Transition region based single and multiple object segmentation of gray scale images

Parida, Priyadarsan; Bhoi, Nilamani

doi:10.1016/j.jestch.2015.12.009

Cited by 16 publications

(15 citation statements)

References 32 publications

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“…This paper describes thresholding technique based on block processing, which removes non uniform illumination background. In this paper we compared proposed algorithm with the existing algorithms.Priyadarsan Parida et al [8] have proved transition region based image segmentation. However, transition region based image segmentation has two shortcomings.…”

Section: Related Workmentioning

confidence: 99%

A Review on the Performance of Object Detection Algorithm

kaur

Marwaha²

2017

IJECS

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This paper represents multiple Object recognitions technology in the field of computer vision for finding and identifying multiple objects in a image or video sequence.It is basically highlight only those objects which are needed. Algorithmic descriptions of recognition task are implemented on machines which is an complex job. Thus multiple object recognition techniques need to be developed which are less intricate and well-organized The multiple object detection is a very important application of image processing. the literature survey has shown that the most of existing methods have ignored the poor quality images like image with noise or poor intensity . Noise means those signals which are not required .Also the most of the existing work for multiple object detection has neglected the use of hybrid technique. The overall goal of this research work is to propose an efficient and simple multiple object detection using the fuzzy and transition region based image segmentation.

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Section: Related Workmentioning

confidence: 99%

A Review on the Performance of Object Detection Algorithm

kaur

Marwaha²

2017

IJECS

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“…The algorithm applies the morphological operation on the fish image and then acceptable peaks of gray image histogram are selected to extract the maximum gray area of the fish image. Transition region based gray scale image segmentation approach has been introduced in [4]. This approach successfully extracts the simple object from textured background and vice versa.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The approaches in [4]- [8] use global features for solving problems in various domains. Our proposed algorithm is about fish image segmentation for extracting the fish image local features.…”

Section: Literature Reviewmentioning

confidence: 99%

Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System

Baloch¹,

Ali²,

Gul³

et al. 2017

ijacsa

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Abstract-The image features (local, global) pay vital role in image retrieval system. The effectiveness of these image features depends on the application domain, i.e., in some domains the global features generate better results while in others the local features give good results. Different species of fishes have different color, texture, and shape features in their body parts (head, abdomen, and tail). Previously most of the work, in fish image domain has been done using global features. This work claims that fish image retrieval system using local features can generate better results as compared to global features. This is because of the fact that fish image has different features in its body parts. In this research, a fish image segmentation algorithm is proposed to extract fish object from its background and then separate fish object into three distinguished body parts, i.e. head, abdomen, and tail. The proposed algorithm was tested on a subset of "QUT_fish_data" data set containing 369 fishes of various sizes of 30 species. The experimental results showed an accuracy of 87.5% on fish image segmentation and demonstrated the effectiveness of local features over global features.

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“…Image segmentation aims to get objects from an image using image characteristics such as grey level, colour, and texture. It is usually used for further processing such as biomedical image analysis, character identification and object recognition [1]. In general, the method of image segmentation can be categorized into several types of approaches: thresholding based approach [2][3][4][5], boundary based approach [6], region-based approach [7] and hybrid approach [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Image segmentation with thresholding to determine the transition region is a hybrid method that combines region-based and thresholding approaches [1,14]. In this segmentation method, pixels that have values above the threshold are considered as transitional pixels which then form a transition region.…”

Section: Introductionmentioning

confidence: 99%

Median Filter For Transition Region Refinement In Image Segmentation

Rosyadi¹,

Suciati²

2018

JUTI

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2) ABSTRAK Segmentasi citra berbasis transition region adalah salah satu metode segmentasi citra yang sederhana dan efektif. Metode ini mampu mensegmentasi citra yang mengandung objek tunggal atau banyak. Namun, metode ini bergantung pada latar belakang. Metode ini dapat memberikan hasil segmentasi yang buruk jika varians tingkat keabuan pada background cukup tinggi atau background bertekstur. Jadi metode untuk memperbaiki transition region sangatlah diperlukan. Pada penelitian ini diusulkan sebuah metode baru untuk memperbaiki transition region dengan median filter berdasarkan persentase transitional pixels yang saling berdekatan. Transition region diekstraksi dari citra grayscale. Perbaikan pada transition region dilakukan berdasarkan persentase transitional pixels yang saling berdekatan. Beberapa operasi morfologi kemudian dilakukan pada transition region. Setelah itu, proses edge linking digunakan untuk menghubungkan ujung transition region yang putus. Region filling dilakukan untuk mendapatkan area foreground. Akhirnya, citra hasil segmentasi diperoleh dengan menampilkan piksel citra grayscale yang terletak di area foreground. Nilai misclassification error (ME), false negative rate (FNR), dan false positive rate (FPR) dari hasil segmentasi dihitung untuk mengukur kinerja metode yang diusulkan. Kinerja metode yang diusulkan dibandingkan dengan kinerja metode lainnya. Hasil eksperimen menunjukkan bahwa metode yang diusulkan memiliki nilai rata-rata ME, FPR, dan FNR: 0.0297, 0.0209, dan 0.0828 yang menunjukkan bahwa metode yang diusulkan memiliki kinerja yang lebih baik daripada metode-metode lain. Terlebih lagi, metode yang diusulkan bekerja dengan baik pada citra dengan berbagai macam background, terutama pada citra dengan background bertekstur.

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Transition region based single and multiple object segmentation of gray scale images

Cited by 16 publications

References 32 publications

A Review on the Performance of Object Detection Algorithm

A Review on the Performance of Object Detection Algorithm

Fish Image Segmentation Algorithm (FISA) for Improving the Performance of Image Retrieval System

Median Filter For Transition Region Refinement In Image Segmentation

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