| 1. |
- Alsam, Ali, et al.
(författare)
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Calibrating color cameras using metameric blacks
- 2007
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Ingår i: Journal of the Optical Society of America A: Optics and Image Science, and Vision. - 1084-7529. ; 24:1, s. 11-17
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Tidskriftsartikel (refereegranskat)abstract
- Spectral calibration of digital cameras based on the spectral data of commercially available calibration charts is an ill-conditioned problem that has an infinite number of solutions. We introduce a method to estimate the sensor's spectral sensitivity function based on metamers. For a given patch on the calibration chart we construct numerical metamers by computing convex linear combinations of spectra from calibration chips with lower and higher sensor response values. The difference between the measured reflectance spectrum and the numerical metamer lies in the null space of the sensor. For each measured spectrum we use this procedure to compute a collection of color signals that lie in the null space of the sensor. For a collection of such spaces we compute the robust principal components, and we obtain an estimate of the sensor by computing the common null space spanned by these vectors. Our approach has a number of advantages over standard techniques: It is robust to outliers and is not dominated by larger response values, and it offers the ability to evaluate the goodness of the solution where it is possible to show that the solution is optimal, given the data, if the calculated range is one dimensional. © 2006 Optical Society of America.
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| 2. |
- Alsam, Ali, et al.
(författare)
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Calibrating Color Cameras Using Metameric Blacks
- 2006
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Ingår i: European Conference on Color in Graphics, Imaging and Vision,2006. - 7003 Kilworth Lane, Springfield, VA 22151 USA : The Society for Imaging Science and Technology. ; , s. 75-
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Konferensbidrag (refereegranskat)abstract
- Spectral calibration of digital cameras based on the spectral data of commercially available calibration charts is an illconditioned problem which has an infinite number of solutions. To improve upon the estimate, different constraints are commonly employed. Traditionally such constraints include: nonnegativity, smoothness, uni-modality and that the estimated sensors results in as good as possible response fit. In this paper, we introduce a novel method to solve a general ill-conditioned linear system with special focus on the solution of spectral calibration. We introduce a new approach based on metamerism. We observe that the difference between two metamers (spectra that integrate to the same sensor response) is in the null-space of the sensor. These metamers are used to robustly estimate the sensor-s null-space. Based on this nullspace, we derive projection operators to solve for the range of the unknown sensor. Our new approach has a number of advantages over standard techniques: It involves no minimization which means that the solution is robust to outliers and is not dominated by larger response values. It also offers the ability to evaluate the goodness of the solution where it is possible to show that the solution is optimal, given the data, if the calculated range is one dimensional. When comparing the new algorithm with the truncated singular value decomposition and Tikhonov regularization we found that the new method performs slightly better for the training set with noticeable improvements for the test data.
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| 3. |
- Andersson, Thord, et al.
(författare)
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A Fast Optimization Method for Level Set Segmentation
- 2009
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Ingår i: Image Analysis. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642022296 - 9783642022302 ; , s. 400-409
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Konferensbidrag (refereegranskat)abstract
- Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.
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| 4. |
- Andersson, Thord, et al.
(författare)
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Modified Gradient Search for Level Set Based Image Segmentation
- 2013
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Ingår i: IEEE Transactions on Image Processing. - : IEEE Signal Processing Society. - 1057-7149 .- 1941-0042. ; 22:2, s. 621-630
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Tidskriftsartikel (refereegranskat)abstract
- Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.
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| 5. |
- Brun, Anders, 1976-
(författare)
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Manifold learning and representations for image analysis and visualization
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. This coordinate system is in a way a generalization of Cartesian coordinates in Euclidean space. We translate this idea to a cloud of data points in order to perform dimension reduction. Our implementation currently uses Dijkstra's algorithm for shortest paths in graphs and some basic concepts from differential geometry. We expect this approach to open up new possibilities for analysis of e.g. shape in medical imaging and signal processing of manifold-valued signals, where the coordinate system is “learned” from experimental high-dimensional data rather than defined analytically using e.g. models based on Lie-groups.We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps (Belkin and Niyogi, 2002), we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.
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| 6. |
- Bui, Hai Thanh, 1972-, et al.
(författare)
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Color and texture induced structures of keyword space
- 2005
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Ingår i: Proceedings of the 10th Congress of the International Colour Association,2005. - Granada : International Colour Association. ; , s. 1059-
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Konferensbidrag (refereegranskat)
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| 7. |
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| 8. |
- Bui, Hai Thanh, 1972-, et al.
(författare)
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Group theoretical investigations of daylight spectra
- 2004
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Ingår i: 2nd European Conference on Color in Graphics, Imaging and Vision,2004. - Springfield, VA, USA : IST: The Society for Imaging Science and Technology. ; , s. 437-
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Konferensbidrag (refereegranskat)
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| 9. |
- Bui, Hai Thanh, 1972-, et al.
(författare)
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Illumination Induced Changes in Image Statistics
- 2006
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Ingår i: CGIV 2006, Third European Conference on Color in Graphics, Imaging and Vision. - 7003 Kilworth Lane, Springfield, VA 22151 USA : The Society for Imaging Science and Technology. - 0892082623 ; , s. 48-53
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Konferensbidrag (refereegranskat)abstract
- We have previously shown that it is possible to construct a coordinate system in the space of illumination spectra such that the coordinate vectors of the illuminants are located in a cone. Changes in the space of illuminants can then be described by an intensity related scaling and a transformation in the Lorentz group SU(1,1). In practice it is often difficult and expensive to measure these coordinate vectors. Therefore it is of interest to estimate the characteristics of an illuminant from an RGB image captured by a camera. In this paper we will investigate the relation between sequences of illuminants and statistics computed from RGB images of scenes illuminated by these illuminants. As a typical example we will study sequences of black body radiators of varying temperature. We have shown earlier that black body radiators in the mired parametrization can be described by one-parameter groups of the Lorentz group SU(1,1). In this paper we will show that this group theoretical structure of the illuminant space induces a similar structure in spaces of statistical descriptors of the resulting RGB images. We show this relation for mean vectors of RGB images, for RGB histograms and for histograms of images obtained by applying certain spatiospectral linear filters to the RGB images. As a result we obtain estimates of the color temperature of the illuminant from sequences of RGB images of scenes under these illuminants.
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| 10. |
- Carmona, Pedro Latorre, et al.
(författare)
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Performance evaluation of dimensionality reduction techniques for multispectral images
- 2007
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Ingår i: International journal of imaging systems and technology (Print). - : Institutionen för teknik och naturvetenskap. - 0899-9457 .- 1098-1098. ; 17:3, s. 202-217
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Tidskriftsartikel (refereegranskat)abstract
- We consider several collections of multispectral color signals and describe how linear and non-linear methods can be used to investigate their internal structure. We use databases consisting of blackbody radiators, approximated and measured daylight spectra, multispectral images of indoor and outdoor scenes under different illumination conditions and numerically computed color signals. We apply Principal Components Analysis, group-theoretical methods and three manifold learning methods: Laplacian Eigenmaps, ISOMAP and Conformal Component Analysis. Identification of low-dimensional structures in these databases is important for analysis, model building and compression and we compare the results obtained by applying the algorithms to the different databases.
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