Parallel image segmentation using multi-threading and k-means algorithm

Bose, Soumyo; Mukherjee, Amrita; Madhulika,; Chakraborty, Sayan; Samanta, Sourav; Dey, Nilanjan

doi:10.1109/iccic.2013.6724171

Cited by 28 publications

(15 citation statements)

References 4 publications

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“…The image processing literature confirms that, heuristic algorithm approach is efficient in analysing the traditional and medical images [5][6][7][8][9][10]. Previous works also confirms that, a number of image processing techniques are proposed by the researchers to investigate the cancer with the help of clinical images [11].…”

Section: Introductionmentioning

confidence: 59%

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

S¹,

Madhumathi²,

P³

et al. 2017

IJET

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Section: Introductionmentioning

confidence: 59%

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

S¹,

Madhumathi²,

P³

et al. 2017

IJET

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“…It provides the best recall of ideal value equal one in the case of the dark background with bright objects. Since, segmentation has a significant role in several applications [21][22][23][24][25][26][27][28][29][30], Thus, it is recommended to apply the proposed method in the medical domain applications as a future work.…”

Section: Resultsmentioning

confidence: 99%

“…Segmentation in imaging discipline is extremely valuable in numerous fields such as satellite imaging [1], computer vision [2] as well as medical applications in order to diagnose the abnormalities in the image [3]. It is an imperative step in every image analysis process.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

Hore¹,

Chakraborty²,

Chatterjee³

et al. 2016

IJECE

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<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

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“…Thus, we have proposed a method in which the sequencing is done by parallel segmentation, each image is divided into several parts and then one thread controls each one of them. After segmentation, all parts are merged and the desired result is obtained [4].…”

Section: Proposed Systemmentioning

confidence: 99%

“…In paper [4], the authors have studied the performance of image processing algorithms when the multithreading approach is applied on different platforms (single core / multi-core). Results shown that multithreading improve processing time on single-core or multi-core platforms.…”

Section: Introductionmentioning

confidence: 99%

Smart City Parking Lot Occupancy Solution

Tatulea¹,

Calin²,

Brad³

et al. 2019

IJACSA

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In the context of Smart City projects, the management of parking lots is one of the main concerns of local administrations and of industrial solution providers. In this respect, we have presented an image processing application, which overcomes the issues of classical electro-mechanical solutions and employs the feed of a surveillance camera. The final web-based interface could provide to the clients the realtime availability and position of the parking space. The proposed method uses a series of feature measures in order to speed-up and accurately classifies the occupancy of the space. Using a published benchmark, our method has proved to provide very accurate results and have been extensively tested on two proprietary parking locations.

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Parallel image segmentation using multi-threading and k-means algorithm

Cited by 28 publications

References 4 publications

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

Smart City Parking Lot Occupancy Solution

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