Abstract: A system and method of classifying images of pathology. An image is received, normalized, and segmented normalizing the image; into a plurality of regions; A disease vector is automatically determining for the plurality of regions with at least one classifier comprising a neural network. Each of the respective plurality of regions is automatically annotated, based on the determined disease vectors. The received image is automatically graded based on at least the annotations. The neural network is trained based on at least an expert annotation of respective regions of images, according to at least one objective classification criterion. The images may be eye images, vascular images, or funduscopic images. The disease may be a vascular disease, vasculopathy, or diabetic retinopathy, for example.

Abstract: A system and method of classifying images of pathology. An image is received, normalized, and segmented normalizing the image; into a plurality of regions; A disease vector is automatically determining for the plurality of regions with at least one classifier comprising a neural network. Each of the respective plurality of regions is automatically annotated, based on the determined disease vectors. The received image is automatically graded based on at least the annotations. The neural network is trained based on at least an expert annotation of respective regions of images, according to at least one objective classification criterion. The images may be eye images, vascular images, or funduscopic images. The disease may be a vascular disease, vasculopathy, or diabetic retinopathy, for example.

Abstract: A method for transforming an image descriptor, based on a gradient histogram (h) having a plurality of histogram bins (hi), into a transformed gradient histogram descriptor (v) including a set of values (vj), wherein it is provided the step of applying at least one criterium for calculating the set of values (vj), at least one criterium having a location criterium of the histogram bins (hi). The invention also relates to an image processing device suitable for carrying out the method.

Abstract: A system and method of classifying images of pathology. An image is received, normalized, and segmented normalizing the image; into a plurality of regions; A disease vector is automatically determining for the plurality of regions with at least one classifier comprising a neural network. Each of the respective plurality of regions is automatically annotated, based on the determined disease vectors. The received image is automatically graded based on at least the annotations. The neural network is trained based on at least an expert annotation of respective regions of images, according to at least one objective classification criterion. The images may be eye images, vascular images, or funduscopic images. The disease may be a vascular disease, vasculopathy, or diabetic retinopathy, for example.

Abstract: A method for deriving a representation of an image, by processing signals corresponding to the image, comprises identifying a plurality of feature points in the image, deriving feature descriptors of feature points, and assigning feature descriptors to pre-defined center points, wherein each feature descriptor is assigned to a plurality of center points, the method further comprising, for each center point, calculating the difference between each feature descriptor assigned to said center point, deriving a value descriptor for each center point from said calculated differences, and deriving the representation from said value descriptors for said center points.

Abstract: A method for transforming an image descriptor, based on a gradient histogram (h) having a plurality of histogram bins (hi), into a transformed gradient histogram descriptor (v) including a set of values (vj), wherein it is provided the step of applying at least one criterium for calculating the set of values (vj), at least one criterium having a location criterium of the histogram bins (hi). The invention also relates to an image processing device suitable for carrying out the method.

Abstract: A method for transforming an image descriptor, based on a gradient histogram (h) having a plurality of histogram bins (hi), into a transformed gradient histogram descriptor (v) including a set of values (vj), wherein it is provided the step of applying at least one criterium for calculating the set of values (vj), at least one criterium having a location criterium of the histogram bins (hi). The invention also relates to an image processing device suitable for carrying out the method.

Abstract: A method for deriving a representation of an image, by processing signals corresponding to the image, comprises identifying a plurality of feature points in the image, deriving feature descriptors of feature points, and assigning feature descriptors to pre-defined centre points, wherein each feature descriptor is assigned to a plurality of centre points, the method further comprising, for each centre point, calculating the difference between each feature descriptor assigned to said centre point, deriving a value descriptor for each centre point from said calculated differences, and deriving the representation from said value descriptors for said centre points.

Abstract: A method for transforming an image descriptor, based on a gradient histogram (h) having a plurality of histogram bins (hi), into a transformed gradient histogram descriptor (v) including a set of values (vj), wherein it is provided the step of applying at least one criterium for calculating the set of values (vj), at least one criterium having a location criterium of the histogram bins (hi). The invention also relates to an image processing device suitable for carrying out the method.

Abstract: A method for deriving an image identifier comprises deriving a scale-space representation of an image, and processing the scale-space representation to detect a plurality of feature points having values that are maxima or minima. A representation is derived for a scale-dependent image region associated with one or more of the detected plurality of feature points. In an embodiment, the size of the image region is dependent on the scale associated with the corresponding feature point. An image identifier is derived using the representations derived for the scale-dependent image regions. The image identifiers may be used in a method for comparing images.

Abstract: A method and apparatus for processing a first sequence of images and a second sequence of images to compare the first and second sequences is disclosed. Each of a plurality of the images in the first sequence and each of a plurality of the images in the second sequence is processed by (i) processing the image data for each of a plurality of pixel neighborhoods in the image to generate at least one respective descriptor element for each of the pixel neighborhoods, each descriptor element comprising one or more bits; and (ii) forming a plurality of words from the descriptor elements of the image such that each word comprises a unique combination of descriptor element bits. The words for the second sequence are generated from the same respective combinations of descriptor element bits as the words for the first sequence.

Abstract: A method for deriving a representation of an image is described. The method involves processing signals corresponding to the image. At least one region of the image that is representative of the image is identified. In one embodiment, each identified region corresponds to a feature of the image. For each identified image region, a two dimensional function of at least part of the image region is derived and processed to obtain an intermediate representation of the at least part of the image region. A representation of the image is derived from the intermediate representation of at least one of the image regions.

Abstract: A method of deriving a representation of an image by processing signals corresponding to the image, the method comprises deriving a function of the image, where a function of a translated, scaled or rotated version of the image is a translated or scaled version of the derived function of the image, and using a plurality of frequency components of a frequency representation of the function to derive a representation of the image.

Abstract: A method for deriving a representation of an image is described. The method involves processing signals corresponding to the image. A three dimensional representation of the image is derived. The three dimensional representation of the image to used to derive the representation of the image. In one embodiment, each line of the image is defined by a first parameter (d) and a second parameter (?), and a position on each line is defined by a third parameter (t), and the three dimensional representation is parameterized by the first, second and third parameters. A set of values is extracted from the three dimensional representation at a value of the first parameter, and a functional is applied along lines, or parts of lines, of the extracted set of values, the lines extending along values of the second or third parameter.

Abstract: A method and apparatus for deriving a representation of an image is described. The method involves processing signals corresponding to the image. A two-dimensional function of the image, such as a Trace transform (T (d, ?)), of the image using at least one functional T, is derived and processed using a mask function (?) to derive an intermediate representation of the image, corresponding to a one-dimensional function. In one embodiment, the mask function defines pairs of image bands of the Trace transform in the Trace domain. The representation of the image may be derived by applying existing techniques to the derived one-dimensional function.

Abstract: A method of representing at least one image comprises deriving at least one descriptor based on color information and color interrelation information for at least one region of the image, the descriptor having at least one descriptor element, derived using values of pixels in said region, wherein at least one descriptor element for a region is derived using a non-wavelet transform. The representations may be used for image comparisons.

Abstract: A method of representing and analysing images comprises producing a plurality of descriptors of an image at one or more scales and for one or more color channels, said descriptors capturing color content and interrelation information within the regions, and associating the descriptors in a plurality of ways based on their characteristics such as scale, color channel, feature semantics, and region, and comparing such representations of images to assess the similarity of images.

Abstract: A method for encoding a descriptor of multimedia content, in which the descriptor includes a plurality of components describing respective parts of the multimedia content comprises processing the descriptor to determine a priority of the plurality of components, and encoding the components of the descriptor based on the determined priority. A method of decoding the descriptor comprises decoding a predetermined number of the plurality of components, by decoding each of the components in the priority order. Advantageously, the encoding and decoding techniques enable fast, scalable searching.

Abstract: A method of representing an image comprises processing the image to produce a second image highlighting edges in the image (eg a intensity gradient image) and deriving a descriptor based on spatially integrated or rotationally invariant representations of regions of the second image.

Abstract: A method of representing at least one image comprises deriving at least one descriptor based on color information and color interrelation information for at least one region of the image, the descriptor having at least one descriptor element, derived using values of pixels in said region, wherein at least one descriptor element for a region is derived using a non-wavelet transform. The representations may be used for image comparisons.