Identification and mapping of amplified fragment length polymorphism markers linked to shell color in bay scallop, Argopecten irradians irradians (Lamarck, 1819)

Amplified fragment length polymorphisms (AFLP) were used to study the inheritance of shell color in Argopecten irradians. Two scallops, one with orange and the other with white shells, were used as parents to produce four F-1 families by selfing and outcrossing. Eighty-eight progeny, 37 orange and 51 white, were randomly selected from one of the families for segregation and mapping analysis with AFLP and microsatellite markers. Twenty-five AFLP primer pairs were screened, yielding 1138 fragments, among which 148 (13.0%) were polymorphic in two parents and segregated in progeny. Six AFLP markers showed significant (P < 0.05) association with shell color. All six loci were mapped to one linkage group. One of the markers, F1f335, is completely linked to the gene for orange shell, which we designated as Orange1, without any recombination in the progeny we sampled. The marker was amplified in the orange parent and all orange progeny, but absent in the white parent and all the white progeny. The close linkage between F1f335 and Orange1 was validated using bulk segregation analysis in two natural populations, and all our data indicate that F1f335 is specific for the shell color gene, Orange1. The genomic mapping of a shell color gene in bay scallop improves our understanding of shell color inheritance and may contribute to the breeding of molluscs with desired shell colors.

Estimating the Illuminant Color from the Shading of a Smooth Surface

\0\05{\0\0\0\0\0\0\0\0 a uniform wall illuminated by a spot light often gives a strong impression of the illuminant color. How can it be possible to know if it is a white wall illuminated by yellow light or a yellow wall illuminated by white light? If the wall is a Lambertian reflector, it would not be possible to tell the difference. However, in the real world, some amount of specular reflection is almost always present. In this memo, it is shown that the computation is possible in most practical cases.

Using hybrid knowledge engineering and image processing in color virtual restoration of ancient murals

IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 5, pp. 1338-1343, 2003.

Carta de color.

26 hojas : fotografías a color.

Carta de color y manual de tintorería para la palma estera.

47 hojas : ilustraciones, fotografías a color, muestras.

Diversificación del color : mejoramiento de la materia prima.

13 hojas.

Productos funcionales y artísticos elaborados con enchapados en tamo al natural y a color mostrando materiales y fases del proceso.

19 hojas : ilustraciones, fotografías a color

Monturas de cuero recubiertas con pellones y gualdrapas tejidas con hilos de color, de los indígenas Wayúu Guajira.

11 hojas : ilustraciones, fotografías a color.

Muestras de productos funcionales y decorativos elaboraos en cerámica esmaltada a color.

27 hojas : ilustraciones, fotografías

Skin Color-Based Video Segmentation under Time-Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24% is observed in 17 out of 21 test sequences. In all but one case the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.

Color Region Grouping and Shape Recognition with Deformable Models

A new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.

Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.