The development of a spectral difference-based statistical processing of hyperspectral images is provided in this article. Kullback-Leibler pseudo-divergence function, which was specifically developed for the metrological processing of hyperspectral images, is used at the foundation of the statistics. As a demonstration of its use, the proposed statistics are used in visualising surface variability within a set of pigment patches. It is then further exploited to detect anomalies and deterioration that occur on the patches.
Texture discrimination was studied a lot for texture classification/recognition in image databases, but less under the metrological point of view. In this work, we focused on the metrological behaviour related to the human vision for Control Quality purposes. Inside this study, we introduce as a pair a novel texture feature associated to an adapted similarity measure. The main idea was to define a compact representation adapted from the human visual characteristics in order to obtain an accurate description of the texture. Combined to an adapted similarity measure, the obtained pair feature/similarity becomes highly efficient. Performance Classification of the proposed texture feature is assessed on six popular and challenging databases used to provide the reference results in the state-of-the-art. Obtained results show the efficiency and the robustness of the proposed pair feature/similarity measure defined by the relocated Colour Contrast Occurrence Matrix.
We report on some recent advances in industrial color-difference evaluation focused in three main fields: Development of reliable experimental visual datasets; proposal of new color spaces and color-difference formulas; tools to evaluate the merits of color-difference formulas. The use of fuzzy techniques to assign consistency degrees to color pairs in combined visual datasets is described. The CIE/ISO joint proposal of the CIEDE2000 color-difference formula as a standard will facilitate the communication among companies and users. The CIE recommendation of the STRESS index to assess observers’ variability and relative merits of different color-difference formulas is reported. Power functions are an efficient method to improve the performance of modern color-difference formulas. We need of advanced color-difference formulas accounting for new materials with different kind of textures and gonioapparent effects.
Texture discrimination was the second more important task studied after colour perception and characterization. Nevertheless, colour texture assessment and characterization was few studied and no vector processing was proposed to assess this important visual information. In this work we show the construction of a new vector that integrates fully the information of texture and color. This vector is based on Julesz psico-physics conjectures and the Haralick cooccurrence matrix. A colour texture image in the CIEL*a* b* colour space is characterizing in a 3D matrix, from which it is possible to visually some variations in chromaticity. The performance of this vector had evaluated in tasks of classification in front of other developments that mix the texture and colour information. The colour contrast occurrence matrix (C2O) has the best classification rates in three of the four image database evaluated as OUTEX, VISTEX, STEX and ALOT. C2O texture classification was evaluated in front of co-occurrence matrix (GLMC), run-length matrix (RLM) and local binary patterns (LBP) approaches.
Mathematical morphology is a powerful tool for filtering, segmentation and texture analysis, extended to multivariate
signal in the last years. The major limitations of applying it to colour image reside in the non-linear behavior of the
Human Visual System to the perception of colour. So a direct extension of the multivariate approach to colour image is not
appropriate and the existing approaches can not offer generic solutions from a perceptual point of view. To overcome this
limit, we present a coherent solution for the addition/subtraction parts of the colour dilatation/erosion specification, which
didn't limit the structural element to the flat ones. By combination of two perceptual colour spaces, we define a partial
order, specified by a perceptual colour distance. By this way, we solve lot of problems induced by all methods based on
bit mixing or lexicographical strategies. In addition, we define unic supremum and infimum in the colour space allowing
classical developments for filtering or segmentation and colour texture analysis without colour artefacts.
In this paper, we will discuss about the colour spaces and their specificities, then we present the possible colour ordering
schemes for mathematical morphology. In a second time, we develop our specific approach, beginning by the discussion
about an adapted colour space, following with the extrema extraction formulation in this adapted colour space, by distance
computation. Then we propose the colour addition expression needed in the complete formulation of supremum and
infimum. Finally, we show the first results in colour textured image filtering and we conclude with perspectives.
The automatic prediction of perceived quality from image data in
general, and the assessment of particular image characteristics or
attributes that may need improvement in particular, becomes an
increasingly important part of intelligent imaging systems. The
purpose of this paper is to propose to the color imaging community in
general to develop a software package available on internet to help
the user to select among all these approaches which is better
appropriated to a given application. The ultimate goal of this project
is to propose, next to implement, an open and unified color imaging
system to set up a favourable context for the evaluation and analysis
of color imaging processes. Many different methods for measuring the performance of a process have been proposed by different researchers. In this paper, we will discuss the advantages and shortcomings of most of main analysis criteria and performance measures currently used. The aim is not to establish a harsh competition between algorithms or processes, but rather to test and compare the efficiency of methodologies firstly to highlight strengths and weaknesses of a given algorithm or methodology on a given image type and secondly to have these results publicly available. This paper is focused on two important unsolved problems. Why it is so difficult to select a color space which gives better results than another one? Why it is so difficult to select an image quality metric which gives better results than another one, with respect to the judgment of the Human Visual System? Several methods used either in color imaging or in image quality will be thus discussed. Proposals for content-based image measures and means of developing a standard test suite for will be then presented. The above reference advocates for an evaluation protocol based on an automated procedure. This is the ultimate goal of our proposal.
This paper aims to present a complete methodology based on a multidisciplinary approach, that combines the extraction of low-level features to describe images in a high-level concept or formalism dedicated to Computer-Aided Categorization of ornamental stones (granite, marble). The problem is resolved thanks to a Content-Based Image Retrieval scheme where each image from the ornamental database is represented by a features vector. This last is composed, on one hand, by a color feature corresponding to a novel characterization of color histogram and on the other hand by a texture feature corresponding to a color-based co-occurrence matrix from where we extract some feature representation. The combination of both color texture descriptors is done thanks to a stage of expert know-how extraction. This know-how is represented by the way of weighting factors and confidence degrees. The fusion of the whole data allows to improve the categorization performances.
This paper describes a preliminary study aimed at improving the quality of soft-blue veined cheeses by the use of magnetic resonance images analysis. MRI measurements were performed on thirty-two samples from two different processing conditions and at three different stages from day 3 after the production to day 37. A segmentation algorithm based on a Self Organizing Map was used to segment the images into six classes. A cavity extraction was then performed. A principal component analysis was computed on variables corresponding to the cavities surface distribution. The results pointed out differences between the two types of cheese, particularly for day 3 and day 37. This confirmed the interest to use MRI to analyze such products. Further investigations are planned for the analysis of other characteristics of the cheeses and other methods of segmentation.
This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very different from one to each other and an attribute that is powerful for a database category may be very powerless for the other categories. The idea is to use very simple features, such as color histogram, correlograms, Color Coherence Vectors (CCV), to fill out the signature vector. Then, a number of mixture vectors is prepared depending on the number of very distinctive categories in the database. Knowing that a mixture vector is a vector containing the weight of each attribute that will be used to compute a similarity distance. We post a query in the database using successively all the mixture vectors defined previously. We retain then the N first images for each vector in order to make a mapping using the following information: Is image I present in several mixture vectors results? What is its rank in the results? These informations allow us to switch the system on an unsupervised relevance feedback or user's feedback (supervised feedback).
This paper deals with Computer Aided Diagnosis for skin cancers (melanomas). The diagnosis is based on some rules called the ABCD mnemonics. They take into account color distribution, lesion's diameter, etc. The goal isn't to classify the lesion but to find those, which are the most similar, in order to help the expert to confirm his diagnosis and to avoid any useless excision. This is done thanks to an indexation system, which compare the signatures of previously diagnosed lesions contained in a database and patient's lesion signature. This last is constructed by translating the rules into image processing attributes. We have divided then into three families: Color attributes (color and fuzzy histograms), Texture attributes (co-occurrence matrix and Haralick indices) and Shape attributes (lesion surface and maximum included circle). Image quantization permits us to keep only the most significant colors, thus giving a light structure. Finally, we define a distance for each attribute and use weighted combination for the similarity measure.
Most indexing systems are based on global image descriptors. Nowadays, new representations are used. They try to describe images more precisely without exploiting any semantic description. They use local statistics and their relationships in the image. In this paper, we present these approaches and introduce a new representation system based on a pyramidal graph. First results are also presented which show that the proposed system is very promising for both partial and global requests.
This paper is dedicated to Computer-Aided Diagnosis CAD for skin cancers in order to help the expert (dermatologist) to diagnose a dermatological lesion as benign or malignant. The need of this kind of tools has largely expressed because of the difficulties that have the expert to distinguish benign lesion from melanoma. One way to help him without a classification is to find and display to the expert the most similar images (lesions) to the query (lesion of the patient). The similarity must be measured using features and their representation inspired from the medical diagnosis rules. In fact, the diagnosis rules known as ABCD mnemonics are very interesting because they describe a lesion using color, texture and shape. In order to approach the system from the reality, we build it as a Content-Based Image Retrieval CBIR scheme. Images are represented as an object model including the features and their representation and a set of belief degrees. The aim is to combine, on one hand, the experts analysis which include their knowledge, experience. but also their subjectivity, inexactness, uncertainty, etc. On the other hand, the ground truth based on biopsy results of all the database lesions. The combination gives to the system the autonomy and let it evolve without needing a relevance feedback.
In this paper, we propose an original decomposition scheme based on Meyer's wavelets. In opposition to a classical technique of wavelet packet analysis, the decomposition is an adaptative segmentation of the frequential axis which does not use a filters bank. This permits a higher flexibility in the band frequency definition. The decomposition computes all possible partitions from a sequential space: it does not only compute those that come from a dyadic decomposition. Our technique is applied on the electroencephalogram signal; here the purpose is to extract a best basis of frequential decomposition. This study is part of a multimodal functional cerebral imagery project.
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