Abstract: According to various embodiments, a method for providing task related information to a user may be provided. The method may include: determining location information based on a spatial model; determining task information based on a task model; determining sensor information; determining output information based on the location information, task information and sensor information; and providing the output information to the user. In a specific embodiment, the output information may comprise an orientation cue, an error indication or a contextual cue to assist the user in performing the task associated with the location detected by a vision recognition method, and the output information can be provided to the user as augmented reality in a wearable device.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A two-dimensional retinal fundus image of the retinal fundus of an eye is processed by optic disc segmentation (2) followed by cup segmentation 4. Data derived from the optic disc segmentation (i.e. the output of the disc segmentation (2) and/or data derived from the output of the optic disc segmentation step, e.g. by a smoothing operation 3) and data derived from the out-put of the optic cup segmentation (i.e. the output of the cup segmentation (4) and/or data derived from the output of the optic disc segmentation, e.g. by a smoothing operation 5) are fed (6) to an adaptive model which has been trained to generate from such inputs a value indicative of cup-to-disc ratio (CDR) of the eye. The CDR is indicative of glaucoma. Thus, the method can be used to screen patients for glaucoma.

Abstract: A text detection device is provided. The text detection device may include: an image input circuit configured to receive an image; an edge property determination circuit configured to determine a plurality of edge properties for each of a plurality of scales of the image; and a text location determination circuit configured to determine a text location in the image based on the plurality of edge properties for the plurality of scales of the image.

Abstract: A method for determining the position of an optic cup boundary in a 2D retinal image. The method includes detecting kinks in blood vessels at an estimated boundary of the optic cup and the optic disc, and determining the position of the optic cup boundary in the 2D retinal image based on the detected kinks. The determined optic cup boundary may be used for determining a CDR which may in turn be used for determining a risk of contracting glaucomatous.

Abstract: A method and system for generating an entirely well-focused image of a three-dimensional scene. The method comprises the steps of a) learning a prediction model including at least a focal depth probability density function (PDF), h(k), for all depth values k, from historical tiles of the scene; b) predicting the possible focal surfaces in subsequent tiles of the scene by applying the prediction model; c) for each value of k, examining h(k) such that if h(k) is below a first threshold, no image is acquired at the depth k? for said one tile; and if h(k) is above or equal to a first threshold, one or more images are acquired in a depth range around said value of k for said one tile; and d) processing the acquired images to generate a pixel focus map for said one tile.

Abstract: A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode.

Abstract: A method for determining a grade of nuclear cataract in a test image. The method includes: (1a) defining a contour of a lens structure in the test image, the defined contour of the lens structure comprising a segment around a boundary of a nucleus of the lens structure; (1b) extracting features from the test image based on the defined contour of the lens structure in the test image; and (1c) determining the grade of nuclear cataract in the test image based on the extracted features and a grading model.

Abstract: A method and system for maintaining a database of reference images, the database including a plurality of sets of images, each set associated with one location or object. The method comprises the steps of identifying local features of each set of images; determining distances between each local feature of each set and the local features of all other sets; identifying discriminative features of each set of images by removing local features based on the determined distances; and storing the discriminative features of each set of images.

Abstract: A method performed by a computer system for detecting opacity in an image of the lens of an eye. The method includes detecting a region of interest in a picture of the lens, and processing the region of interest to produce a modified image using an algorithm which emphasizes opacity associated with a cortical cataract relative to opacity caused by other types of opacity, such as opacity caused by posterior sub-capsular cataracts (PSC). The modified image may be used for grading the level of cortical opacity, by measuring, in the modified image, the proportion of opacity in at least one area of the region of interest.

Abstract: A two-dimensional retinal fundus image of the retinal fundus of an eye is processed by optic disc segmentation (2) followed by cup segmentation 4. Data derived from the optic disc segmentation (i.e. the output of the disc segmentation (2) and/or data derived from the output of the optic disc segmentation step, e.g. by a smoothing operation 3) and data derived from the out-put of the optic cup segmentation (i.e. the output of the cup segmentation (4) and/or data derived from the output of the optic disc segmentation, e.g. by a smoothing operation 5) are fed (6) to an adaptive model which has been trained to generate from such inputs a value indicative of cup-to-disc ratio (CDR) of the eye. The CDR is indicative of glaucoma. Thus, the method can be used to screen patients for glaucoma.

Abstract: A method and system for generating an entirely well-focused image of a three-dimensional scene. The method comprises the steps of a) learning a prediction model including at least a focal depth probability density function (PDF), h(k), for all depth values k, from historical tiles of the scene; b) predicting the possible focal surfaces in subsequent tiles of the scene by applying the prediction model; c) for each value of k, examining h(k) such that if h(k) is below a first threshold, no image is acquired at the depth k? for said one tile; and if h(k) is above or equal to a first threshold, one or more images are acquired in a depth range around said value of k for said one tile; and d) processing the acquired images to generate a pixel focus map for said one tile.

Abstract: A method of annotating footage that includes a structured text broadcast stream, a video stream and an audio stream, the method includes the steps of: extracting directly or indirectly one or more keywords and/or features from at least said structured text broadcast streams, temporally annotating said footage with said keywords and/or features analysing temporally adjacent annotated keywords and/or features to determine information about one or more events within said footage. Also provided are: a data store for storing video footage, a method of generation of a personalised video summary, a system for annotating footage and a system for generation of a personalised video summary.

Abstract: A method for use in indexing video footage, the video footage comprising an image signal and a corresponding audio signal relating to the image signals, the method comprising extracting audio features from the audio signal of the video footage and visual features from the image signal of the video footage; comparing the extracted audio and visual features with predetermined audio and visual keywords; identifying the audio and visual keywords associated with the video footage based on the comparison of the extracted video and visual features with the predetermine audio and visual keywords; and determining the presence of events in the video footage based on the audio and visual keywords associated with the video footage.

Abstract: A method, an apparatus and a computer program product for indexing and retrieving image data using visual keywords (108) is disclosed. Visual keywords (108) are prototypical, visual tokens (104) and are extracted from samples of visual documents (100) in a visual-content domain via supervised and/or unsupervised learning processes. An image or a video-shot key frame is described and indexed by a signature (112) that registers the spatial distribution of the visual keywords (108) present in its visual content. Visual documents (100) are retrieved for a sample query (120) by comparing the similarities between the signature (112) of the query (120) and those of visual documents (100) in the database. The signatures (112) of visual documents (100) are generated based on spatial distributions of the visual keywords (108). Singular-value decomposition (114) is applied to the signatures (112) to obtain a coded description (116).