A Multi-scale colour and Keypoint Density-based Approach for Visual Saliency Detection.

In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces

Colour displays for categorical images

We propose a method for identifying a set of colours for displaying 2-D and 3-D categorical images when the categories are unordered labels. The principle is to find maximally distinct sets of colours. We either generate colours sequentially, to maximise the dissimilarity or distance between a new colour and the set of colours already chosen, or use a simulated annealing algorithm to find a set of colours of specified size. In both cases, we use a Euclidean metric on the perceptual colour space, CIE-LAB, to specify distances

Quantification of metamerism and colour constancy

Reliable colour constancy by industry for colour conducted to quantify metamerism. and metamerism indices are highly desired quality control. Two experiments were the degree of colour constancy and In the colour constancy experiment, 240 wool samples were prepared and scaled using a magnitude estimation method by a panel of 5 experienced observers under sources D65, A and TL84. 2 corresponding data sets derived from the experimental results were used to test various chromatic adaptation transforms. The results clearly show that the BFD transform gave the most precise prediction than the other transforms. Attempts were also made to derive 4 new transforms from four independent data sets. These gave similar performance as that of the BFD, but overcome the BFO's problem (incapable of predicting some of the high saturated colours). Hence, these transforms should be used with confidence for predicting the degree of colour constancy. This experimental results were also used to test various uniform colour spaces and colour appearance models. The Hunt94 model gave the most precise prediction to the colourfulness and hue results. Modification was made to its lightness scale for improving the fit. In the metamerism experiment, 76 pairs of wool samples were prepared and assessed with 20 observations using a grey scale under 7 sources: D65, A, TL84, TL83, P27, W and WW. The experimental results were used to test 3 types of illuminant metamerism indices derived here. It was found that calculating colour difference using 3 colour difference formulae, i.e. CMC, BFD and CIE94 gave the most precise prediction to the visual results. The degree of precision is quite satisfactory in comparison with typical observer precision. A new standard deviate observer (SDO) was also derived. This together with the CIE SDO and 1964 Observer were tested using the author's and the Obande's data. The results showed that the new SDO predicted results more accurate than those from the other two CIE Observers. An Observer Metamerism Index (OMI) was also derived to indicate the degree of metamerism based upon the new SDO. The results showed that the new SDO was more suitable for indicating the degree of observer metamerism

Optimal learning spaces: design implications for primary schools

Review guide of the design evidence for primary school

A Fuzzy Approach to Text Segmentation in Web Images Based on Human Colour Perception

This chapter describes a new approach for the segmentation of text in images on Web pages. In the same spirit as the authors’ previous work on this subject, this approach attempts to model the ability of humans to differentiate between colours. In this case, pixels of similar colour are first grouped using a colour distance defined in a perceptually uniform colour space (as opposed to the commonly used RGB). The resulting colour connected components are then grouped to form larger (character-like) regions with the aid of a propinquity measure, which is the output of a fuzzy inference system. This measure expresses the likelihood for merging two components based on two features. The first feature is the colour distance between the components, in the L*a*b* colour space. The second feature expresses the topological relationship of two components. The results of the method indicate a better performance than previous methods devised by the authors and possibly better (a direct comparison is not really possible due to the differences in application domain characteristics between this and previous methods) performance to other existing methods

Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites

Facial composite construction is one of the most successful applications of interactive evolutionary computation. In spite of this, previous work in the area of composite construction has not investigated the algorithm design options in detail. We address this issue with four experiments. In the first experiment a sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second experiment the performances of two mutation and two recombination operators for interactive genetic algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive genetic algorithm to interactive differential evolution. Our results show that the facial composite construction process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human face perception and differences between the participants in how they interact with the algorithms. Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora. V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani 12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo. Rezultati kažejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma. To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom

Two Approaches for Text Segmentation in Web Images

There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characters with an anthropocentric perspective of colour perception— in preference to RGB space analysis. Both approaches enable the extraction of text in complex situations such as in the presence of varying colour and texture (characters and background)