of points in a volume of a space of the point cloud based on receiving point cloud dat. The method further includes obtaining, with a global transformer, a set of global feature tokens and local feature points of the 3D point cloud, enforcing with an attention mechanism information flow across the set of global feature tokens and local feature point, and generating, with the global transformer, a set of new global feature tokens and new local feature points with modeled global context information.

Abstract: Described are systems and methods that use one or more two-dimensional (“2D”) body images of a body to determine body fat measurements of that body. For example, a standard 2D camera of a portable device, such as a cell phone, tablet, laptop, etc., may be used to generate one or more 2D body images of a user. Those 2D body images, or image, may be processed using the disclosed implementations to determine a body fat measurement of the body represented in the image.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Methods and systems are presented for detecting a boundary of a document within a digital image. Upon receiving an image, the image is converted into a binary image. One or more kernel-based transformations are performed on the binary image using a horizontal kernel and a vertical kernel. A plurality of edges are identified based on the one or more kernel-based transformations. The plurality of edges includes a plurality of horizontal edges and a plurality of vertical edges. Multiple quadrilaterals are constructed using different combinations of horizontal edges and vertical edges from the plurality of edges. A particular quadrilateral is selected from the multiple quadrilaterals based on how well the edges fit the perimeters of the quadrilaterals. The selected quadrilateral is used to define a boundary of the document within the digital image.

Abstract: The present disclosure describes an image processor of a display device. The image processing includes a pre-processor, a segmentation processor, and a correction processor. The pre-processor performs spatial filtering on an input image signal and output a line image signal. The segmentation processor classifies a class of the line image signal and outputting a segmentation signal representing the class. The correction processor corrects the segmentation signal based on the line image signal. Additionally, the correction processor detects a class boundary of the segmentation signal, detects an edge within an edge region of the line image signal corresponding to the class boundary, and corrects the segmentation signal based on the detected edge.