Sébastien Lefèvre

A comparative study on multivariate mathematical morphology

The successful application of univariate morphological operators on several domains, along with the increasing need for processing the plethora of available multivalued images, have been the main motives behind the efforts concentrated on extending the mathematical morphology framework to multivariate data. The few theoretical requirements of this extension, consisting primarily of a ranking scheme as well as extrema operators for vectorial data, have led to numerous suggestions with diverse properties. However, none of them has yet been widely accepted. Furthermore, the comparison research work in the current literature, evaluating the results obtained from these approaches, is either outdated or limited to a particular application domain. In this paper, a comprehensive review of the proposed multivariate morphological frameworks is provided. In particular, they are examined mainly with respect to their data ordering methodologies. Additionally, the results of a brief series of illustrative application oriented tests of selected vector orderings on colour and multispectral remote sensing data are also discussed.

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

We present in this paper a review of methods for segmentation of uncompressed video sequences. Video segmentation is usually performed in the temporal domain by shot change detection. In case of real-time segmentation, computational complexity is one of the criteria which has to be taken into account when comparing different methods. When dealing with uncompressed video sequences, this criterion is even more significant. However, previous published reviews did not involve complexity criterion when comparing shot change detection methods. Only recognition rate and ability to classify detected shot changes were considered. So contrary to previous reviews, we give here the complexity of most of the described methods. We review in this paper an extensive set of methods presented in the literature and classify them in several parts, depending on the information used to detect shot changes. The earliest methods were comparing successive frames by relying on the most simple elements, that is to say pixels. Comparison could be performed on a global level, so methods based on histograms were also proposed. Block-based methods have been considered to process data at an intermediate level, between local (using pixels) and global (using histograms) levels. More complex features can be involved, resulting in feature-based methods. Alternatively some methods rely on motion as a criterion to detect shot changes. Finally, different kinds of information could be combined together in order to increase the quality of shot change detection. So our review will detail segmentation methods based on the following information: pixel, histogram, block, feature, motion, or other kind of information.

Comments on “Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy”

The impact of PET on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the illdefinition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasise spatial accuracy and present a new metric that addresses the lack of unique ground truth. 30 methods are used at 13 different institutions to contour functional VOIs in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualisation of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.

On lexicographical ordering in multivariate mathematical morphology

Since mathematical morphology is based on complete lattice theory, a vector ordering method becomes indispensable for its extension to multivariate images. Among the several approaches developed with this purpose, lexicographical orderings are by far the most frequent, as they possess certain desirable theoretical properties. However, their main drawback consists of the excessive priority attributed to the first vector dimension. In this paper, the existing solutions to solving this problem are recalled and two new approaches are presented. First, a generalisation of @a-modulus lexicographical ordering is introduced, making it possible to accommodate any quantisation function. Additionally, an input specific method is suggested, based on the use of a marker image. Comparative application results on colour noise reduction and texture classification are also provided.

Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation

Automatic image interpretation is often achieved by first performing a segmentation of the image (i.e., gathering neighbouring pixels into homogeneous regions) and then applying a supervised region-based classification. In such a process, the quality of the segmentation step is of great importance in the final classified result. Nevertheless, whereas the classification step takes advantage from some prior knowledge such as learning sample pixels, the segmentation step rarely does. In this paper, we propose to involve such samples through machine learning procedures to improve the segmentation process. More precisely, we consider the watershed transform segmentation algorithm, and rely on both a fuzzy supervised classification procedure and a genetic algorithm in order to respectively build the elevation map used in the watershed paradigm and tune segmentation parameters. We also propose new criteria for segmentation evaluation based on learning samples. We have evaluated our method on remotely sensed images. The results assert the relevance of machine learning as a way to introduce knowledge within the watershed segmentation process.

Morphological Description of Color Images for Content-Based Image Retrieval

Placed within the context of content-based image retrieval, we study in this paper the potential of morphological operators as far as color description is concerned, a booming field to which the morphological framework, however, has only recently started to be applied. More precisely, we present three morphology-based approaches, one making use of granulometries independently computed for each subquantized color and two employing the principle of multiresolution histograms for describing color, using respectively morphological levelings and watersheds. These new morphological color descriptors are subsequently compared against known alternatives in a series of experiments, the results of which assert the practical interest of the proposed methods.

A robust hit-or-miss transform for template matching applied to very noisy astronomical images

The morphological hit-or-miss transform (HMT) is a powerful tool for digital image analysis. Its recent extensions to grey level images have proven its ability to solve various template matching problems. In this paper we explore the capacity of various existing approaches to work in very noisy environments and discuss the generic methods used to improve their robustness to noise. We also propose a new formulation for a fuzzy morphological HMT which has been especially designed to deal with very noisy images. Our approach is validated through a pattern matching problem in astronomical images that consists of detecting very faint objects: low surface brightness galaxies. Despite their influence on the galactic evolution model, these objects remain mostly misunderstood by the astronomers. Due to their low signal to noise ratio, there is no automatic and reliable detection method yet. In this paper we introduce such a method based on the proposed hit-or-miss operator. The complete process is described starting from the building of a set of patterns until the reconstruction of a suitable map of detected objects. Implementation, running cost and optimisations are discussed. Outcomes have been examined by astronomers and compared to previous works. We have observed promising results in this difficult context for which mathematical morphology provides an original solution.

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks.

This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: (1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; (2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; (3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.

Efficient and Robust Shot Change Detection

In this article, we deal with the problem of shot change detection which is of primary importance when trying to segment and abstract video sequences. Contrary to recent experiments, our aim is to elaborate a robust but very efficient (real-time even with uncompressed data) method to deal with the remaining problems related to shot change detection: illumination changes, context and data independency, and parameter settings. To do so, we have considered some adaptive threshold and derivative measures in a hue-saturation colour space. We illustrate our robust and efficient method by some experiments on news and football broadcast video sequences.

Hyperconnections and Hierarchical Representations for Grayscale and Multiband Image Processing

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.