Narrow Results

The use of color in computer vision has received growing attention. This chapter gives the state-of-the-art in this subfield, and tries to answer the questions: What is color? Which are the adequate representations? How is it computed? What can be done using it?

The first section introduces some basic tools and models that can be used to describe the color imaging process. We first summarize the classical photometric and colorimetric notions: light measurement, intensity equation, color signal, color perception, trichromatic theory. The growing interest in color during the last few years comes from two new classes of models of reflection, physical models and linear models, which lead to highlight algorithms as well as color constancy algorithms. We present these models in detail and discuss some of their limitations.

The second section deals with the problem of color constancy. The term “color constancy” refers to the fact that the colors perceived by humans in real scenes are relatively stable under large variations of illumination and of material composition of scenes. From a computational standpoint, achieving color constancy is an underdetermined problem: computing the spectral reflectance from the sensor measurements. We compare three classes of color constancy algorithms, based on lightness computation, linear models, and physical models, respectively. For each class, the principle is explained, and one or two significant algorithms are given. A comparative study serves to introduce the others.

The third section is concerned with the use of color in universal, i.e. mainly low-level, vision tasks. We emphasize the distinction between tasks that have been extensively studied in monochromatic images and for which the contribution of color is just a quantitative generalization, and tasks where color has a qualitative role. In the first case, additional image features are obtained, and have to be represented and used efficiently. In the latter case, it is hoped that color can help recover intrinsic physical properties of scenes. We study successively three important themes in computer vision: edges, segmentation, matching. For each of them, we present the two frameworks for the use of color.

Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants’ memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.

The use of color in computer vision has received growing attention. This chapter introduces the basic principles underlying the physics and perception of color and reviews the state-of-the-art in color vision algorithms. Parts of this chapter have been condensed from [58] while new material has been included which provides a critical review of recent work. In particular, research in the areas of color constancy and color segmentation is reviewed in detail.

The first section reviews physical models for color image formation as well as models for human color perception. Reflection models characterize the relationship between a surface, the illumination environment, and the resulting color image. Physically motivated linear models are used to approximate functions of wavelength using a small number of parameters. Reflection models and linear models are introduced in Section 1 and play an important role in several of the color constancy and color segmentation algorithms presented in Sections 2 and 3. For completeness, we also present a concise summary of the trichromatic theory which models human color perception. A discussion is given of color matching experiments and the CIE color representation system. These models are important for a wide range of applications including the consistent representation of color on different devices. Section 1 concludes with a description of the most widely used color spaces and their properties.

The second section considers progress on computational approaches to color constancy. Human vision exhibits color constancy as the ability to perceive stable surface colors for a fixed object under a wide range of illumination conditions and scene configurations. A similar ability is required if computer vision systems are to recognize objects in uncontrolled environments. We begin by reviewing the properties and limitations of the early retinex approach to color constancy. We describe in detail the families of linear model algorithms and highlight algorithms which followed. Section 2 concludes with a subsection on recent indexing methods which integrate color constancy with the higher level recognition process.

Section 3 addresses the use of color for image segmentation and stresses the role of image models. We start by presenting classical statistical approaches to segmentation which have been generalized to include color. The more recent emphasis on the use of physical models for segmentation has led to new classes of algorithms which enable the accurate segmentation of effects such as shadows, highlights, shading, and interreflection. Such effects are often a source of error for algorithms based on classical statistical models. Finally, we describe a color texture model which has been used successfully as the basis of an algorithm for segmenting images of natural outdoor scenes.

Analysis of eye movement due to different visual stimuli always has been one of the major research areas in vision science. An important category of works belongs to decoding of eye movement due to variations of color of visual stimuli. In this research, for the first time, we employ fractal analysis in order to investigate the variations of complex structure of eye movement time series in response to variations of color of visual stimuli. For this purpose, we applied two different images in three different colors (red, green, blue) to subjects. The result of our analysis showed that eye movement has the greatest complexity in case of green visual stimulus. On the other hand, the lowest complexity of eye movement was observed in case of red stimulus. In addition, the results showed that except for red visual stimulus, applying the visual stimulus with greater complexity causes the lower complexity in eye movements. The employed methodology in this research can be further applied to analyze the influence of other variations of visual stimuli on human eye movement.

In this paper, we present two steps in the process of automatic annotation in archeological images. These steps are feature extraction and feature selection. We focus our research on archeological images which are very much studied in our days. It presents the most important steps in the process of automatic annotation in an image. Feature extraction techniques are applied to get the feature that will be used in classifying and recognizing the images. Also, the selection of characteristics reduces the number of unattractive characteristics. However, we reviewed various images of feature extraction techniques to analyze the archaeological images. Each feature represents one or more feature descriptors in the archeological images. We focus on the descriptor shape of the archaeological objects extraction in the images using contour method-based shape recognition of the monuments. So, the feature selection stage serves to acquire the most interesting characteristics to improve the accuracy of the classification. In the feature selection section, we present a comparative study between feature selection techniques. Then we give our proposal of application of methods of selection of the characteristics of the archaeological images. Finally, we calculate the performance of two steps already mentioned: the extraction of characteristics and the selection of characteristics.

Paintings convey the composition and characteristics of artists; therefore, it is possible to feel the intended style of painting and emotion of each artist through their paintings. In general, basic elements that constitute traditional paintings are color, texture, and composition (formative elements constituting the paintings are color and shape); however, color is the most crucial element expressing the emotion of a painting. In particular, traditional colors manifest the color containing historicity of the era, so the color shown in painting images is considered a representative color of the culture to which the painting belongs. This study constructed a color emotional system by analyzing colors and rearranged color emotion adjectives based on color combination techniques and clustering algorithm proposed by Kobayashi as well as I.R.I HUE & TONE 120 System. Based on the embodied color emotion system, this study confirmed classified emotions of images by extracting and classifying emotions from traditional Korean painted images, focusing on traditional painted images of the late Joseon Dynasty. Moreover, it was possible to verify the cultural traits of the era through the classified emotion images.

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 research work has focused on detection and prediction of melanoma which is done by subjecting to features extraction, where the features of an image consisting of melanoma regions are detected by analysis and this analysis is done by considering the features like color and texture-based features learning strategy. These features are extracted by combining color and texture-based features extraction with deep convolutional features representation learning strategy. The colors of images are extracted by representing the colors of different channels into red, green and blue channel information. The combination of texture features extraction with color-based features extraction in addition to Alex net features extraction learning has made the system more robust and efficient toward the segmentation and classification of images. Further, the erected method involves convoluting the features of extracted information with color and texture-based method which has led our system to full convolution neural networks with images features extraction. The melanoma is detected and segmented with watershed segmentation, these segmented features are subjected to the proposed features extraction method, where the features are extracted by combining the methods of texture with color-based information. These colors are made available to the proposed method by analyzing the regions of melanoma images. The erected method does the task of features extraction by Weber law descriptors in combination with red, green, blue channels information extracted from features representation learning. The proposed method has yielded an accuracy of 94.12% of segmentation accuracy and a classification accuracy of 94.32% with respect to various other classification techniques.

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.