Region-based representations of image and video: segmentation tools for multimedia services

Salembier, Philippe; Marqués, Ferran

doi:10.1109/76.809153

Cited by 194 publications

(77 citation statements)

References 59 publications

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“…The first one involves merging adjacent regions according to some criterion after the use of the watershed algorithm (Courses & Surveys 1999;Haris et al 1998;Tremeau & Colantoni 2000). The second one uses markers to reduce over-segmentation (Salembier & Pardas 1994;Salembier & Marques 1999). Since we can get the centers of magnetic elements as markers, we chose the second approach.…”

Section: Magnetic Element Segmentation With the Marker-controlled Watmentioning

confidence: 99%

Properties of magnetic elements in the quiet Sun using the marker-controlled watershed method

Xie

Zhang

et al. 2009

A&A

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Context. The quiet Sun is an important part of understanding the global magnetic properties of the Sun. A recently launched observation system, named HINODE, provides a lot of high-resolution images for studying the quiet Sun. Obviously, it is time-consuming to analyze these images by hand. It is desirable to develop a technique for recognizing magnetic elements, thus automatically computing magnetic properties and the relationship between magnetic elements and granulation. Aims. We design an automatic method of recognizing magnetic elements based on the features of HINODE magnetograms and of measuring their properties. Then we study the relationship between magnetic elements and granulation. Methods. We used the magnetogram, continuum image, and Dopplergram on April 16, 2007, which were taken with the Solar Optical Telescope instrument aboard HINODE. The field of view is 147. 60 × 162. 30 in a quiet solar region, locating at disk center. We introduced the mark-controlled watershed method to detect magnetic elements automatically, because it is a popular image-segmentation method for dealing with overlapping objects. We took the centers that are the local maximum in all directions as the marks for restraining over-segmentation. We computed the properties of the detected magnetic elements and the relation among magnetic field strength, relative continuum intensity, and Doppler velocity at the same locations of magnetic elements. Results. We obtain the following results: (1) 34% of our observation region are covered by magnetic fields; (2) the magnetic flux distribution of all elements reaches a peak at 1.07 × 10 16 Mx for the whole region; (3) the relative continuum intensity distribution at the locations of magnetic elements reaches a peak at 0.97, which shows that the majority of magnetic elements located at the areas where the relative continuum intensity is less than its average. The relative continuum intensities in the areas with strong flux density are the median, meaning that the strong magnetic elements are usually located at the boundary of granulation; (4) the absolute Doppler velocity distribution at the locations of magnetic elements reaches a peak at 1.00 km s −1 , and the majority of weak magnetic elements located at the areas where the absolute velocity is greater than 1.00 km s −1 ; (5) strongly magnetized regions only have weak absolute Doppler velocities. The absolute velocity is lower than 1.00 km s −1 in the regions where the magnetic flux density of elements is higher than 100 G.

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Section: Magnetic Element Segmentation With the Marker-controlled Watmentioning

confidence: 99%

Properties of magnetic elements in the quiet Sun using the marker-controlled watershed method

Xie

Zhang

et al. 2009

A&A

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“…A brief review of image and video segmentation algorithms that have been presented for multimedia applications is provided in [16]. Every segmentation method emphasizes on different issues.…”

Section: Introductionmentioning

confidence: 99%

A Novel Video Object Segmentation Approach for Noisy Video Sequences towards Effective Video Retrieval

Padmakala¹,

Anandhamala²

2010

IJCTE

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Abstract-In modern times, video object segmentation has emerged as one of the most imperative and challenging area of research. The principal objective of video object segmentation is to facilitate content-based representation by extracting objects of interest from a series of consecutive video frames. Recently, a number of video object segmentation algorithms have been discussed and unfortunately most existing segmentation algorithms are not adequate and robust enough to process noisy video sequences. Competence of most segmentation techniques is affected by the presence of noise in frames which is a critical issue of edge preservation. This paper presents a novel video object segmentation approach for noisy color video sequences towards effective video retrieval. Initially, the noisy video frames are denoised using a strategy based on an enhanced sparse representation in transform domain. Afterwards, the background is estimated from the denoised frames using the Expectation Maximization (EM) algorithm. Then, the foreground objects i.e.) moving video objects are segmented with the aid of the novel approach presented. The biorthogonal wavelet transform and the L2 norm distance measure are employed in the foreground object segmentation. The experimental results demonstrate the effectiveness of the presented approach in segmenting the video objects from noisy color video sequences.

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“…A variety of applications such as object recognition, image annotation, image coding and image indexing, utilize at some point a segmentation algorithm and their performance depends highly on the quality of the latter. Comparatively to the research efforts in automatic image and video segmentation [8], [18] and global [9], [14] or region-based [3], [13] image classification, still, human vision perception outperforms state-of-the-art computer algorithms. The main reason for this is that human vision is additionally based on high level a priori knowledge about the semantic meaning of the objects that compose the image and on contextual knowledge about their relationships.…”

Section: Introductionmentioning

confidence: 99%

A Context-Based Region Labeling Approach for Semantic Image Segmentation

Athanasiadis

Mylonas

Avrithis

2006

Lecture Notes in Computer Science

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Abstract. In this paper we present a framework for simultaneous image segmentation and region labeling leading to automatic image annotation. The proposed framework operates at semantic level using possible semantic labels to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we applied our idea on two region growing algorithms, i.e. watershed and recursive shortest spanning tree. Additionally we exploit the notion of visual context by employing fuzzy algebra and ontological taxonomic knowledge representation, incorporating in this way global information and improving region interpretation. In this process, semantic region growing labeling results are being re-adjusted appropriately, utilizing contextual knowledge in the form of domain-specific semantic concepts and relations. The performance of the overall methodology is demonstrated on a real-life still image dataset from the popular domains of beach holidays and motorsports.

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Region-based representations of image and video: segmentation tools for multimedia services

Cited by 194 publications

References 59 publications

Properties of magnetic elements in the quiet Sun using the marker-controlled watershed method

Properties of magnetic elements in the quiet Sun using the marker-controlled watershed method

A Novel Video Object Segmentation Approach for Noisy Video Sequences towards Effective Video Retrieval

A Context-Based Region Labeling Approach for Semantic Image Segmentation

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