This paper presents a method for pyramidal image decomposition called “inverse” because of the order followed to obtain the pyramid levels: from top to bottom, in correspondence with the requirement for “progressive” image transmission. The pyramid top (level zero) consists in selecting the low-frequency coefficients of the discrete cosine image transform. The following pyramid levels are made up of low-frequency discrete cosine transform (DCT) coefficients of the subimages obtained from quadtree division at each level. The quadtree root coincides with the pyramid top. The first level is the difference between the image and its approximation obtained by inverse DCT. The following (second) level is a difference too, between the previous (first) level and its approximation obtained with inverse DCT for every subimage in the first level, etc. The paper describes the principle of image decomposition, the possibilities for recursive calculation, its basic characteristics and modifications. The block diagram and the generalised scheme of the decomposition are given and some results of its modelling show the application capacities in image coding systems.

This paper presents a new non-uniform subsampling strategy based on mathematical morphology. This sampling scheme is determinist and iterative. At each iteration, a sample position is determined in order to minimize the overall absolute reconstruction error. We show that satisfying this criterion is equivalent to maximizing the volume of reconstructed image. The sample selection strategy is repeated until reaching either a previously determined number of samples, or a maximal admissible error. The correspondent reconstruction procedure is also described. The algorithm is first presented using only one structuring element, then, extended to the case of a family of structuring elements. Experiments on the choice of structuring elements in the family, allow us to choose the optimal description set. Finally, we present an application of this novel image representation method on image compression. A comparison of results with JPEG standard shows the good performance of our approach, especially for high compression ratios.

In this paper, we first present a non uniform optimal sampling strategy based on mathematical morphology, for the description of image sequences. This sampling scheme is determinist and iterative. At each iteration, a sample position in the sequence, is determined in order to minimize the overall absolute reconstruction error. This procedure is repeated until reaching either an a priori fixed number N of samples, or satisfying a maximal admissible error criterion. The associated sequence reconstruction procedure is described as well. The reconstructed sequence is an approximation of the opened sequence by the family of structuring elements used. The second part is devoted to the elaboration of a complete sequence coding scheme based on this sampling strategy for image description. Finally, simulation results on video sequences illustrate the performances of this new coding scheme.

Character recognition and texture segmentation are performed by a filter based on morphological opening. The filter is characterized by two shape patterns, one of them being larger than the other. If it is applied to an image, the output image will keep only the image objects which are smaller than the large shape pattern but larger that the small one. Thus, the filter has shape band-pass characteristics. It is used for character recognition and texture segmentation. Experimental results show the good performance of the filter for these purposes.

This paper presents a new method for texture classification and textured image segmentation based on grayscale mathematical morphology. It defines a recursive morphological decomposition algorithm by using a group of structuring elements with different sizes which decomposes a texture image into a series of component images according to texture primitive sizes and gray levels. Each component image contains only the texture primitives of a certain size, and the original texture image can be exactly reconstructed by the sum of all of its component images. Many texture features can be extracted from these component images for texture classification and textured image segmentation. This paper, then, proposes an adaptive textured image segmentation technique. The size of the window from which texture features are extracted is selected according to expectation of misclassification from the textures to be segmented. The window position for each pixel is determined by its neighborhood. This technique can improve segmentation results, especially along texture boundaries. The experimental results show that the method presented is fast in computation and efficient for classification and segmentation of both structural and random textures. Fairly good experimental results have been obtained, even though few features have been used.