Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

Analysis of video sequences: table of content and index creation

This paper deals with the representation of video sequences useful for tasks such as long-term analysis, indexing or browsing. A Table Of Content and index creation algorithm is presented, as well as additional tools involved in their creation. The proposed method does not assume any a priori knowledge about the content or the structure of the video. It is therefore a generic technique. Some examples are presented in order to assess the performance of the algorithmPeer ReviewedPostprint (published version

On morphological hierarchical representations for image processing and spatial data clustering

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

General Adaptive Neighborhood Image Processing. Part II: Practical Applications Issues

23 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting morphological operators perform a really spatiallyadaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property. Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the 'contrast' criterion are shown through practical application examples

Generation of Synthetic Rainfall Data in Sekayam sub-watershed Based on TRMM Satellite Rainfall Data Correction Equation

The hydrological analysis is the first stage of the review in waterworks planning. Hydrological analysis necessitates the availability of sufficient data. Data availability tends to have several problems, including a lack of availability, incomplete/empty data, a smaller number of stations, observers, and observation systems, data entry that is still manual, and slow data collection. One possible solution is to use rain satellites. However, TRMM data must be evaluated for field suitability. TRMM (Tropical Rainfall Measurement Mission) rainfall data can help overcome this. TRMM is a NASA mission that uses weather monitoring satellite technology to monitor tropical rainfall. This is also the case for the Sekayam Subwatershed, part of the Kapuas River Basin.In the Sekayam sub-watershed, there are 14 (fourteen) rainfall observation stations managed by the Balai Wilayah Sungai Kalimantan (BWSK) I, but currently only 5 observation stations are still active, namely the SGU-01 Sanggau, SGU-03 Balai Karangan, SGU-06 Entikong, SGU-19 Semuntai and SC-01 Kembayan observation stations with data recorded up to 2019, while the other 9 (nine) observation stations do not have long continuous data, because there are years where rainfall data is not recorded. This is because the recording of rainfall on average stopped until 2005, or even some have stopped operating since the 1990s or early 2000s. This study aims to generate representative synthetic TRMM daily rainfall amount data for the Sekayam sub-watershed based on the correction equations obtained in a series of TRMM rainfall data validation analyses, so that it can be used as alternative daily rainfall data in water resources planning and management in the Sekayam sub-watershed. From the analysis, it can be seen that synthetic rainfall data in the Sekayam sub-watershed will be valid if it is generated with the linear model correction equation Y = 0.6708 X + 139.123 or can be interpreted as TRMM' = 0.6708 TRMM + 139.123

Video object segmentation introducing depth and motion information

We present a method to estimate the relative depth between objects in scenes of video sequences. The information for the estimation of the relative depth is obtained from the overlapping produced between objects when there is relative motion as well as from motion coherence between neighbouring regions. A relaxation labelling algorithm is used to solve conflicts and assign every region to a depth level. The depth estimation is used in a segmentation scheme which uses grey level information to produce a first segmentation. Regions of this partition are merged on the basis of their depth level.Peer ReviewedPostprint (published version

Stain guided mean-shift filtering in automatic detection of human tissue nuclei

Background: As a critical technique in a digital pathology laboratory, automatic nuclear detection has been investigated for more than one decade. Conventional methods work on the raw images directly whose color/intensity homogeneity within tissue/cell areas are undermined due to artefacts such as uneven staining, making the subsequent binarization process prone to error. This paper concerns detecting cell nuclei automatically from digital pathology images by enhancing the color homogeneity as a pre-processing step. Methods: Unlike previous watershed based algorithms relying on post-processing of the watershed, we present a new method that incorporates the staining information of pathological slides in the analysis. This pre-processing step strengthens the color homogeneity within the nuclear areas as well as the background areas, while keeping the nuclear edges sharp. Proof of convergence for the proposed algorithm is also provided. After pre-processing, Otsu's threshold is applied to binarize the image, which is further segmented via watershed. To keep a proper compromise between removing overlapping and avoiding over-segmentation, a naive Bayes classifier is designed to refine the splits suggested by the watershed segmentation. Results: The method is validated with 10 sets of 1000 × 1000 pathology images of lymphoma from one digital slide. The mean precision and recall rates are 87% and 91%, corresponding to a mean F-score equal to 89%. Standard deviations for these performance indicators are 5.1%, 1.6% and 3.2% respectively. Conclusion: The precision/recall performance obtained indicates that the proposed method outperforms several other alternatives. In particular, for nuclear detection, stain guided mean-shift (SGMS) is more effective than the direct application of mean-shift in pre-processing. Our experiments also show that pre-processing the digital pathology images with SGMS gives better results than conventional watershed algorithms. Nevertheless, as only one type of tissue is tested in this paper, a further study is planned to enhance the robustness of the algorithm so that other types of tissues/stains can also be processed reliably