J. M. Geusebroek, A. W. M. Smeulders, and R. van den Boomgaard. Measurement of color invariants. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 50--57. IEEE Computer Society, 2000. Home/Search Document Details and Download
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Feature Histograms for Content-Based Image Retrieval - Siggelkow (2002) (3 citations) (Correct)
....Another important research topic in the field of color is color normalization: An object s color varies considerably due to changes in the object s position and or illumination variations. There exist several algorithms that calculate a representation which is invariant to one or both variations [35, 38, 31, 42, 40]. Such techniques have also been successfully applied to image retrieval [75, 39] Texture: There is a wide variety of features used for texture analysis. 33] uses coarseness, contrast and directionality. In [70] these were enhanced by cooccurrence matrix features and the wold model, which ....
....sufficient. Often series are comprised of stamps with identical motive but different printing colors. We want to stress that the color variation does not result from different illumination only. Therefore we cannot use common color constancy algorithms or illumination invariant color spaces like [35, 38, 32, 40]. To cope with different printing colors we have to extract features that do not depend on the absolute color value. We give three different solutions: Use of gradient orientation based feature histograms These features have been presented in Section 5.3.3. They do not consider absolute ....
J.-M. Geusebroek and A. W. M. Smeulders. Measurement of color invariants. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 50--57, Hilton Head Island, SC, June 2000.
A Physical Basis for Color Constancy - Geusebroek, van den Boomgaard.. (2002) (2 citations) Self-citation (Geusebroek Smeulders Van den boomgaard) (Correct)
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J. M. Geusebroek, A. W. M. Smeulders, and R. van den Boomgaard. Measurement of color invariants. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 50--57. IEEE Computer Society, 2000.
A Physical Basis for Color Constancy - Jan-Mark Geusebroek Rein (2002) (2 citations) Self-citation (Geusebroek Smeulders Van den boomgaard) (Correct)
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J. M. Geusebroek, A. W. M. Smeulders, and R. van den Boomgaard. Measurement of color invariants. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 50--57. IEEE Computer Society, 2000.
The Kubelka-Munk Theory for Color Image Invariant Properties - Geusebroek, Gevers.. (2002) Self-citation (Geusebroek Smeulders) (Correct)
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J. M. Geusebroek, A. W. M. Smeulders, and R. van den Boomgaard. Measurement of color invariants. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 50--57. IEEE Computer Society, 2000.
Color Invariance - Geusebroek, van den Boomgaard.. (2001) (1 citation) Self-citation (Geusebroek Smeulders Van den boomgaard) (Correct)
....namely overall illumination intensity or camera gain. Discrimination degraded for set and , invariant for shading effects and illumination color. Set r, invariant for shadows and highlights, has lowest discriminative power. Illumination and viewing direction invariance is evaluated in [31] by experiments on a collection of real world surfaces. Discriminating power is increased when considering a larger spatial scale Hence, a larger spatial scale results in a more accurate estimate of color at the point of interest, increasing the accuracy of the result. The aim of the paper is ....
J.M. Geusebroek, A.W.M. Smeulders, and R. van den Boomgaard, "Measurement of Color Invariants," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 50-57, 2000.
Color Differential Structure - Romeny, Geusebroek, Van Osta, van.. (2001) Self-citation (Geusebroek Van den boomgaard) (Correct)
....that the illumination l( is independent of position. Hence the equation describes spectral image formation of matte objects, illuminated by a single light source. For shiny surfaces the image formation equation has to be extended with an additive term describing the Fresnel re ected light, see [3] for more details. The structure of the spatio spectral energy distribution is due to the three functions c, l, and m. By making some general assumptions, these quantities may be derived from the measured image. Estimation of the object re ectance function c boils down to deriving material ....
Geusebroek, J.M., Smeulders A.W.M., and van den Boomgaard, R., Measurement of Color Invariants. Proc. CVPR, vol. 1, pp. 50|57, June 13-15, 2000.
Invariant Representation In Image Processing - Smeulders, Geusebroek, Gevers (2001) Self-citation (Geusebroek Smeulders) (Correct)
....in the inner scene. H=N Implies the classification of cover type by combination of results. invariant for source = 1 = 3 intensity direction patch orient specular E 983 1000 W 978 1000 C 820 970 N 757 974 H 461 462 Table 3. From [15]. The trade off between tightness of the invariance and discriminatory power by showing the number of color patches from 1000 which still can be discriminated. denotes spatial scale of the filter. Note that invariance for surface orientation implies invariance for viewing direction and ....
....a feature with a very wide class of invariance looses the power to discriminate among essential differences. The aim is to select the tightest set of invariants. What is needed is a complete sets of image properties with well described variant conditions that they are capable of handling, see [15] and the table 4. This paper was aimed at a further step towards the description of information available from the scene. 7. ....
J. M. Geusebroek, A. W. M. Smeulders, and R. van den Boomgaard, "Measurement of color invariants, " in CVPR. 2000, vol. 1, pp. 50--57, IEEE Press.
Image Retrieval and Segmentation based on Color.. - Geusebroek, Koelma.. (2000) (2 citations) Self-citation (Geusebroek Smeulders) (Correct)
....B values, e.g. 2, 6, 9] The fundamentals of image processing tells us that the value of a particular pixel is not a meaningful entity. Combining spatial and color information in the Gaussian scale space paradigm [4] results in robust and more accurate estimation of color value. Our paper on CVPR [3] exploits the Gaussian color model to measure invariant properties from color images. We demonstrate the usefulness of the proposed color invariants in segmentation of images independent of shadow and highlights. Our PicToVision system provides segmentation of images through the world wide web. ....
....is sent to the server. The server converts the image to the desired format, enabling the image processing routines to process the image. The result will be send back to the client for display. The interface between client (Java) and server (C) is written in Java. Further, the color invariants [3] have been implemented in C. The server runs on a Ultra 10 Sun station at 300 Mhz. Summarizing, the Java Applet al..lows the user to select load an (external) image, select appropriate invariances, and send the image to the server. Then, the image is processed by the server, and the result is send ....
J. M. Geusebroek, A. W. M. Smeulders, and R. van den Boomgaard. Measurement of color invariants. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, june 13-15, 2000.
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