Selective Segmentation of Piecewise Homogeneous Regions

Kapuriya, B. R.; Pradhan, Debasish; Sharma, Reena

doi:10.1007/978-3-030-38040-3_60

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Although the above-mentioned methods can segment inhomogeneous images, there is still scope of improving the segmentation accuracy of the such images. In Kapuriya et al [11], have discussed selective segmentation method of piecewise homogeneous regions by selecting points related to different similar regions in the object, but the authors have not mentioned about the effectiveness of the method in non-homogeneous regions. Subsequently, they have improved computational efficiency of the method in [12] using normalized fidelity terms for vector-valued images.…”

Section: Introductionmentioning

confidence: 99%

A Level Set Approach for Segmentation of Intensity Inhomogeneous Image Based on Region Decomposition

Chakravarty

Pradhan

2020

SN COMPUT. SCI.

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Medical images like magnetic resonance angiogram (MRA) images have inhomogeneous intensities. When such images are segmented using active contour models, they do not produce desired results. Hence, segmenting an image with inhomogeneous intensities is a challenge. In this paper, the problem of segmenting medical images with intensity inhomogeneity has been addressed. Here, we propose a method which uses the concept of region decomposition along with combination of region-based segmentation and edge-detector function. In this process, the input image is divided into four quadrants and checked for segmentation. If the segmentation of any sub-image is not as desired, it is further decomposed into four more sub-regions. The process continues until each sub-image is properly segmented. In the end, all the segmented parts are merged to obtain the desired segmented image. The proposed method has been implemented and desired results are obtained by the same and compared with existing results.

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Section: Introductionmentioning

confidence: 99%

A Level Set Approach for Segmentation of Intensity Inhomogeneous Image Based on Region Decomposition

Chakravarty

Pradhan

2020

SN COMPUT. SCI.

Self Cite

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Multimodal Object Segmentation Using Geodesic Distance and Regional Marker Points in Active Contour Approach

Kapuriya

Pradhan

Sharma

2021

Int. J. Appl. Comput. Math

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Construction of LWCNN Framework and its Application to Pedestrian Detection with Segmentation Process

Kanthavel¹

2021

JIIP

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To solve the challenges in traffic object identification, fuzzification, and simplification in a real traffic environment, it is highly required to develop an automatic detection and classification technique for roads, automobiles, and pedestrians with multiple traffic objects inside the same framework. The proposed method has been evaluated on a database with complicated poses, motions, backgrounds, and lighting conditions for an urban scenario where pedestrians are not obstructed. The suggested CNN classifier has an FPR of less than that of the SVM classifier. Confirming the significance of automatically optimized features, the SVM classifier's accuracy is equal to that of the CNN. The proposed framework is integrated with the additional adaptive segmentation method to identify pedestrians more precisely than the conventional techniques. Additionally, the proposed lightweight feature mapping leads to faster calculation times and it has also been verified and tabulated in the results and discussion section.

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Selective Segmentation of Piecewise Homogeneous Regions

Cited by 3 publications

References 25 publications

A Level Set Approach for Segmentation of Intensity Inhomogeneous Image Based on Region Decomposition

A Level Set Approach for Segmentation of Intensity Inhomogeneous Image Based on Region Decomposition

Multimodal Object Segmentation Using Geodesic Distance and Regional Marker Points in Active Contour Approach

Construction of LWCNN Framework and its Application to Pedestrian Detection with Segmentation Process

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