scholarly journals AUTOMATIC DETERMINATION OF SEEDS FOR RANDOM WALKER BY SEEDED WATERSHED TRANSFORM FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 11 (1) ◽  
pp. 52 ◽  
Author(s):  
Moch Zawaruddin Abdullah ◽  
Dinial Utami Nurul Qomariah ◽  
Lafnidita Farosanti ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Image Classification ◽  
Misclassification Error ◽  
Segmentation Method ◽  
Automatic Determination ◽  
Tuna Fish ◽  
Watershed Transform ◽  
Random Walker

Tuna fish image classification is an important part to sort out the type and quality of the tuna based upon the shape. The image of tuna should have good segmentation results before entering the classification stage. It has uneven lighting and complex texture resulting in inappropriate segmentation. This research proposed method of automatic determination seeded random walker in the watershed region for tuna image segmentation. Random walker is a noise-resistant segmentation method that requires two types of seeds defined by the user, the seed pixels for background and seed pixels for the object. We evaluated the proposed method on 30 images of tuna using relative foreground area error (RAE), misclassification error (ME), and modified Hausdroff distances (MHD) evaluation methods with values of 4.38%, 1.34% and 1.11%, respectively. This suggests that the seeded random walker method is more effective than exiting methods for tuna image segmentation.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

scholarly journals Segmentasi Citra Ikan Tuna dengan Mahalanobis Histogram Thresholding dan Mahalanobis Fuzzy C-Means

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Andi Baso Kaswar ◽  
Agus Zainal Arifin ◽  
Arya Yudhi Wijaya
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Accuracy Improvement ◽  
Tuna Fish ◽  
Fuzzy C Means ◽  
Membership Value ◽  
Number Of Iterations

Abstract. Fuzzy C-Means segmentation algorithm based on Mahalanobis distance can be utilized to segment tuna fish image. However, initialization of pixels membership value and clusters centroid randomly cause the segmentation process become inefficient in terms of iteration and time of computation. This paper proposes a new method for tuna fish image segmentation by Mahalanobis Histogram Thresholding (M-HT) and Mahalanobis Fuzzy C-Means (MFCM). The proposed method consists of three main phases, namely: centroid initialization, pixel clustering and accuracy improvement. The experiment carried out obtained average of iteration amount is as many as 66 iteration with average of segmentation time as many as 162.03 second. While the average of Accuracy is 98.54%, average of Missclassification Error is 1.46%. The result shows that the proposed method can improve the efficiency of segmentation method in terms of number of iterations and time of segmentation. Besides that, the proposed method can give more accurate segmentation result compared with the conventional method.Keywords: Tuna Fish Image, Segmentation, Fuzzy Clustering, Histogram Thresholding, Mahalanobis Distance. Abstrak. Algoritma segmentasi Fuzzy C-Means berbasis jarak Mahalanobis dapat digunakan untuk mensegmentasi ikan tuna. Namun, inisialisasi derajat keanggotaan piksel dan centroid klaster secara random mengakibatkan proses segmentasi menjadi tidak efisien dalam hal iterasi dan waktu komputasi. Penelitian ini mengusulkan metode baru untuk segmentasi citra ikan tuna dengan Mahalanobis Histogram Thresholding (M-HT) dan Mahalanobis Fuzzy C-Means (MFCM). Metode ini terdiri atas tiga tahap utama, yaitu: inisialisasi centroid, pengklasteran piksel dan peningkatan akurasi. Berdasarkan hasil ekseprimen, diperoleh rata-rata jumlah iterasi sebanyak 66 iterasi dengan rata-rata waktu segmentasi 162,03 detik. Rata-rata Akurasi 98,54% dengan rata-rata tingkat Missclassification Error 1,46%. Hasil yang diperoleh menunjukkan bahwa metode yang diusulkan dapat meningkatkan efisiensi metode segmentasi dalam hal jumlah iterasi dan waktu segmentasi. Selain itu, metode yang diusulkan dapat memberikan hasil segmentasi yang lebih akurat dibandingkan dengan metode konvensional.Kata Kunci: Citra Ikan Tuna, Segmentasi, Fuzzy Clustering, Histogram Thresholding, Jarak Mahalanobis.

scholarly journals METHODOLOGY FOR AUTOMATIC DETERMINATION OF CONTRAST OF LASER MARKING FOR DIFFERENT MATERIALS

2019 ◽  
Vol 3 ◽  
pp. 134
Author(s):  
Lyubomir Lazov ◽  
Edmunds Teirumnieks ◽  
Nikolay Angelov ◽  
Erika Teirumnieka
Keyword(s):  
Laser System ◽  
Digital Camera ◽  
Test Field ◽  
Automatic Determination ◽  
Technological Parameters ◽  
Test Analysis ◽  
Laser Marking ◽  
Different Types

A new methodology for determining and optimizing the contrast of the technological laser marking process has been developed. It can evaluate the quality of the markings regardless of the type of material and the type of laser system. To perform the test analysis, a specialized test field is programmed, which including the change of two of the main parameters influencing the marking process: the linear energy density (LED) and the linear density of the pulses (LDI). Marking of a test field consisting of squares of a certain size is done by means of a raster marking method with a constant step between the lines. The results are processed with a digital camera and specialized software. The maximum blackening is compared with the background of all fields and is juxtaposed with the effective energy needed to obtain a certain contrast. Several consecutive iterations are made, with each of the following experiments excluding the variants with least contrast. Thus, the study consistently brings the result to a minimum working area of the basic technological parameters, providing the user's desired contrast of the marking. The developed author's method of automatically determining the contrast of the laser marking reduces the time for preliminary experimental research and gives a reliable and subjectively absent way of qualitatively marking different types of industrial products.

scholarly journals Generation of Homogeneous Slope Units Using a Novel Object-Oriented Multi-Resolution Segmentation Method

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3422
Author(s):  
Yange Li ◽  
Jianhua He ◽  
Fang Chen ◽  
Zheng Han ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Image Processing ◽  
Sensitivity Analysis ◽  
Image Segmentation ◽  
Landslide Susceptibility ◽  
Object Oriented ◽  
Segmentation Method ◽  
Novel Object ◽  
Digital Elevation

The generation of map units is a fundamental step for an appropriate assessment of landslide susceptibility. Recent studies have indicated that the terrain relief-based slope units perform better in homogeneity compared with the grid units. However, it is difficult at present to generate high-precision and high-matching slope units by traditional methods. The problem commonly concentrates in the plain areas without obvious terrain reliefs and the junction of sudden changes in terrain. In this paper, we propose a novel object-oriented segmentation method for generating homogeneous slope units. Herein, the multi-resolution segmentation algorithm in the image processing field is introduced, enabling the integration of terrain boundary conditions and image segmentation conditions in slope units. In order to illustrate the performances of the proposed method, Kitakyushu region in Japan is selected as a case study. The results show that the proposed method generates satisfactory slope units that satisfactorily reproduce the actual terrain relief, with the best within-unit and between-unit homogeneities compared with the previous methods, in particular at the plain areas. We also verify the effectiveness of the presented method through the sensitivity analysis using different resolutions of digital elevation models (DEMs) data of the region. It is reported that the presented approach is notably advanced in the requirements of the quality of DEM data, as the presented approach is less sensitive to DEM spatial resolution compared with other available methods.

scholarly journals Segmentasi Citra Ikan Tuna Menggunakan Gradient-Barrier Watershed Berbasis Analisis Hierarki Klaster dan Regional Credibility Merging

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Arif Fadllullah ◽  
Agus Zainal Arifin ◽  
Dini Adni Navastara
Keyword(s):  
Hierarchical Cluster ◽  
Adaptive Threshold ◽  
Object Identification ◽  
Misclassification Error ◽  
Object Extraction ◽  
Segmentation Method ◽  
Average Value ◽  
Modified Hausdorff Distance ◽  
Physical Features

Abstract. The main issue of object identification in tuna image is the difficulty of extracting the entire contour of tuna physical features, because it is often influenced by uneven illumination and the ambiguity of object edges in tuna image. We propose a novel segmentation method to optimize the determination of tuna region using GBW-AHK and RCM. GBW-AHK is used to optimize the determination of adaptive threshold in order to reduce over-segmented watershed regions. Then, RCM merges the remaining regions based on two merging criteria, thus it produces two main areas of segmentation, the object extraction of tuna and the background. The experimental results on 25 tuna images demonstrate that the proposed method successfully produced an image segmentation with the average value of RAE by 4.77%, ME of 0.63%, MHD of 0.20, and the execution time was 11.61 seconds. Keywords: watershed, gradient-barrier, hierarchical cluster analysis, regional credibility merging, tuna segmentation Abstrak. Kendala utama identifikasi objek tuna pada citra ikan tuna adalah sulitnya mengekstraksi seluruh kontur tubuh ikan, karena seringkali dipengaruhi faktor iluminasi yang tidak merata dan ambiguitas tepi objek pada citra. Penelitian ini mengusulkan metode segmentasi baru yang mengoptimalkan penentuan region objek tuna menggunakan Gradient-Barrier Watershed berbasis Analisis Hierarki Klaster (GBW-AHK) dan Regional Credibility Merging (RCM). Metode GBW-AHK digunakan untuk mengoptimalkan penentuan adaptif threshold untuk mereduksi region watershed yang over-segmentasi. Kemudian RCM melakukan penggabungan region sisa hasil reduksi berdasarkan dua syarat penggabungan hingga dihasilkan dua wilayah utama segmentasi, yakni ekstraksi objek ikan tuna dan background. Hasil eksperimen pada 25 citra ikan tuna membuktikan bahwa metode usulan berhasil melakukan segmentasi dengan nilai rata-rata relative foreground area error (RAE) 4,77%, misclassification error (ME) 0,63%, modified Hausdorff distance (MHD) 0,20, dan waktu eksekusi 11,61 detik. Kata Kunci: watershed, gradient-barrier, analisis hierarki klaster, regional credibility merging, segmentasi tuna

Mathematical fundamentals of the method of identification of metal inclusions in raw materials with automatic determination of their coordinates

2021 ◽  
Vol 103 (3) ◽  
pp. 23-32
Author(s):  
Leonid Zamikhovskyi ◽  
Ivan Levitskyi ◽  
Mykola Nykolaychuk ◽  
Yuriy Striletskyi
Keyword(s):  
Raw Materials ◽  
Experimental Studies ◽  
Conveyor Belt ◽  
Raw Material ◽  
Automatic Determination ◽  
Actual Problem ◽  
Theoretical Substantiation ◽  
Excitation Coil

The article deals with the actual problem of theoretical substantiation of the method of identification (diagnosis) of metal inclusions (hereinafter referred to as metal inclusions) in bulk raw materials under the conditions of a conveyor belt. The presence of metal inclusions in the raw material transported by the conveyor belt can lead to both emergencies and deterioration in the quality of the output product. The identification method provides for diagnosing the presence of metal inclusions, determining its dimensions, type of metal and coordinates relative to the cross-section of the conveyor belt. The results of theoretical and experimental studies of the method for identifying metal inclusions based on a scanning signal and an additional excitation coil are considered. A mathematical model has been developed for determining the position of metal inclusions on a conveyor belt relative to a line perpendicular to the axis between two excitation coils, including two trajectories for determining coordinates for three excitation coils and two receiving coils.

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