TESTING COLOR APPEARANCE MODELS IN COMPLEX SCENE

The sensation of sight is our primary mechanism to perceive the world around us. However it is not yet perfectly clear how the human visual system works. The images of the world are formed on the retina, captured by sensors and converted in signals sent to the brain. Here the signals are processed and somehow interpreted, thus we are able to see. A lot of information, hypothesis, hints come from a field of the optical (or visual) illusions. These illusions have led many scientists and researchers to ask themselves why we are not able to interpret in a correct way some particular scenes. The word \u201cinterpret\u201d underlines the fact that the brain, and not only the eye, is involved in the process of vision. If our sight worked as a measurement tool, similar to a spectrophotometer, we would not perceive, for example, the simultaneous contrast phenomenon, in which a grey patch placed on a black background appears lighter than an identical coloured patch on a white background. So, why do we perceive the patches as different, while the light that reaches the eyes is the same? In the same way we would not be able to distinguish a white paper seen in a room lit with a red light from a red paper seen under a white light, however humans can do this. These phenomena are called colour appearance phenomena. Simulating the appearance is the objective of a range of computational models called colour appearance models. In this dissertation themes about colour appearance models are addressed. Specific experiments, performed by human observers, aim to evaluate and measure the appearance. Different algorithms are tested in order to compare the results of the computational model with the human sensations about colours. From these data, a new printing pipeline is developed, able to simulate the appearance of advertising billboard in different context

Blood flow velocity in monocular retinoblastoma assessed by color doppler

OBJECTIVE: To analyze the flow of retrobulbar vessels in retinoblastoma by color Doppler imaging. METHODS: A prospective study of monocular retinoblastoma treated by enucleation between 2010 and 2014. The examination comprised fundoscopy, magnetic resonance imaging, ultrasonography and color Doppler imaging. The peak blood velocities in the central retinal artery and central retinal vein of tumor-containing eyes (tuCRAv and tuCRVv, respectively) were assessed. The velocities were compared with those for normal eyes (nlCRAv and nlCRVv) and correlated with clinical and pathological findings. Tumor dimensions in the pathological sections were compared with those in magnetic resonance imaging and ultrasonography and were correlated with tuCRAv and tuCRVv. In tumor-containing eyes, the resistivity index in the central retinal artery and the pulse index in the central retinal vein were studied in relation to all variables. RESULTS: Eighteen patients were included. Comparisons between tuCRAv and nlCRAv and between tuCRVv and nlCRVv revealed higher velocities in tumor-containing eyes (

Milano Retinex family

Several different implementations of the Retinex model have been derived from the original Land and McCann's paper. This paper aims at presenting the Milano-Retinex family, a collection of slightly different Retinex implementations, developed by the Department of Computer Science of Universit\ue1 degli Studi di Milano. One important difference is in their goals: while the original Retinex aims at modeling vision, the Milano-Retinex family is mainly applied as an image enhancer, mimicking some mechanisms of the human vision system

Linking organic matter chemistry with soil aggregate stability: Insight from 13C NMR spectroscopy

Soil aggregation is considered as a crucial process in agro-system sustainability due to the role in soil physical, chemical and biological dynamics. Here we tested the hypothesis that the initial chemical traits of organic matter (OM) may help to explain the variability of soil aggregation dynamics after organic amendment. We characterized ten OM types (alfalfa litter, biochar, cellulose, glucose, green compost, maize litter, manure compost, meat powder, sawdust, and solid digestate) by 13C-CPMAS NMR and elemental chemical features to investigate the effects of amendment quality on soil aggregation. In a manipulative factorial experiment, dry samples (200 g) of three soil types (S1, S2 and S3) with different texture, high pH (7\u20139), and similar OM content, were incorporated with 4 g (2% w/w) of dry, 2 mm-grounded OM, incubated in mesocosms for 300 days under controlled temperature (18 \ub1 2 \ub0C night and 24 \ub1 2 \ub0C day), and sampled at 4 dates for measuring aggregation index (AI), based on water stability of soil aggregates (WSA). We found that meat powder and alfalfa litter induced a rapid initial increase of AI, exceeding that of the controls by one to two orders of magnitude, likely acting as a C source for microbes. Biochar incorporation in soil barely affected AI, with intermediate effects with other OM types. Considering C bond types corresponding to OM 13C-CPMAS NMR spectral regions, carbonyl C was only correlated to early AI, possibly due to overlapping signals of amide structures; O-alkyl C and di-O-alkyl C (carbohydrate fraction) were positively associated to AI, indicating a promoting effect on soil structure, while aromatic C fractions showed an opposite pattern, possibly related to aggregate protection by coatings associated to water repellency, or to direct aggregate internal binding. This study demonstrates that OM chemical quality plays an important role in soil aggregation process, with the molecular composition defined by 13C-CPMAS NMR spectroscopy being more predictive of aggregation dynamics compared to classical elemental features. As such, this study provides a significant novel contribution to clarify the relationships between OM chemistry and soil aggregation

Linking organic matter chemistry with soil aggregate stability: Insight from 13C NMR spectroscopy

Soil aggregation is considered as a crucial process in agro-system sustainability due to the role in soil physical, chemical and biological dynamics. Here we tested the hypothesis that the initial chemical traits of organic matter (OM) may help to explain the variability of soil aggregation dynamics after organic amendment. We characterized ten OM types (alfalfa litter, biochar, cellulose, glucose, green compost, maize litter, manure compost, meat powder, sawdust, and solid digestate) by 13C-CPMAS NMR and elemental chemical features to investigate the effects of amendment quality on soil aggregation. In a manipulative factorial experiment, dry samples (200 g) of three soil types (S1, S2 and S3) with different texture, high pH (7\u20139), and similar OM content, were incorporated with 4 g (2% w/w) of dry, 2 mm-grounded OM, incubated in mesocosms for 300 days under controlled temperature (18 \ub1 2 \ub0C night and 24 \ub1 2 \ub0C day), and sampled at 4 dates for measuring aggregation index (AI), based on water stability of soil aggregates (WSA). We found that meat powder and alfalfa litter induced a rapid initial increase of AI, exceeding that of the controls by one to two orders of magnitude, likely acting as a C source for microbes. Biochar incorporation in soil barely affected AI, with intermediate effects with other OM types. Considering C bond types corresponding to OM 13C-CPMAS NMR spectral regions, carbonyl C was only correlated to early AI, possibly due to overlapping signals of amide structures; O-alkyl C and di-O-alkyl C (carbohydrate fraction) were positively associated to AI, indicating a promoting effect on soil structure, while aromatic C fractions showed an opposite pattern, possibly related to aggregate protection by coatings associated to water repellency, or to direct aggregate internal binding. This study demonstrates that OM chemical quality plays an important role in soil aggregation process, with the molecular composition defined by 13C-CPMAS NMR spectroscopy being more predictive of aggregation dynamics compared to classical elemental features. As such, this study provides a significant novel contribution to clarify the relationships between OM chemistry and soil aggregation

A novel approach to visual rendering of astro-photographs

When we perform a visual analysis of a cosmic object photograph the contrast plays a fundamental role. A linear distribution of the observable values is not necessarily the best possible for the Human Visual System (HVS). In fact HVS has a non-linear response, and exploits contrast locally with different stretching for different lightness areas. As a consequence, according to the observation task, local contrast can be adjusted to make easier the detection of relevant information. The proposed approach is based on Spatial Color Algorithms (SCA) that mimic the HVS behavior. These algorithms compute each pixel value by a spatial comparison with all (or a subset of) the other pixels of the image. The comparison can be implemented as a weighted difference or as a ratio product over given sampling in the neighbor region. A final mapping allows exploiting all the available dynamic range. In the case of color images SCA process separately the three chromatic channels producing an effect of color normalization, without introducing channel cross correlation. We will present very promising results on amateur photographs of deep sky objects. The results are presented for a qualitative and subjective visual evaluation and for a quantitative evaluation through image quality measures, in particular to quantify the effect of algorithms on the noise. Moreover our results help to better characterize contrast measures

Open issues in the study of human retina

This paper reports a concise survey on some characteristics of the human retina. The goal is to present some of the known parameters together with some open issues that still do not fit in the actual models of human vision. Colour and vision in general are still far from being well known mechanisms

Fuzzy color image segmentation using watershed transform

In this paper, we present a segmentation technique which joins a multi-scale texture based approach and a fuzzy segmentation method using the color gradient of Di Zenzo. Thus maps of homogeneous texture patterns are enhanced by edge localization. A topographic distance is used to calculate membership degrees to each region. Both lead to segmentation using a step of watershed flooding, with few parameters to set. Moreover computation complexity has been reduced to allow treatments of images in full size and colors