A fuzzy measure approach to motion frame analysis for scene detection

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments

Quantitative analysis of properties and spatial relations of fuzzy image regions

Properties of objects and spatial relations between objects play an important role in rule-based approaches for high-level vision. The partial presence or absence of such properties and relationships can supply both positive and negative evidence for region labeling hypotheses. Similarly, fuzzy labeling of a region can generate new hypotheses pertaining to the properties of the region, its relation to the neighboring regions, and finally, the labels of the neighboring regions. In this paper, we present a unified methodology to characterize properties and spatial relationships of object regions in a digital image. The proposed methods can be used to arrive at more meaningful decisions about the contents of the scene

Mine Classification based on a Fuzzy Characterisation

International audienceHigh resolution sonars provide high-quality acoustic images, allowing the classification of objects from their cast shadow. For a given ground mine except mine with radial symmetry, shadow appearance generally depends on the point of view. After a segmentation step performed on images acquired along a part of a circular trajectory of the sonar around the object, we can match and superimpose binary data. The resulting image displays a fuzzy shadow region whose pixels grey-levels depend on their successive localisation in the images of the sequence, i.e. if they belong or not to the shadow region. As an extension of feature extraction in the binary case, fuzzy geometry is a practical tool to describe fuzzy regions characterised by the degree of membership of each pixel to them. After a Principal Component Analysis applied to a set of fuzzy features, encouraging results have been achieved on simulated sonar images covering both classical and stealthy mines

Fuzzy geometry, entropy, and image information

Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described

A Parallel Thinning Algorithm for Grayscale Images

International audienceGrayscale skeletonization offers an interesting alternative to traditional skeletonization following a binarization. It is well known that parallel algorithms for skeletonization outperform sequential ones in terms of quality of results, yet no general and well defined framework has been proposed until now for parallel grayscale thinning. We introduce in this paper a parallel thinning algorithm for grayscale images, and prove its topological soundness based on properties of the critical kernels framework. The algorithm and its proof, given here in the 2D case, are also valid in 3D. Some applications are sketched in conclusion

Метод сегментації зображень на основі нечітких чисел

Розглянута проблема розбиття зображення на декілька сегментів, межі яких є нечіткими. Запропоновано метод, який дозволяє ввести нечіткість в сегменти, на які розбите зображення. Такі нечіткі сегменти в подальшому використовуються для побудови нечіткого представлення зображення. Чіткі сегменти зображення, на яких базуються нечіткі сегменти і нечітке представлення зображення, отримуються шляхом тріангуляції зображення, або вручну за локальними особливостями.Предложено метод сегментации изображений на нечеткие сегменты, которые строятся на основе четких сегментов, полученных с помощью нахождения локальных особенностей изображения и дальнейшей триангуляции изображения по полученным точкам. Детально описан механизм преобразования четкого сегмента в нечеткий.A method for splitting an image into fuzzy segments is described. Fuzzy segments are based on distinct ones obtained by image triangulation with the use of local features as triangulation points. A technique for transformation of a fuzzy segment into a distinct one is described in detail

Segmentation floue d'images couleur

- Nous proposons dans cet article un algorithme permettant d'obtenir des régions floues à partir d'images monochromes ou couleur. Les régions floues se chevauchent, ont des coeurs déterminés automatiquement dans les minimums locaux des normes du gradient ; les degrés d'appartenance, fondés sur la distance topographique décroissent fortement dès qu'un contour est rencontré. Outre la segmentation nette, qu'on peut obtenir en « defuzzifiant » la segmentation floue, la principale application de cet algorithme réside dans les régions floues elles-même qui pourront être employées pour de la reconnaissance de formes ou de l'indexation d'images

Weighted fusion graphs: merging properties and watersheds

International audienc