Region and graph-based motion segmentation

Indexado ISIThis paper describes an approach for integrating motion estimation and region clustering techniques with the purpose of obtaining precise multiple motion segmentation. Motivated by the good results obtained in static segmentation we propose a hybrid approach where motion segmentation is achieved within a region-based clustering approach taken the initial result of a spatial pre-segmentation and extended to include motion information. Motion vectors are first estimated with a multiscale variational method applied directly over the input images and then refined by incorporating segmentation results into a region-based warping scheme. The complete algorithm facilitates obtaining spatially continuous segmentation maps which are closely related to actual object boundaries. A comparative study is made with some of the best known motion segmentation algorithms

Image segmentation using region merging combined with a multi-class spectral method

In this paper we propose an image segmentation algorithm that combines region merging with spectral-based techniques. An initial partitioning of the image into primitive regions is produced by applying a region merging approach which produces a chunk graph that takes in attention the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process that produces the final segmentation. The latter process uses a multi-class partition that minimizes the normalized cut value for the region graph. We have efficiently applied the proposed approach with good visual and objective segmentation quality results

Editorial

This Special Issue on Electrical and Computer Engineering includes selected papers from the 1st edition of the Symposium on Electrical and Computer Engineering (ECE 2015), one of the symposia included in the 1st Doctoral Congress in Engineering, held at FEUP, 11-12 June, 2015. ECE 2015 was an important forum for presenting the research activities of ECE students, particularly from the Doctoral Program in Electrical and Computer Engineering, at FEUP (PDEEC). ECE 2015 received a total of 42 two-page abstracts. The review process was carried out by members of the Symposium Scientific Committee and other reviewers. Each abstract was reviewed by at least two reviewers, and checked by the Program Committee. 37 abstracts were finally accepted and appear in the Symposium book of abstracts. From the 37 abstracts, 17 were presented in four oral sessions, and 20 in one poster session. We were very pleased to include two keynote talks: “The Internet of Things - Latest Trends and Future Perspectives” by Carlos Azeredo Leme, University of Lisbon, Portugal; “A Perspective on Virtual Radio Access Networks” by Luís M. Correia, University of Lisbon, Portugal. Six papers were invited to submit extended versions to this special issue, that were further reviewed and published in this issue. We would like to sincerely thank the authors for submitting these extended versions, and we thank the special issue reviewers for the careful evaluation and feedback provided to the authors. We also would like to express our gratitude to Luís Miguel Costa, for supporting the organization of this Special issue of the U.Porto Journal of Engineering. Finally, we are very pleased to give the readership of this Special Issue on ECE examples of the research developed by PDEEC students, from a vast area covered by the Electrical and Computer Engineering at FEUP and at the associated research institutes and research centers

Distance measures for image segmentation evaluation

In this paper we present a study of evaluation measures that enable the quantification of the quality of an image segmentation result. Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Such an evaluation criterion can be useful for different applications: the comparison of segmentation results, the automatic choice of the best fitted parameters of a segmentation method for a given image, or the definition of new segmentation methods by optimization. We first present the state of art of distance evaluation measures, and then, we compare several evaluation criteria

Measurement of retinal blood vessel caliber using two different segmentation methods

In this paper, we explore two different retinal vessel segmentationmethods for the reliable estimation of vessels caliber in retinal images inorder to assess vascular changes as an aid for the diagnosis of the ocularmanifestations of several systemic diseases, namely diabetic retinopathyand hypertensive retinopathy

An automatic graph-based method for retinal blood vessel classification

In this paper, we present an automatic approach to classify retinal vessels intoartery and vein classes by analyzing the extracted graph from the vasculature treeand deciding on the type of intersection points (bifurcation, crossing or meetingpoints). The results obtained by the proposed method were compared withmanual classification on 40 images of the INSPIRE-AVR dataset

Correction of geometrical distortions in bands of chromatography images

This paper presents a methodology for correcting band distortions in Thin-LayerChromatography (TLC) images. After the segmentation of image lanes, theintensity profile of each lane column is spatially aligned with a reference profileusing a modified version of the Correlation Optimized Warping (COW)algorithm. The proposed band correction methodology was assessed using 105profiles of TLC lanes. A set of features for band characterization was extractedfrom each lane profile, before and after band distortion correction, and was usedas input for three distinct one-class classifiers aiming at band identification. In allcases, the best results of band classification were obtained for the set lanes afterband distortion correction

Classifier-based cell segmentation from confocal microscopy images

In vivo observation and tracking of cell division in theArabidopsis thaliana root meristem, by time-lapse confocalmicroscopy, is central to biology research. This paper discussesan automatic cell segmentation method, which selectsthe best cell candidates from a starting watershed segmentation.The selection of individual cells is obtained usinga Support Vector Machine (SVM) classifier, based on theshape and edge strength of the cells contour. The result isan improved segmentation, which is largely pruned of badlysegmented cell