Hierarchical morphological segmentation for image sequence coding

This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach.Peer ReviewedPostprint (published version

Cardiac Cavity Segmentation in Echocardiography Using Triangle Equation

In this paper, cardiac cavity segmentation in echocardiography is proposed. The method uses triangle equation algorithms to detect and reconstruct the border. Prior to the application of both algorithms, some preprocessings have to be carried out. The first step is high boost filter to enhance high frequency component while still keeping the low frequency component. The second step is applying morphological and thresholding operations to eliminate noise and convert the image into binary image. The third step is negative laplacian filter to apply edge detector. The fourth step is region filter to eliminate small region. The last step is using triangle equation to detect and reconstruct the imprecise border. This technique is able to perform segmentation and detect border of cardiac cavity from echocardiographics sequences. Keywords: cardiac cavity, high boost filter, morphology, negative laplacian, region filter, and triangle equation

Recursive image sequence segmentation by hierarchical models

This paper addresses the problem of image sequence segmentation. A technique using a sequence model based on compound random fields is presented. This technique is recursive in the sense that frames are processed in the same cadency as they are produced. New regions appearing in the sequence are detected by a morphological procedure.Peer ReviewedPostprint (published version

Coding of details in very low bit-rate video systems

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept ofPeer ReviewedPostprint (published version

Morphological operators for very low bit rate video coding

This paper deals with the use of some morphological tools for video coding at very low bit rates. Rather than describing a complete coding algorithm, the purpose of this paper is to focus on morphological connected operators and segmentation tools that have proved to be attractive for compression.Peer ReviewedPostprint (published version

Foreground detection enhancement using Pearson correlation filtering

Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A video object generation tool allowing friendly user interaction

In this paper we describe an interactive video object segmentation tool developed in the framework of the ACTS-AC098 MOMUSYS project. The Video Object Generator with User Environment (VOGUE) combines three different sets of automatic and semi-automatic-tool (spatial segmentation, object tracking and temporal segmentation) with general purpose tools for user interaction. The result is an integrated environment allowing the user-assisted segmentation of any sort of video sequences in a friendly and efficient manner.Peer ReviewedPostprint (published version

Automatic cellular segmentation in time-lapse phase contrast images

The process of cellular detection and tracking is a key task in the analysis of cellular motility and proliferation. The current clinical procedure involves a time consuming procedure that requires the manual annotation of cells in sequences of time-lapse phase contrast microscopy images. With the development of modern imaging modalities, the amount of data to be interpreted by biologists is constantly increasing, thus the development of automatic techniques that are able to detect cellular structures in large image sequences is more necessary than ever before. Robust cellular detection represents the first step in the development of cellular tracking algorithms and one of the objectives of our work was focused on the development of an automatic technique that is able to segment the cells in various sequences of cellular data. The proposed segmentation framework adaptively determines the criteria to separate the cells and the background and additional morphological operations are applied to detect the initial structures that define the cells in each image of the sequence. The initial segmentation results are refined by applying motion consistency constraints to detect the cells that are missed by the initial segmentation process due to factors such as image noise and low contrast. In our experiments we have applied the proposed segmentation framework to NE4C, MDCK and HUVEC cellular data. A number of experimental results are illustrated in Figure 1

Automatic detection and extraction of artificial text in video

A significant challenge in large multimedia databases is the provision of efficient means for semantic indexing and retrieval of visual information. Artificial text in video is normally generated in order to supplement or summarise the visual content and thus is an important carrier of information that is highly relevant to the content of the video. As such, it is a potential ready-to-use source of semantic information. In this paper we present an algorithm for detection and localisation of artificial text in video using a horizontal difference magnitude measure and morphological processing. The result of character segmentation, based on a modified version of the Wolf-Jolion algorithm [1][2] is enhanced using smoothing and multiple binarisation. The output text is input to an “off-the-shelf” noncommercial OCR. Detection, localisation and recognition results for a 20min long MPEG-1 encoded television programme are presented