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An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

An approach to integrating the global and local kernel-based automated analysis of vocal fold images aiming to categorize laryngeal diseases is presented in this paper. The problem is treated as an image analysis and recognition task. A committee of support vector machines is employed for performing the categorization of vocal fold images into healthy, diffuse and nodular classes. Analysis of image color distribution, Gabor filtering, cooccurrence matrices, analysis of color edges, image segmentation into homogeneous regions from the image color, texture and geometry view point, analysis of the soft membership of the regions in the decision classes, the kernel principal components based feature extraction are the techniques employed for the global and local analysis of laryngeal images. Bearing in mind the high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 vocal fold images is rather encouraging.

This paper proposes the utility of texture and color for iris recognition systems. It contributes for improvement of system accuracy with reduced feature vector size of just 1 × 3 and reduction of false acceptance rate (FAR) and false rejection rate (FRR). It avoids the iris normalization process used traditionally in iris recognition systems. Proposed method is compared with the existing methods. Experimental results indicate that the proposed method using only color achieves 99.9993 accuracy, 0.0160 FAR, and 0.0813 FRR. Computational time efficiency achieved is of 947.7 ms.

Color images depend on the color of the capture illuminant and object reflectance. As such image colors are not stable features for object recognition, however stability is necessary since perceived colors (the colors we see) are illuminant independent and do correlate with object identity. Before the colors in images can be compared, they must first be preprocessed to remove the effect of illumination. Two types of preprocessing have been proposed: first, run a color constancy algorithm or second apply an invariant normalization. In color constancy preprocessing the illuminant color is estimated and then, at a second stage, the image colors are corrected to remove color bias due to illumination. In color invariant normalization image RGBs are redescribed, in an illuminant independent way, relative to the context in which they are seen (e.g. RGBs might be divided by a local RGB average). In theory the color constancy approach is superior since it works in a scene independently: color invariant normalization can be calculated post-color constancy but the converse is not true. However, in practice color invariant normalization usually supports better indexing. In this paper we ask whether color constancy algorithms will ever deliver better indexing than color normalization. The main result of this paper is to demonstrate equivalence between color constancy and color invariant computation.

The equivalence is empirically derived based on color object recognition experiments. colorful objects are imaged under several different colors of light. To remove dependency due to illumination these images are preprocessed using either a perfect color constancy algorithm or the comprehensive color image normalization. In the perfect color constancy algorithm the illuminant is measured rather than estimated. The import of this is that the perfect color constancy algorithm can determine the actual illuminant without error and so bounds the performance of all existing and future algorithms. Post-color constancy or color normalization processing, the color content is used as cue for object recognition. Counter-intuitively perfect color constancy does not support perfect recognition. In comparison the color invariant normalization does deliver near-perfect recognition. That the color constancy approach fails implies that the scene effective illuminant is different from the measured illuminant. This explanation has merit since it is well known that color constancy is more difficult in the presence of physical processes such as fluorescence and mutual illumination. Thus, in a second experiment, image colors are corrected based on a scene dependent "effective illuminant". Here, color constancy preprocessing facilitates near-perfect recognition. Of course, if the effective light is scene dependent then optimal color constancy processing is also scene dependent and so, is equally a color invariant normalization.

The aims of this study were to investigate a novel strategy for drying longan without stone viz. the two-stage drying using a superheated steam dryer followed by a hot air dryer (SSD/HAD), both from heat/mass transfer and product quality points of view. The experiments were performed in the two different stages of SSD/HAD using the superheated steam temperatures of 120-180°C in the first-stage and then the drying air temperatures of 60-70°C in the second-stage. The moisture content of longan at the end of the superheated steam drying stage was 200% dry basis. The effects of drying medium temperatures and the intermediate moisture content of dried longan after SSD/HAD on the drying kinetics and quality of dried longan were investigated. The quality of longan was evaluated in terms of its color characteristics and shrinkage.

The objective of the present study was to investigate the effects of various parameters, i.e., concentration of salt solution (2, 3, 4% (w/v)), boiling time (3, 5, 7 minutes) and drying air temperature (80, 100, 120°C) on the kinetics of drying and various quality attributes of shrimp, namely, color, texture, shrinkage and rehydration ability, during drying in a jet-spouted bed dryer. Small shrimp (350-360 shrimp/kg) was boiled and then dried until its moisture content was around 25% (d.b.). It was found that the color changes, toughness and shrinkage of shrimp increased while the rehydration ability decreased with an increase in the concentration of salt solution and boiling time.

The application of color in information graphics is important and useful. When compared with text, color not only brings richly visual enjoyment to readers, but also transfers a great quantity of information quickly and effectively. In this paper, we integrate the pattern of information transmission in nature, which is color union and color comparison, into our analysis. This pattern has been used to illustrate the importance and effect of color in information graphics. Concrete examples with special color are used to show the efficiency of information transmission through color.