Color-Based Image Salient Region Segmentation Using Novel Region Merging Strategy

Kuan, Yu-Hsin; Kuo, Chung-Ming; Yang, Nai-Chung

doi:10.1109/tmm.2008.922853

Cited by 25 publications

(5 citation statements)

References 38 publications

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“…Since the background is controlled, a color based separation is possible. We have tested with various popular image segmentation schemes that are commonly used for natural image segmentation [3], [8]- [11]. Performance of mean-shift based segmentation scheme proposed by Comaniciu et al [3] for the images of the domain under consideration has been found to be satisfactory.…”

Section: Segmentation and Binarization Of Imagementioning

confidence: 97%

Automatic Adductors Angle Measurement for Neurological Assessment of Post-neonatal Infants during Follow Up

Dogra

Majumdar

Sural

et al. 2011

Lecture Notes in Computer Science

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In many medical examinations, image or video based automatic schemes are preferred over conventional approaches. Such schemes can greatly increase the efficacy and accuracy of various medical examinations. The work proposed in this article presents an image processing based method to automate adductors angle measurement which is carried out on infants as a part of Hammersmith Infant Neurological Examination (HINE). It is used for assessing neurological development of infants aged below two years. During HINE, postures and reactions of the infant under consideration are recorded. An overall score is estimated and used to quantify the neurological development index of the baby. In the conventional approach, for measuring adductors angle, doctors use rulers. The proposed method uses image segmentation and thinning techniques to measure the angle without involvement of rulers. Results show that the proposed scheme can be used as an aid to the doctors for conducting such examinations.

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Section: Segmentation and Binarization Of Imagementioning

confidence: 97%

Automatic Adductors Angle Measurement for Neurological Assessment of Post-neonatal Infants during Follow Up

Dogra

Majumdar

Sural

et al. 2011

Lecture Notes in Computer Science

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“…Many region merging algorithms build objective optimization strategy based on combined constraints of pixel features, by which a global objective function is introduced to supervise the searching manipulation to accomplish merging process [34]. There are also many approaches integrate the boundary pixels information into the overall merging strategy [35,36]. Usually, a superpixel has more than one adjacent region and its boundaries can be divided into several parts according to different neighbor areas.…”

Section: Related Workmentioning

confidence: 99%

“…Usually, a superpixel has more than one adjacent region and its boundaries can be divided into several parts according to different neighbor areas. Considering adjacent relations [35], the regional borders length is measured for similarity metrics, which is an effective assistant of color and spatial information in the merging process. In addition to above methods, there are many interaction method based on homogeneity seeds selection for region merging [37,38].…”

Section: Related Workmentioning

confidence: 99%

Texture Superpixels Merging by Color-texture Histograms for Color Image Segmentation

2014

KSII TIIS

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Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

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“…The resultant HOS map was simplified using morphological reconstruction followed by region merging to produce the output segmentation result. Kuan et al 61 presented a novel region merging strategy for segmenting salient regions in color images. The technique generated an initial set of regions by extracting dominant colors in the input image, using a nonparametric density estimation methodology.…”

Section: Spatially Guided Approachesmentioning

confidence: 99%

“…Subsequently, a merging protocol based on "importance index" and "merging likelihood" criterions was used to refine the initial set. With a similar global objective to the work in Kuan et al, 61 Liu et al 62 proposed an unsupervised segmentation algorithm aimed at salient object extraction. The method was based on region merging in a binary partition tree (BPT) framework.…”

Section: Spatially Guided Approachesmentioning

confidence: 99%

Survey of contemporary trends in color image segmentation

Vantaram

Saber

2012

J. Electron. Imaging

108

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In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

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Color-Based Image Salient Region Segmentation Using Novel Region Merging Strategy

Cited by 25 publications

References 38 publications

Automatic Adductors Angle Measurement for Neurological Assessment of Post-neonatal Infants during Follow Up

Automatic Adductors Angle Measurement for Neurological Assessment of Post-neonatal Infants during Follow Up

Texture Superpixels Merging by Color-texture Histograms for Color Image Segmentation

Survey of contemporary trends in color image segmentation

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