10995 results about "Imaging Feature" patented technology

In a method for determining the general semantic theme of a group of images, whereby each digitized image is identified as belonging to a specific group of images, one or more image feature measurements are extracted from each of the digitized images in an image group, and then used to produce an individual image confidence measure that an individual image belongs to one or more semantic classifications. Then, the individual image confidence measures for the images in the image group are used to produce an image group confidence measure that the image group belongs to one or more semantic classifications, and the image group confidence measure is used to decide whether the image group belongs to one or to none of the semantic classifications, whereby the selected semantic classification constitutes the general semantic theme of the group of images. Additionally, a plurality of semantic theme processors, one for each semantic classification, are provided to produce enhanced value imaging services and products for image groups that fall into an appropriate semantic theme.

Video programs (media) are analyzed, often using computerized image feature analysis methods. Annotator index descriptors or signatures that are indexes to specific video scenes and items of interest are determined, and these in turn serve as an index to annotator metadata (often third party metadata) associated with these video scenes. The annotator index descriptors and signatures, typically chosen to be free from copyright restrictions, are in turn linked to annotator metadata, and then made available for download on a P2P network. Media viewers can then use processor equipped video devices to select video scenes and areas of interest, determine the corresponding user index, and send this user index over the P2P network to search for index linked annotator metadata. This metadata is then sent back to the user video device over the P2P network. Thus video programs can be enriched with additional content without transmitting any copyrighted video data.

The invention relates to a visual recognition and positioning method for robot intelligent capture application. According to the method, an RGB-D scene image is collected, a supervised and trained deep convolutional neural network is utilized to recognize the category of a target contained in a color image and a corresponding position region, the pose state of the target is analyzed in combinationwith a deep image, pose information needed by a controller is obtained through coordinate transformation, and visual recognition and positioning are completed. Through the method, the double functions of recognition and positioning can be achieved just through a single visual sensor, the existing target detection process is simplified, and application cost is saved. Meanwhile, a deep convolutional neural network is adopted to obtain image features through learning, the method has high robustness on multiple kinds of environment interference such as target random placement, image viewing anglechanging and illumination background interference, and recognition and positioning accuracy under complicated working conditions is improved. Besides, through the positioning method, exact pose information can be further obtained on the basis of determining object spatial position distribution, and strategy planning of intelligent capture is promoted.

The present disclosure relates to systems and methods for modeling, recognizing, and tracking object images in video files. In one embodiment, a video file, which includes a plurality of frames, is received. An image of an object is extracted from a particular frame in the video file, and a subsequent image is also extracted from a subsequent frame. A similarity value is then calculated between the extracted images from the particular frame and subsequent frame. If the calculated similarity value exceeds a predetermined similarity threshold, the extracted object images are assigned to an object group. The object group is used to generate an object model associated with images in the group, wherein the model is comprised of image features extracted from optimal object images in the object group. Optimal images from the group are also used for comparison to other object models for purposes of identifying images.

The invention relates to an intelligent and automatic delineation method for a gross tumor volume and organs at risk, which includes the following steps: (1) tumor multi-modal (mode) image reconstruction, de-noising, enhancement, registration, fusion and other preprocessing; (2) automatic extraction of tumor image features: automatically extracting one or more pieces of tumor image group (texture feature spectrum) information from CT, CBCT, MRI, PET and/or ultrasonic multi-modal (mode) tumor medical image data; and (3) intelligently and automatically delineating a gross tumor volume and organs at risk through deep learning, machine learning, artificial intelligence, regional growing, the graph theory (random walk), a geometric level set, and/or a statistical theory method. A gross tumor volume (GTV) and organs at risk (OAR) can be delineated with high precision.

A method of generating one or more new digital images using an original digitally-acquired image including a selected image feature includes identifying within a digital image acquisition device one or more groups of pixels that correspond to the selected image feature based on information from one or more preview images. A portion of the original image is selected that includes the one or more groups of pixels. The technique includes automatically generating values of pixels of one or more new images based on the selected portion in a manner which includes the selected image feature within the one or more new images.

A digital mirror system is provided that emulates a traditional mirror by displaying real-time video imagery of a user who stands before it. The digital mirror system provides a plurality of digital mirror modes, including a traditional mirror mode and a third person mirror mode. The digital mirror system provides a plurality of digital mirroring features including an image freeze feature, an image zoom feature, and an image buffering feature. The digital mirror system provides a plurality of operational states including a digital mirroring state and an alternate state, the alternate state including a power-conservation state and/or a digital picture frame state. The digital mirror system provides a user sensor that automatically transitions between operational states in response to whether or not a user is detected before the digital mirror display screen for a period of time. The digital mirror system provides for hands-free user control using speech recognition, the speech recognition being employed to enable a user to selectively access one or more of the digital mirror modes or features in response to verbal commands relationally associated with those modes or features.

An optical coherence tomography (OCT) system including a polarizing splitter disposed to direct light in an interferometer such that the OCT detector operates in a noise-optimized regime. When scanning an eye, the system detector simultaneously produces a low-frequency component representing a scanning laser ophthalmoscope-like (SLO-like) image pixel and a high frequency component representing a two-dimensional (2D) OCT en face image pixel of each point. The SLO-like image is unchanging with depth, so that the pixels in each SLO-like image may be quickly realigned with the previous SLO-like image by consulting prominent image features (e.g., vessels) should lateral eye motion shift an OCT en face image during recording. Because of the pixel-to-pixel correspondence between the simultaneous OCT and SLO-like images, the OCT image pixels may be remapped on the fly according to the corresponding SLO-like image pixel remapping to create an undistorted 3D image data set for the scanned region.

A system and method for recognizing instances of classes in a 2D image using 3D class models and for recognizing instances of objects in a 2D image using 3D class models. The invention provides a system and method for constructing a database of 3D class models comprising a collection of class parts, where each class part includes part appearance and part geometry. The invention also provides a system and method for matching portions of a 2D image to a 3D class model. The method comprises identifying image features in the 2D image; computing an aligning transformation between the class model and the image; and comparing, under the aligning transformation, class parts of the class model with the image features. The comparison uses both the part appearance and the part geometry.

A illumination device sequentially projects a selective set of spatially encoded intensity light pulses toward a scene. The spatially encoded patterns are generated by an array of diffractive optical or holographic elements on a substrate that is rapidly translated in the path of the light beam. Alternatively, addressable micromirror arrays or similar technology are used to manipulate the beam wavefront. Reflected light is collected onto an individual photosensor or a very small set of high performance photodetectors. A data processor collects a complete set of signals associated with the encoded pattern set. The sampled signals are combined by a data processing unit in a prescribed manner to calculate range estimates and imaging features for elements in the scene. The invention may also be used to generate three dimensional reconstructions.

The present invention is a diagnosis support system providing automated guidance to a user by automated retrieval of similar disease images and user feedback. High resolution standardized labeled and unlabeled, annotated and non-annotated images of diseased tissue in a database are clustered, preferably with expert feedback. An image retrieval application automatically computes image signatures for a query image and a representative image from each cluster, by segmenting the images into regions and extracting image features in the regions to produce feature vectors, and then comparing the feature vectors using a similarity measure. Preferably the features of the image signatures are extended beyond shape, color and texture of regions, by features specific to the disease. Optionally, the most discriminative features are used in creating the image signatures. A list of the most similar images is returned in response to a query. Keyword query is also supported.

An information processing apparatus according to the present invention includes a three-dimensional model storage unit configured to store data of a three-dimensional model that describes a geometric feature of an object, a two-dimensional image input unit configured to input a two-dimensional image in which the object is imaged, a range image input unit configured to input a range image in which the object is imaged, an image feature detection unit configured to detect an image feature from the two-dimensional image input from the two-dimensional image input unit, an image feature three-dimensional information calculation unit configured to calculate three-dimensional coordinates corresponding to the image feature from the range image input from the range image input unit, and a model fitting unit configured to fit the three-dimensional model into the three-dimensional coordinates of the image feature.

A system for image registration and quantitative feature extraction for multiple image sets. The system includes an imaging workstation having a data processor and memory in communication with a database server in communication. The a data processor capable of inputting and outputting data and instructions to peripheral devices and operating pursuant to a software product and accepts instructions from a graphical user interface capable of interfacing with and navigating the imaging software product. The imaging software product is capable of instructing the data processor, to register images, segment images and to extract features from images and provide instructions to store and retrieve one or more registered images, segmented images, quantitative image features and quantitative image data and from the database server.

A method for efficiently and accurately inventorying image features such as timber, including steps of segmenting digital images into tree stands, segmenting tree stands into tree crowns, each tree crown having a tree crown area, classifying tree crowns based on species, and analyzing the tree crown classification to determine information about the individual tree crowns and aggregate tree stands. The tree crown area is used to determine physical information such as tree diameter breast height, tree stem volume and tree height. The tree crown area is also used to determine the value of timber in tree stands and parcels of land using tree stem volume and market price of timber per species.

The present invention provides a gaming console 40 having display means 41, and game control means 100 arranged to control images displayed on the display means 41. The game control means 100 are arranged to play a game wherein a plurality of symbols 43 are randomly selected and displayed on the display means 41 and, if a winning combination results, the console 40 pays a prize. The console 40 is characterised in that the displayed images include a first image component 31 which displays basic game features and a second image component 50 which displays image features which appear superimposed on the first image component 31. The second image component 50 is transparent, whereby the first image component 31 is visible through the second image component 50 under the control of the control means 100.

An image processing device comprises receiving means operable to receive, from a camera, a captured image corresponding to an image of a scene captured by the camera. The scene contains at least one object. The device comprises determining means operable to determine a distance between the object within the scene and a reference position defined with respect to the camera, and generating means operable to detect a position of the object within the captured image, and to generate a modified image from the captured image based on image features within the captured image which correspond to the object in the scene. The generating means is operable to generate the modified image by displacing the position of the captured object within the modified image with respect to the determined position of the object within the captured image by an object offset amount which is dependent on the distance between the reference position and the object in the scene so that, when the modified image and the captured image are viewed together as a pair of images on a display, the captured object appears to be positioned at a predetermined distance from the display.

Disclosed is an improved algorithm for estimating the 3D shape of a 3-dimensional object, such as a human face, based on information retrieved from a single photograph by recovering parameters of a 3-dimensional model and methods and systems using the same. Beside the pixel intensity, the invention uses various image features in a multi-features fitting algorithm (MFF) that has a wider radius of convergence and a higher level of precision and provides thereby better results.

A position/orientation measurement apparatus holds a three-dimensional shape model of a object, acquires approximate value indicating a position and an orientation of the object, acquires a two-dimensional image of the object, projects a geometric feature of the three-dimensional shape model on the two-dimensional image based on the approximate value, calculates the direction of the geometric feature of the three-dimensional shape model projected on the two-dimensional image, detects an image feature based on the two-dimensional image, calculates the direction of the image feature, associates the image feature and the geometric feature by comparing the direction of the image feature calculated based on the two-dimensional image and the direction of the geometric feature calculated based on the three-dimensional shape model, and calculates the position and orientation of the object by correcting the approximate value based on the distance between the geometric feature and the image feature associated therewith.

A method of tracking objects on a plane within video images of the objects captured by a video camera. The method includes processing the captured video images so as to extract one or more image features from each object, detecting each of the objects from a relative position of the objects on the plane as viewed from the captured video images by comparing the one or more extracted image features associated with each object with sample image features from a predetermined set of possible example objects which the captured video images may contain; and generating object identification data for each object, from the comparing, which identifies the respective object on the plane. The method further includes generating a three dimensional model of the plane and logging, for each detected object, the object identification data for each object which identifies the respective object on the plane together with object path data. The object path provides a position of the object on the three dimensional model of the plane from the video images with respect to time and relates to the path that each object has taken within the video images. The logging includes detecting an occlusion event in dependence upon whether a first image feature associated with a first of the objects obscures a whole or part of at least a second image feature associated with at least a second of the objects; and, if an occlusion event is detected, associating the object identification data for the first object and the object identification data for the second object with the object path data for both the first object and the second object respectively and logging the associations. The logging further includes identifying at least one of the objects involved in the occlusion event in dependence upon a comparison between the one or more image features associated with that object and the sample image features from the predetermined set of possible example objects, and updating the logged path data after the identification of at least one of the objects so that the respective path data is associated with the respective identified object.