The apparatus includes: a weighted feature extractor configured to extract a weighted feature from an input image and generate a weighted descriptor to which a feature of a salient region is applied; a dictionary constructor configured to construct a dictionary composed of images with different characteristics of one object using the weighted descriptor to which the feature of the salient region is applied by the weighted feature extractor and store the dictionary in a database (DB); and a coefficient estimator and ID determiner configured to apply sparse representation for estimating a coefficient that allows an object to be reconstructed as much as possible with a few linear combinations of candidate objects of a target object constituting the dictionary, and perform identification using an error between a target and the reconstructed object.

A method of assessing the quality of an retinal image (such as a fundus image) includes selecting at least one region of interest within a retinal image corresponding to a particular structure of the eye (e.g. the optic disc or the macula), and a quality score is calculated in respect of the, or each, region-of-interest. Each region of interest is typically one associated with pathology, as the optic disc and the macula are. Optionally, a quality score may be calculated also in respect of the eye as a whole (i.e. over the entire image, if the entire image corresponds to the retina).

A method of extracting a warehouse in a port from a hierarchically screened remote sensing image includes the following steps: first, recognizing a texture feature of a remote sensing image and extracting edge lines of a coast of a port; then, selecting a sample of an optional irregular texture region and forming, through a CA transformation, principal component images of different hierarchies by taking a ratio of a between-class difference to an intra-class difference being maximum as an optimization condition; sequentially, extracting a correlation relationship of the warehouse in the port, and forming a feature point set with recognized warehouses to be analyzed; and last, extracting a feature of a visually sensitive image through a scene image to obtain a feedback selection of a real scene image to extract the warehouse in the port accurately.

A method of extracting a warehouse in a port from a hierarchically screened remote sensing image includes the following steps: first, recognizing a texture feature of a remote sensing image and extracting edge lines of a coast of a port; then, selecting a sample of an optional irregular texture region and forming, through a CA transformation, principal component images of different hierarchies by taking a ratio of a between-class difference to an intra-class difference being maximum as an optimization condition; sequentially, extracting a correlation relationship of the warehouse in the port, and forming a feature point set with recognized warehouses to be analyzed; and last, extracting a feature of a visually sensitive image through a scene image to obtain a feedback selection of a real scene image to extract the warehouse in the port accurately.

The described positional awareness techniques employing visual-inertial sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to provide positional awareness to machines with improved speed and accuracy.

A method and device for transforming 2D images into 3D are disclosed. The disclosed device includes a dictionary storage unit configured to store a word-depth gradient dictionary; a color patch obtainer unit configured to obtain color patches from an input image; a matching word search unit configured to transform each of the color patches obtained by the color patch obtainer unit into a SIFT descriptor form and search for words closest to the SIFT descriptors of the obtained color patches from among the words of the word-depth gradient dictionary; a matching depth gradient obtainer unit configured to obtain depth gradient information of the words matching the obtained color patches from the word-depth gradient dictionary; and a depth map generation unit configured to compute a depth from the obtained matching depth gradient for each of the obtained color patches and generate a depth map.

There is disclosed a method of generating an index of images, the index of images for enabling comparison of the image against other images, the method executable at a server. The method comprises: determining at least one key for the index, the at least one key including at least a portion of a visual features composite parameter associated with an image to be indexed, the visual features composite parameter having been determined by executing steps of; identifying a first local region of the image and a second local region of the image.

The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

The described positional awareness techniques employing visual-inertial sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to provide positional awareness to machines with improved speed and accuracy.

Methods and systems of identification of objects in query images are disclosed. Keypoints in the query images are identified corresponding to objects to be identified. Visual words are identified in a dictionary of visual words for the identified keypoints. A set of hits is identified corresponding to reference images comprising the identified keypoints. Reference images corresponding to the identified set of hits are ranked using clustering of matches in a limited pose space. The limited pose space comprises a one-dimensional table corresponding to the rotation between the object to be identified with respect to the reference image. A first subset of M reference images that obtained a rank above a predetermined threshold is then selected. Offline, hybrid and combined offline and hybrid systems for performing the proposed methods are disclosed.