Abstract: Techniques are generally described for object detection in image data. A first frame of image data associated with a first domain is received by a detector executing on at least one computing device. The detector generates a first feature data in the first domain. The first feature data is transformed from the first domain into a second feature data in a second domain. The detector may be effective to detect objects in the second domain. A location of an object in the first frame of image data is determined based at least in part on the second feature data.

Abstract: This disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. A processor performs a method for estimating an illumination spectrum of an image which defines an input spectrum for each point of the image. The processor determines for each point of a first set of points a measure of variation in relation to the input spectrum of that point. The processor then selects one point from the first set based on the measure of variation. The processor determines a cluster of points based on the input spectrum of the selected point and determines an estimate for the illumination spectrum based on the input spectra of points in the cluster. Since the processor selects a point based on the measure of variation and then determines a cluster based on that point, it performs better than using other methods where the cluster is determined based on a random starting point, for example.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images (302) to determine an output color value for a component spectrum of a decomposed multispectral or hyperspectral image. A processor (120) first determines or accesses the component spectrum and a first color value for the component spectrum and receives an input color value for a pixel location in the image, wherein the component spectrum contributes to the pixel. The processor (120) then determines the output color value for the component spectrum based on the first color value for the component spectrum, the input color value and a utility score and stores the output color value for the component spectrum on a datastore (118). It is an advantage that the processor (120) receives a color value for a pixel location but the output color value is not exactly the received color value but one that is determined based on a utility score.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral, or multispectral images. In particular, but is not limited to, a method, software and computer for estimating shape information or a photometric invariant of a location of image of a scene. The image data (300) indexed by wavelength ? and polarization filter angle ?. For each wavelength ? index, a polarization angle ? is estimated from the image data (300) by the processor (810). The processor (810) then also estimates the shape information (such as azimuth ?, such as zenith ?, or surface normal) or photometric invariants (such as refractive index) based on the estimated polarization angle ? for each wavelength index ?. Greater accuracies can be achieved in the estimated shape information and/or photometric invariants by using wavelength-indexed data. Further, surface information or photometric invariant can be estimated based upon polarization in a single-view hyperspectral or multi-spectral imagery.

Abstract: This disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. A processor performs a method for estimating an illumination spectrum of an image which defines an input spectrum for each point of the image. The processor determines for each point of a first set of points a measure of variation in relation to the input spectrum of that point. The processor then selects one point from the first set based on the measure of variation. The processor determines a cluster of points based on the input spectrum of the selected point and determines an estimate for the illumination spectrum based on the input spectra of points in the cluster. Since the processor selects a point based on the measure of variation and then determines a cluster based on that point, it performs better than using other methods where the cluster is determined based on a random starting point, for example.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. Processing of the images includes object recognition and image enhancement in a way that harnesses the information available from the image based on the wavelength indexed spectral data these types of images provide. Illumination spectrum of such an image is estimated (FIG. 21) using a cost function based on a dichromatic reflection model and a constraint term. This method may be performed on sub-images of the image. A method for selecting these sub-images (FIG. 23) is also disclosed. A method of determining photometric parameters of the image given the estimated illumination spectrum (FIG. 22) is also disclosed.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral, multispectral or trichromatic images. In particular, but is not limited to, a method, software and computer for estimating parameters of a reflectance model applied to an image is disclosed. Examples of processing of the images using the estimated parameters includes material recognition, re-coloring and re-shading of objects represented in the image. That is, a computer implemented method is provided of estimating one or more of photogrammetric parameters, ?(u) surface shape N and index of refraction n(u,?) represented in a reflectance image having one or more known illumination directions L and a known viewing direction V, the method comprising optimizing (802) the difference between the reflectance image and a reflectance model, the reflectance model being based on surface shape N; the material index of refraction n(u,?) and a set of photogrammetric parameters ?(u).

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images (302) to determine an output colour value for a component spectrum of a decomposed multispectral or hyperspectral image. A processor (120) first determines or accesses the component spectrum and a first colour value for the component spectrum and receives an input colour value for a pixel location in the image, wherein the component spectrum contributes to the pixel. The processor (120) then determines the output colour value for the component spectrum based on the first colour value for the component spectrum, the input colour value and a utility score and stores the output colour value for the component spectrum on a datastore (118). It is an advantage that the processor (120) receives a colour value for a pixel location but the output colour value is not exactly the received colour value but one that is determined based on a utility score.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. In particular, but is not limited to methods, software and computer systems for determining underlying spectra of an image of a scene. The image data comprises for each pixel location a sampled image spectrum that is a mixture of plural reflectance spectra. Processor 310 determines or accesses plural hyperplanes that each have plural linearly independent basis vectors. Each hyperplane represents an estimate of one of the plural reflectance spectra. The processor 310 then determines for each pixel location, a contribution of the plural basis vectors of each hyperplane to the image spectrum of that pixel location. The processor 310 determines or accesses plural hyperplanes and not plural endmembers directly. Hyperplanes are two-dimensional while endmembers are only one-dimensional. As a result, hyperplanes carry more information, such as the illumination spectrum and are therefore of greater use.

Abstract: The invention concerns the compact representation of a reflectance spectrum of a material. For example, for in compression, identification and comparison of reflectance spectrum data of multiple materials. The compressed representation interpolating a spline curve to the reflectance spectrum data, the spline curve having a set of control points, a knot vector, and representing wavelength and reflectance as functions of an independent parameter (42). Then removing one or more knots from the knot vector that minimise a cost function in a parameter domain of the spline curve based on the wavelength function (44). Aspects of the invention include a method, software, a computer system and the compact representation itself.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral, or multispectral images. In particular, but is not limited to, a method, software and computer for estimating shape information or a photometric invariant of a location of image of a scene. The image data (300) indexed by wavelength ? and polarisation filter angle ?. For each wavelength ? index, a polarisation angle ? is estimated from the image data (300) by the processor (810). The processor (810) then also estimates the shape information (such as azimuth a, zenith ?, or surface normal) or photometric invariants (such as refractive index) based on the estimated polarisation angle cp for each wavelength index ?. Greater accuracies can be achieved in the estimated shape information and/or photometric invariants by using wavelength-indexed data. Further, surface information or photometric invariant can be estimated based upon polarisation in a single-view hyperspectral or multi-spectral imagery.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. In particular, but is not limited to methods, software and computer systems for determining underlying spectra of an image of a scene. The image data comprises for each pixel location a sampled image spectrum that is a mixture of plural reflectance spectra. Processor 310 determines or accesses plural hyperplanes that each have plural linearly independent basis vectors. Each hyperplane represents an estimate of one of the plural reflectance spectra. The processor 310 then determines for each pixel location, a contribution of the plural basis vectors of each hyperplane to the image spectrum of that pixel location. The processor 310 determines or accesses plural hyperplanes and not plural endmembers directly. Hyperplanes are two-dimensional while endmembers are only one-dimensional. As a result, hyperplanes carry more information, such as the illumination spectrum and are therefore of greater use.

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral, multispectral or trichromatic images. In particular, but is not limited to, a method, software and computer for estimating parameters of a reflectance model applied to an image is disclosed. Examples of processing of the images using the estimated parameters includes material recognition, re-colouring and re-shading of objects represented in the image. That is, a computer implemented method is provided of estimating one or more of photogrammetric parameters, ?(u) surface shape N and index of refraction n(u,?) represented in a reflectance image having one or more known illumination directions and a known viewing direction V, the method comprising optimising (802) the difference between the reflectance image and a reflectance model, the reflectance model being based on surface shape N; the material index of refraction n(u,?) and a set of photogrammetric parameters ?(u).

Abstract: The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. Processing of the images includes object recognition and image enhancement in a way that harnesses the information available from the image based on the wavelength indexed spectral data these types of images provide. Illumination spectrum of such an image is estimated (FIG. 21) using a cost function based on a dichromatic reflection model and a constraint term. This method may be performed on sub-images of the image. A method for selecting these sub-images (FIG. 23) is also disclosed. A method of determining photometric parameters of the image given the estimated illumination spectrum (FIG. 22) is also disclosed.

Abstract: The invention concerns the compact representation of a reflectance spectrum of a material. For example, for in compression, identification and comparison of reflectance spectrum data of multiple materials. The compressed representation interpolating a spline curve to the reflectance spectrum data, the spline curve having a set of control points, a knot vector, and representing wavelength and reflectance as functions of an independent parameter (42). Then removing one or more knots from the knot vector that minimise a cost function in a parameter domain of the spline curve based on the wavelength function (44). Aspects of the invention include a method, software, a computer system and the compact representation itself.