Management zone delineation using a modified watershed algorithm

Le zonage intra-parcellaire est une méthode couramment utilisée pour gérer la variabilité intra-parcellaire. Ce concept consiste à partitionner une parcelle en zones de management selon une ou plusieurs caractéristiques du sol et/ou du couvert végétal de cette parcelle. Cet article propose une méthode de zonage originale, basée sur l'utilisation d'une méthode de segmentation d'image puissante et rapide : l'algorithme de ligne de partage des eaux. Cet algorithme d'analyse d'image a été adapté aux spécificités de l'agriculture de précision. Les performances de notre méthodes ont été testées sur des cartes biophysiques haute résolution de plusieurs champs de blé situés en Bourgogne. / Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.ZONAGE;PARCELLE;TELEDETECTION;BLE;SEGMENTATION D'IMAGE;AGRICULTURE DE PRECISION;FRANCE;BOURGOGNE;PRECISION AGRICULTURE;MANAGEMENT ZONES;REMOTE SENSING;IMAGE ANALYSIS;WATERSHED SEGMENTATION

Computational processing and analysis of ear images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Segmentation of the glottal space from laryngeal images using the watershed transform

The present work describes a new method for the automatic detection of the glottal space from laryngeal images obtained either with high speed or with conventional video cameras attached to a laryngoscope. The detection is based on the combination of several relevant techniques in the field of digital image processing. The image is segmented with a watershed transform followed by a region merging, while the final decision is taken using a simple linear predictor. This scheme has successfully segmented the glottal space in all the test images used. The method presented can be considered a generalist approach for the segmentation of the glottal space because, in contrast with other methods found in literature, this approach does not need either initialization or finding strict environmental conditions extracted from the images to be processed. Therefore, the main advantage is that the user does not have to outline the region of interest with a mouse click. In any case, some a priori knowledge about the glottal space is needed, but this a priori knowledge can be considered weak compared to the environmental conditions fixed in former works

Region-based spatial and temporal image segmentation

This work discusses region-based representations for image and video sequence segmentation. It presents effective image segmentation techniques and demonstrates how these techniques may be integrated into algorithms that solve some of the motion segmentation problems. The region-based representation offers a way to perform a first level of abstraction and to reduce the number of elements to process with respect to the classical pixel-based representation. Motion segmentation is a fundamental technique for the analysis and the understanding of image sequences of real scenes. Motion segmentation 'describes' the sequence as sets of pixels moving coherently across one sequence with associated motions. This description is essential to the identification of the objects in the scene and to a more efficient manipulation of video sequences. This thesis presents a hybrid framework based on the combination of spatial and motion information for the segmentation of moving objects in image sequences accordingly with their motion. We formulate the problem as graph labelling over a region moving graph where nodes correspond coherently to moving atomic regions. This is a flexible high-level representation which individualizes moving independent objects. Starting from an over-segmentation of the image, the objects are formed by merging neighbouring regions together based on their mutual spatial and temporal similarity, taking spatial and motion information into account with the emphasis being on the second. Final segmentation is obtained by a spectral-based graph cuts approach. The initial phase for the moving object segmentation aims to reduce image noise without destroying the topological structure of the objects by anisotropic bilateral filtering. An initial spatial partition into a set of homogeneous regions is obtained by the watershed transform. Motion vector of each region is estimated by a variational approach. Next a region moving graph is constructed by a combination of normalized similarity between regions where mean intensity of the regions, gradient magnitude between regions, and motion information of the regions are considered. The motion similarity measure among regions is based on human perceptual characteristics. Finally, a spectral-based graph cut approach clusters and labels each moving region. The motion segmentation approach is based on a static image segmentation method proposed by the author of this dissertation. The main idea is to use atomic regions to guide a segmentation using the intensity and the gradient information through a similarity graph-based approach. This method produces simpler segmentations, less over-segmented and compares favourably with the state-of-the-art methods. To evaluate the segmentation results a new evaluation metric is proposed, which takes into attention the way humans perceive visual information. By incorporating spatial and motion information simultaneously in a region-based framework, we can visually obtain meaningful segmentation results. Experimental results of the proposed technique performance are given for different image sequences with or without camera motion and for still images. In the last case a comparison with the state-of-the-art approaches is made

Tie-zone : the bridge between watershed transforms and fuzzy connectedness

Orientador: Roberto de Alencar LotufoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Esta tese introduz o novo conceito de transformada de zona de empate que unifica as múltiplas soluções de uma transformada de watershed, conservando apenas as partes comuns em todas estas, tal que as partes que diferem constituem a zona de empate. A zona de empate aplicada ao watershed via transformada imagem-floresta (TZ-IFT-WT) se revela um elo inédito entre transformadas de watershed baseadas em paradigmas muito diferentes: gota d'água, inundação, caminhos ótimos e floresta de peso mínimo. Para todos esses paradigmas e os algoritmos derivados, é um desafio se ter uma solução única, fina, e que seja consistente com uma definição. Por isso, propõe-se um afinamento da zona de empate, único e consistente. Além disso, demonstra-se que a TZ-IFT-WT também é o dual de métodos de segmentação baseados em conexidade nebulosa. Assim, a ponte criada entre as abordagens morfológica e nebulosa permite aproveitar avanços de ambas. Em conseqüência disso, o conceito de núcleo de robustez para as sementes é explorado no caso do watershed.Abstract: This thesis introduces the new concept of tie-zone transform that unifies the multiple solutions of a watershed transform, by conserving only the common parts among them such that the differing parts constitute the tie zone. The tie zone applied to the watershed via image-foresting transform (TZ-IFTWT) proves to be a link between watershed transforms based on very different paradigms: drop of water, flooding, optimal paths and forest of minimum weight. For all these paradigms and the derived algorithms, it is a challenge to get a unique and thin solution which is consistent with a definition. That is why we propose a unique and consistent thinning of the tie zone. In addition, we demonstrate that the TZ-IFT-WT is also the dual of segmentation methods based on fuzzy connectedness. Thus, the bridge between the morphological and the fuzzy approaches allows to take benefit from the advance of both. As a consequence, the concept of cores of robustness for the seeds is exploited in the case of watersheds.DoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images