Academic literature on the topic 'Fuzzy segmentace'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Fuzzy segmentace.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Fuzzy segmentace"
1
Kusnadi, Adhi, and Abi Kabisah Maulillah. "Perbandingan Algoritma C-Means Clustering dan Fuzzy C-Means Clustering." Ultima Computing : Jurnal Sistem Komputer 11, no. 1 (August 30, 2019): 51–54. http://dx.doi.org/10.31937/sk.v11i1.953.
Full textAbstract:
Salah satu operasi di dalam analisis citra adalah segmentasi citra. Pada mulanya proses segmentasi dilakukan untuk memisahkan objek dari latar belakangnya, sehingga segmentasi merupakan bagian penting dalam pengenalan objek. Saat ini segmentasi sudah mengalami perkembangan yang sangat pesat, bukan hanya untuk tujuan pengenalan objek saja tetapi juga untuk persoalan interpretasi citra, yaitu untuk mengetahui objek-objek yang termuat dalam suatu citra. Banyak algoritma sudah dikembangkan untuk proses segmentasi citra. Beberapa di antaranya adalah algoritma C-Means Clustering dan Fuzzy C-Means Clustering. Pada peneltian ini, dilakukan perbandingan antara algoritma C-Means Clustering dan Fuzzy C-Means Clustering dalam segmentasi citra. Dari beberapa hasil percobaan yang didapat dalam penelitian ini berupa sisi waktu atau kecepatan, ketelitian dan pengulangan, maka dapat disimpulkan algoritma Fuzzy C-Means Clustering adalah algoritma yang terbaik yang dapat digunakan dalam segmentasi citra karena dalam algoritma Fuzzy C-Means Clustering terdapat nilai keanggotaan atau fuzzy yang secara iteratif diperbaiki hingga mencapai keadaan konvergen.
2
Rosyani, Perani, and Saprudin Saprudin. "Deteksi Citra Bunga Menggunakan Analisis Segmentasi Fuzzy C-Means dan Otsu Threshold." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 20, no. 1 (September 29, 2020): 29–36. http://dx.doi.org/10.30812/matrik.v20i1.715.
Full textAbstract:
Segmentasi merupakan proses penting di dalam proses pengenalan citra. Segmentasi citra penting untuk mengektrasi fitur yang kita akan ambil sebagai data di dalam penelitian. Beberapa metode segmentasi digunakan di dalam proses pengambilan fitur. Namun di dalam penelitian ini kami menggunakan metode Fuzzy C-Means dan Otsu Threshold untuk mendeteksi citra bunga. Agar citra bunga dapat dikenali oleh komputer seperti penglihatan manusia. Dataset yang digunakan mengunakan Imageclef 2017. Citra yang diambil sebagai sample sebanyak 41 citra dengan kondisi background citra yang komplek dengan noise. Tujuan penelitian ini adalah mendapatkan metode segmentasi yang lebih baik di antara metode Fuzzy C-Means dengan Otsu Threshold. Hasil dari penelitian ini didapat dari 41 percobaan keberhasilan segmentasi Fuzzy C-Means mendeteksi objek secara sempurna adalah sebanyak 28 citra dan 16 citra yang gagal. Sedangkan untuk segmentasi menggunakan Otsu Threshold adalah sebanyak 24 citra yang sesuai dan 17 citra yang gagal. Persentase keberhasilan untuk metode Fuzzy C-Means adalah 61% dan untuk metode Otsu Threshold 70,8%.
3
Nilawati, Asyaroh Ramadona, and Taufik Hidayat. "EKSTRAKSI FITUR PEMBULUH DARAH CITRA FUNDUS RETINA MENGGUNAKAN FUZZY LOGIC." Jurnal Ilmiah Informatika Komputer 26, no. 2 (2021): 163–75. http://dx.doi.org/10.35760/ik.2021.v26i2.4304.
Full textAbstract:
Ekstraksi pola pembuluh darah retina dapat dimanfaatkan dalam sistem biometrik sebagai otentikasi keamanan. Citra hasil ekstraksi pola pembuluh darah retina dapat dimasukkan ke dalam fitur untuk identifikasi sistem biometrik. Salah satu metode yang dapat dilakukan untuk melakukan segmentasi pembuluh darah retina adalah metode fuzzy logic. Pada penelitian ini, dilakukan ekstraksi pembuluh darah citra fundus retina menggunakan implementasi fuzzy logic. Peneliti menggunakan sejumlah 20 citra fundus yang diperoleh dari dataset DRIVE berformat .tif. Proses segmentasi dimulai dengan tahap preprocessing yang berisikan konversi citra menjadi grayscale, median filtering, perataan histogram CLAHE, dan eliminasi optic disc, kemudian dilanjutkan dengan pembuatan fuzzy inference system. Tahapan preprocessing yang digunakan merupakan hasil dari rangkaian uji coba peneliti dengan melihat hasil dari setiap uji coba yang dilakukan, sehingga mendapatkan citra yang menonjolkan fitur pembuluh darah dan menghilangkan noise atau fitur retina yang tidak diperlukan seperti optic disc. Uji coba segmentasi dilakukan pada Polyspace R2020a sebagai media untuk menjalankan program mulai dari preprocessing hingga segmentasi menggunakan fuzzy logic. Keluaran dari segmentasi ini berupa citra segmentasi hasil dari metode fuzzy logic dan crisp value. Metode fuzzy logic berhasil diterapkan untuk melakukan ekstraksi pembuluh darah retina dan menghasilkan crisp value. Hasil penelitian ini diharapkan dapat digunakan sebagai salah satu fitur sistem identifikasi biometrik retina.
4
Safitri, Qonita Ummi, Arief Fatchul Huda, and Asep Solih Awaludin. "SEGMENTASI CITRA MENGGUNAKAN ALGORITMA FUZZY c-MEANS (FCM) DAN SPATIAL FUZZY c-MEANS (sFCM)." Kubik: Jurnal Publikasi Ilmiah Matematika 2, no. 1 (May 31, 2017): 22–34. http://dx.doi.org/10.15575/kubik.v2i1.1471.
Full textAbstract:
Pengolahan citra merupakan salah satu aplikasi yang dimanfaatkan dalam kehidupan. Salah satu kajian pengolahan citra adalah segmentasi. Segmentasi citra dilakukan dengan banyak pendekatan, diantaranya pedekatan klastering. Algoritma klastering yang digunakan pada segmentasi citra, umumnya berbasis fuzzy c-means. Fuzzy c-mean (FCM) membagi citra menjadi beberapa wilayah tingkat keabuan berdasarkan derajat keanggotaan pada rentang [0,1]. FCM kurang memanfaatkan informasi spasial, yang merupakan atribut penting dalam proses segmentasi citra. Oleh karena itu, Chuang dkk (2006) menambahkan fungsi spasial dalam perhitungan derajat keanggotaan FCM, dengan parameter kontrol non-spasial p dan parameter kontrol spasial q. Metode ini dikenal dengan nama spatial fuzzy c-means (sFCM). Kinerja algoritma FCM dan sFCM diuji menggunakan data citra simulasi, citra batik dan citra otak. Hasil segmentasi terbaik ditentukan berdasarkan indeks validasi Vpe, Vpc, Vxb dan SC. Hasil segmentasi menunjukkan bahwa variasi parameter p dan q terbaik menurut indeks validasi Vpe dan Vpc adalah sFCM2,1 dan sFCM2,2, sedangkan Vxb dan SC menghasilkan nilai optimal untuk FCM. Namun, sFCM hanya memberikan sedikit perbaikan terhadap hasil segmentasi FCM pada citra yang mengandung gaussian noise. Artinya, sFCM tidak robust (tahan) pada citra noise.
5
Gunawan, Wawan, and Agus Zainal Arifin. "Lokal Fuzzy Thresholding Berdasarkan Pengukuran Fuzzy Similarity Pada Interaktif Segmentasi Citra Panoramik Gigi." JURNAL INFOTEL 9, no. 1 (February 1, 2017): 40. http://dx.doi.org/10.20895/infotel.v9i1.162.
Full textAbstract:
Dalam segmentasi citra, thresholding merupakan salah metode yang mudah dan sederhana untuk diimplementasikan. Pada citra panoramik gigi, penentuan global threshold masih kurang begitu optimal untuk diimplementasikan. Hal tersebut dikarenakan adanya factor penghambat seperti pencahayaan yang tidak merata dan citra yang kabur. Faktor-faktor tersebut dapat menyebabkan histogram tidak bisa dipartisi dengan baik, sehingga akan berpengaruh pada hasil segmentasi. Pada penelitian ini diusulkan lokal fuzzy thresholding berdasarkan pengukuran fuzzy similarity pada interaktif segmentasi citra panoramik gigi. Metode yang diusulkan terdiri dari tiga tahapan utama, tahap pertama region splitting untuk mendapatkan lokal region. Tahap kedua adalah user marking untuk mendapat inisial seed background dan objek, Tahap terakhir adalah pengukuran fuzzy similarity pada setiap lokal region untuk mendapatkan nilai lokal threshold. Hasil uji coba pada citra panoramik gigi, metode yang diusulkan berhasil melakukan segmentasi dengan rata-rata missclasification error (ME) 5.47%.
6
Herdian Andika, Tahta. "Pengenalan Pola Berbasis Segmentasi Citra Menggunakan Algoritma Fuzzy C-Means Dan K-Means." Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E) 1, no. 1 (August 1, 2019): 1–10. http://dx.doi.org/10.30604/jti.v1i1.3.
Full textAbstract:
Segmentasi merupakan salah satu bagian penting dalam analisis citra, karena pada prosedur ini gambar/citra yang diinginkan akan dianalisis untuk proses yang lebih lanjut agar lebih mudah di analisis gunat ujuan selanjutnya, misalnya pada pengenalan pola.Segmentasi citra yang merupakan bagian dari analisis citra digunakan untuk membagi sebuah citra menjadi beberapa bagian dan mengambil sebagian objek yang diinginkan.Salah satu teknik dalam segmentasi citra adalah dengan clustering. Clustering adalah suatu usaha untuk melakukan pengelompokan data berdasarkan kelas dan merupakan metode mengelompokkan atau mempartisi data dalam suatu dataset.Segmentasi citra berbasis clustering pada penelitian ini menggunakan metode K-Means dan metode Fuzzy C Means. K-Means merupakan metode yang simple dan cepat perhitungannya, sedangkan Fuzzy C-Means merupakan algoritma yang populer digunakan dalam teknik Fuzzy Clustering.Penelitian ini untuk mengetahui metode yang paling optimal dalam melakukan segmentasi citra. Sebelum melakukan segmentasi terlebih dahulu menentukan ruang warna menggunakan CIELab. Identifikasi data uji menggunakan dua pendekatan, yaitu analisis bentuk dan analisis tekstur.Hasil pengujian menunjukan algoritma K-Means menghasilkan segmentasi untuk identifikasi yang lebih baik dari pada Fuzzy C Means karena menghasilkan nilai yang hampir sama atau mendekati dengan nilai ekstraksi ciri citra yang tersedia.
7
Wibawa, Made Satria. "STUDI KOMPARASI METODE SEGMENTASI PARU-PARU PADA CITRA CT-SCAN AKSIAL." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 7, no. 3 (April 24, 2019): 283. http://dx.doi.org/10.23887/janapati.v7i3.15751.
Full textAbstract:
Kanker paru dapat diobati jika diagnosis dini dilakukan. Diagnosis dapat dilakukan menggunakan modalitas citra Computed Tomography (CT). Diagnosis kanker paru melalui citra CT dilakukan oleh tenaga medis. Untuk membantu diagnosis kanker, tenaga medis dapat dibantu dengan Computer Assisted Diagnosis (CAD). Dalam CAD, tahapan pertama yang paling penting adalah segmentasi citra paru-paru. Penelitian ini melakukan studi komparasi metode segmentasi citra CT paru-paru. Terdapat tiga metode segmentasi yang digunakan, yaitu Otsu, K-Means dan Fuzzy C-Means. Proses evaluasi menggunakan metrik akurasi, true negative rate dan true positive rate. Berdasarkan nilai yang diperoleh dari ketiga parameter evaluasi tersebut, ketiga metode segmentasi dapat memberikan hasil segmentasi yang mendekati citra ground truth. Namun, dilihat dari sebaran hasil nilai ketiga parameter evaluasi yang didapatkan dari seluruh citra, metode Otsu sedikit lebih unggul dibandingkan metode K-Means dan Fuzzy C-Means.
8
Syarif, Nindya Rahmawati, and Windarto Windarto. "IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN METODE RECENCY FREQUENCY MONETARY (RFM) PADA APLIKASI DATA MINING UNTUK PENGELOMPOKAN PELANGGAN." Sebatik 22, no. 2 (December 4, 2018): 88–94. http://dx.doi.org/10.46984/sebatik.v22i2.313.
Full textAbstract:
Pada dunia retail, konsumen merupakan salah satu aset yang sangat berpengaruh. Oleh sebab itu konsumen menjadi alasan perusahaan retail harus merencanakan dan mempunyai strategi yang baik dalam memperlakukan konsumennya. Dengan banyaknya jumlah konsumen yang dimiliki oleh suatu perusahaan retail, maka masalah yang harus dihadapi adalah bagaimana menentukan konsumen potensial dan menerapkan strategi pemasaran yang tepat sehingga mendatangkan keuntungan bagi pihak perusahaan. Maka dari itu, dengan menerapkan konsep CRM (Customer Relationship Management), perusahaan dapat melakukan penerapan konsumen potensial dengan melakukan segmentasi pengelompokan konsumen. Penelitian ini membahas tentang proses data mining menggunakan data konsumen dan data transaksi pada PT Eka Cipta Rasa. Proses data mining dimulai dengan melakukan proses clustering menggunakan algoritma Fuzzy C-Means (FCM). Output dari FCM adalah deretan pusat cluster dan beberapa derajat keanggotaan untuk setiap titik data. Hasil clustering digunakan untuk melakukan segmentasi kelas konsumen dengan menggunakan model Fuzzy RFM. Model Fuzzy RFM yaitu dengan menggabungkan teori himpunan fuzzy dengan model RFM dengan segmentasi berdasarkan atribut Recency, Frequency, dan Monetary. Umumnya perusahaan retail menggunakan metode ini untuk proses segmentasi konsumennya. Pengelompokkan (cluster) pelanggan berdasarkan karakteristik dan sifat saat bertransaksi dapat dijadikan suatu alternatif dalam pemecahan masalah. Dalam hal ini, pelanggan akan dibagi menjadi 4 kelompok atau cluster pelanggan diantaranya Golden, Silver, Bronze, dan Iron adapun variabel yang menjadi acuan pengelompokannya adalah tanggal pembelian akhir , frekuensi beli, dan total pembelian. Data yang digunakan merupakan data transaksi pelanggan periode Januari sampai Juni 2016. Total data adalah 4007 transaksi dari 328 pelanggan. Setelah data tersebut diolah dengan algoritma Fuzzy C-Means dan metode RFM, hasil akhir pada proses ini menunjukkan iterasi berakhir pada iterasi ke – 9 dengan total fungsi objektif sebesar 102,2 dan perubahan fungsi objektif sebesar 51,1. Cluster pelanggan yang dihasilkan adalah Golden : 38, Silver: 186, Bronze : 103 dan Iron : 0 .
9
Ihsan, Indah Purwitasari, and Muh Sakir. "CLUSTERING DAN SEGMENTASI GAMBAR MENGGUNAKAN ALGORITMA FUZZY C-MEANS." Simtek : jurnal sistem informasi dan teknik komputer 4, no. 1 (April 8, 2019): 9–12. http://dx.doi.org/10.51876/simtek.v4i1.41.
Full textAbstract:
Fuzzy C-Means (FCM) adalah suatu teknik pengklasteran data yang mana keberadaan tiap-tiap titik data dalam suatu ditentukan oleh derajat keanggotaan, dalam algoritma Fuzzy C-Means, input data yang akan di cluster berupa matriks X berukuran n x m (n = jumlah sampel data dan m = atribut setiap data). Permasalahan penggunaan ruang memori penyimpanan yang besar membutuhkan metode tertentu untuk efisiensi penggunaan ruang penyimpanan, salah satu metode yang cukup efektif untuk pemampatan adalah kompresi, banyak algoritma yang digunakan untuk kompresi file baik data maupun gambar, namun perkembangan menunjukkan bahwa clustering dan segmentasi merupakan salah satu metode yang cukup efisien. Dari hasil penelitian diketahui bahwa Algoritma fuzzy c-mean sangat efektif diimplementasikan untuk segmentasi gambar berwarna karena proses clusteringnya yang tersebar secara tidak teratur atau acak. Running timenya bergantung pada besarnya ukuran file gambar. Semakin besar ukurannya maka durasinya akan semakin lama begitupun sebaliknya.
10
Wijaksana, I. Gusti Ngurah Winanda, Ida Ayu Dwi Giriantari, and I. Made Sudarma. "Perbandingan Metode Segmentasi SOM dan Fuzzy CMeans pada Content-Based Image Retrieval Berbasis Warna." Majalah Ilmiah Teknologi Elektro 17, no. 3 (December 5, 2018): 333. http://dx.doi.org/10.24843/mite.2018.v17i03.p05.
Full textAbstract:
Intisari— Sulitnya menentukan kata kunci yang tepat untuk mendapatkan citra yang diinginkan merupakan kelemahan pencarian citra berdasarkan kata kunci metadata. Perkembangan teknologi saat ini mengarah pada pencarian citra berdasarkan konten atau Content-based Image Retrieval (CBIR). Salah satu ciri konten citra yang digunakan untuk temu kembali citra adalah ciri warna. Untuk semakin meningkatkan kinerja CBIR, pada penelitian ini diteliti mengenai perbandingan metode segmentasi SOM dan Fuzzy C-Means. Metode segmentasi ini memisahkan foreground dan background dari citra query untuk mendapatkan kinerja CBIR yang lebih baik. Adapun database citra yang digunakan adalah Wang Dataset. Pengujian dilakukan dengan citra uji yang telah mengalami perubahan skala, rotasi dan kekaburan. Hasil dari pengujian menunjukkan penggunaan metode segmentasi meningkatkan nilai recall atau citra benar yang berhasil ditemukan, namun secara signifikan mengurangi nilai precision atau rasio citra benar dari keseluruhan citra yang ditemukan dibandingkan tanpa mengunakan metode segmentasi.
Kata Kunci— CBIR, Color Moment, SOM, FCM.
Dissertations / Theses on the topic "Fuzzy segmentace"
1
Pasáček, Václav. "Segmentace obrazu podle textury." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236463.
Full textAbstract:
Image segmentation is an important step in image processing. A traditional way how to segment an image is a texture-based segmentation that uses texture features to describe image texture. In this work, Local Binary Patterns (LBP) are used for image texture representation. Texture feature is a histogram of occurences of LBP codes in a small image window. The work also aims to comparison of results of various modifications of Local Binary Patterns and their usability in the image segmentation which is done by unsupervised clustering of texture features. The Fuzzy C-Means algorithm is finally used for the clustering in this work.
2
Čambalová, Kateřina. "Volné algebraické struktury a jejich využití pro segmentaci digitálního obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-231711.
Full textAbstract:
The thesis covers methods for image segmentation. Fuzzy segmentation is based on the thresholding method. This is generalized to accept multiple criteria. The whole process is mathematically based on the free algebra theory. Free distributive lattice is created from poset of elements based on image properties and the lattice members are represented by terms used by the threshoding. Possible segmentation results compose the equivalence classes distribution. The thesis also contains description of resulting algorithms and methods for their optimization. Also the method of area subtracting is introduced.
3
Santos, Tiago Souza dos. "Segmenta??o Fuzzy de Texturas e V?deos." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18063.
Full textAbstract:
Made available in DSpace on 2014-12-17T15:48:04Z (GMT). No. of bitstreams: 1
TiagoSS_DISSERT.pdf: 2900373 bytes, checksum: ea7bd73351348f5c75a5bf4f337c599f (MD5)
Previous issue date: 2012-08-17Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
The segmentation of an image aims to subdivide it into constituent regions or objects that have some relevant semantic content. This subdivision can also be applied to videos. However, in these cases, the objects appear in various frames that compose the videos. The task of segmenting an image becomes more complex when they are composed of objects that are defined by textural features, where the color information alone is not a good descriptor of the image. Fuzzy Segmentation is a region-growing segmentation algorithm that uses affinity functions in order to assign to each element in an image a grade of membership for each object (between 0 and 1). This work presents a modification of the Fuzzy Segmentation algorithm, for the purpose of improving the temporal and spatial complexity. The algorithm was adapted to segmenting color videos, treating them as 3D volume. In order to perform segmentation in videos, conventional color model or a hybrid model obtained by a method for choosing the best channels were used. The Fuzzy Segmentation algorithm was also applied to texture segmentation by using adaptive affinity functions defined for each object texture. Two types of affinity functions were used, one defined using the normal (or Gaussian) probability distribution and the other using the Skew Divergence. This latter, a Kullback-Leibler Divergence variation, is a measure of the difference between two probability distributions. Finally, the algorithm was tested in somes videos and also in texture mosaic images composed by images of the Brodatz album
A segmenta??o de uma imagem tem como objetivo subdividi-la em partes ou objetos constituintes que tenham algum conte?do sem?ntico relevante. Esta subdivis?o pode tamb?m ser aplicada a um v?deo, por?m, neste, os objetos est?o presentes nos diversos quadros que comp?em o v?deo. A tarefa de segmentar uma imagem torna-se mais complexa quando estas s?o compostas por objetos que contenham caracter?sticas texturais, com pouca ou nenhuma informa??o de cor. A segmenta??o difusa, do Ingl?s fuzzy, ? uma t?cnica de segmenta??o por crescimento de regi?es que determina para cada elemento da imagem um grau de pertin?ncia (entre zero e um) indicando a confian?a de que esse elemento perten?a a um determinado objeto ou regi?o existente na imagem, fazendo-se uso de fun??es de afinidade para obter esses valores de pertin?ncia. Neste trabalho ? apresentada uma modifica??o do algoritmo de segmenta??o fuzzy proposto por Carvalho [Carvalho et al. 2005], a fim de se obter melhorias na complexidade temporal e espacial. O algoritmo foi adaptado para segmentar v?deos coloridos tratando-os como volumes 3D. Para segmentar os v?deos, foram utilizadas informa??es provenientes de um modelo de cor convencional ou de um modelo h?brido obtido atrav?s de uma metodologia para a escolha dos melhores canais para realizar a segmenta??o. O algoritmo de segmenta??o fuzzy foi aplicado tamb?m na segmenta??o de texturas, fazendo-se uso de fun??es de afinidades adaptativas ?s texturas de cada objeto. Dois tipos de fun??es de afinidades foram utilizadas, uma utilizando a distribui??o normal de probabilidade, ou Gaussiana, e outra utilizando a diverg?ncia Skew. Esta ?ltima, uma varia??o da diverg?ncia de Kullback- Leibler, ? uma medida da diverg?ncia entre duas distribui??es de probabilidades. Por fim, o algoritmo foi testado com alguns v?deos e tamb?m com imagens de mosaicos de texturas criadas a partir do ?lbum de Brodatz e outros
4
Oliveira, Lucas de Melo. "Segmenta??o fuzzy de imagens e v?deos." Universidade Federal do Rio Grande do Norte, 2007. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18117.
Full textAbstract:
Made available in DSpace on 2014-12-17T15:48:12Z (GMT). No. of bitstreams: 1
LucasMO.pdf: 1455032 bytes, checksum: 6bc4218b3d779cfc9915c6a2efda34f1 (MD5)
Previous issue date: 2007-02-23Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
Image segmentation is the process of subdiving an image into constituent regions or objects that have similar features. In video segmentation, more than subdividing the frames in object that have similar features, there is a consistency requirement among segmentations of successive frames of the video. Fuzzy segmentation is a region growing technique that assigns to each element in an image (which may have been corrupted by noise and/or shading) a grade of membership between 0 and 1 to an object. In this work we present an application that uses a fuzzy segmentation algorithm to identify and select particles in micrographs and an extension of the algorithm to perform video segmentation. Here, we treat a video shot is treated as a three-dimensional volume with different z slices being occupied by different frames of the video shot. The volume is interactively segmented based on selected seed elements, that will determine the affinity functions based on their motion and color properties. The color information can be extracted from a specific color space or from three channels of a set of color models that are selected based on the correlation of the information from all channels. The motion information is provided into the form of dense optical flows maps. Finally, segmentation of real and synthetic videos and their application in a non-photorealistic rendering (NPR) toll are presented
Segmenta??o de imagens ? o processo que subdivide uma imagem em partes ou objetos de acordo com alguma caracter?stica comum. J? na segmenta??o de v?deos, al?m dos quadros serem divididos em fun??o de alguma caracter?stica, ? necess?rio obter uma coer?ncia temporal entre as segmenta??es de frames sucessivos do v?deo. A segmenta??o fuzzy ? uma t?cnica de segmenta??o por crescimento de regi?es que determina para cada elemento da imagem um grau de pertin?ncia (entre zero e um) indicando a confian?a de que esse elemento perten?a a um determinado objeto ou regi?o existente na imagem. O presente trabalho apresenta uma aplica??o do algoritmo de segmenta??o fuzzy de imagem, e a extens?o deste para segmentar v?deos coloridos. Nesse contexto, os v?deos s?o tratados como volumes 3D e o crescimento das regi?es ? realizado usando fun??es de afinidade que atribuem a cada pixel um valor entre zero e um para indicar o grau de pertin?ncia que esse pixel tem com os objetos segmentados. Para segmentar as seq??ncias foram utilizadas informa??es de movimento e de cor, sendo que essa ?ltima ? proveniente de um modelo de cor convencional, ou atrav?s de uma metodologia que utiliza a correla??o de Pearson para selecionar os melhores canais para realizar a segmenta??o. A informa??o de movimento foi extra?da atrav?s do c?lculo do fluxo ?ptico entre dois frames adjacentes. Por ?ltimo ? apresentada uma an?lise do comportamento do algoritmo na segmenta??o de seis v?deos e um exemplo de uma aplica??o que utiliza os mapas de segmenta??o para realizar renderiza??es que n?o sejam foto real?sticas
5
Silva, Neto Jos? Francisco da. "Segmenta??o fuzzy de objetos tridimensionais com propriedades texturais." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br/handle/123456789/19668.
Full textAbstract:
Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-01-26T17:46:31Z
No. of bitstreams: 1
JoseFranciscoDaSilvaNeto_DISSERT.pdf: 5950864 bytes, checksum: 5306cd9802b9aa1c09288d75c32ccbe2 (MD5)Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-01-28T18:00:31Z (GMT) No. of bitstreams: 1 JoseFranciscoDaSilvaNeto_DISSERT.pdf: 5950864 bytes, checksum: 5306cd9802b9aa1c09288d75c32ccbe2 (MD5)
Made available in DSpace on 2016-01-28T18:00:31Z (GMT). No. of bitstreams: 1 JoseFranciscoDaSilvaNeto_DISSERT.pdf: 5950864 bytes, checksum: 5306cd9802b9aa1c09288d75c32ccbe2 (MD5) Previous issue date: 2014-09-25
Segmenta??o digital de imagens ? o processo de atribuir r?tulos distintos a diferentes objetos em uma imagem digital, e o algoritmo de segmenta??o fuzzy tem sido utilizado com sucesso na segmenta??o de imagens de diversas modalidades. Contudo, o algoritmo tradicional de segmenta??o fuzzy falha ao segmentar objetos que s?o caracterizados por texturas cujos padr?es n?o podem ser descritos adequadamente por simples estat?sticas computadas sobre uma ?rea restrita. Neste trabalho apresentamos uma extens?o do algoritmo de segmenta??o fuzzy que realiza segmenta??o de texturas empregando fun??es de afinidade adaptativas e o estendemos a imagens tridimensionais. Fun??es de afinidade adaptativas mudam o tamanho da ?rea em que s?o calculados os descritores da textura de acordo com as caracter?sticas da textura processada, enquanto imagens tridimensionais podem ser descritas como um conjunto finito de imagens bidimensionais. O algoritmo ent?o segmenta o volume com uma ?rea apropriada calculada para cada textura, tornando poss?vel obter boas estimativas dos volumes reais das estruturas alvo do processo de segmenta??o. Experimentos ser?o realizados com dados sint?ticos e reais obtidos no estudo de segmenta??o de tumores cerebrais em imagens m?dicas adquiridas atrav?s de exames de Resson?ncia Magn?tica
Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been used successfully in the segmentation of images from several modalities. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper we present an extension of the fuzzy segmentation algorithm that achieves the segmentation of textures by employing adaptive affinity functions as long as we extend the algorithm to tridimensional images. The adaptive affinity functions change the size of the area where they compute the texture descriptors, according to the characteristics of the texture being processed, while three dimensional images can be described as a finite set of two-dimensional images. The algorithm then segments the volume image with an appropriate calculation area for each texture, making it possible to produce good estimates of actual volumes of the target structures of the segmentation process. We will perform experiments with synthetic and real data in applications such as segmentation of medical imaging obtained from magnetic rosonance
6
Siebra, H?lio de Albuquerque. "Segmenta??o fuzzy de imagens coloridas com caracter?sticas texturais: uma aplica??o a rochas sedimentares." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18687.
Full textAbstract:
Made available in DSpace on 2015-03-03T15:47:48Z (GMT). No. of bitstreams: 1
HelioAS_DISSERT.pdf: 11850754 bytes, checksum: c0dc4577693acf33bf104d52950511e6 (MD5)
Previous issue date: 2013-11-08Universidade Federal do Rio Grande do Norte
Image segmentation is the process of labeling pixels on di erent objects, an important step in many image processing systems. This work proposes a clustering method for the segmentation of color digital images with textural features. This is done by reducing the dimensionality of histograms of color images and using the Skew Divergence to calculate the fuzzy a nity functions. This approach is appropriate for segmenting images that have colorful textural features such as geological, dermoscopic and other natural images, as images containing mountains, grass or forests. Furthermore, experimental results of colored texture clustering using images of aquifers' sedimentary porous rocks are presented and analyzed in terms of precision to verify its e ectiveness.
A Segmenta??o de imagens ? o processo de rotulagem de pixels em diferentes objetos, um passo importante em diversos sistemas de processamento de imagens. Este trabalho prop?e um m?todo de agrupamento para a segmenta??o de imagens digitais coloridas com propriedades texturais. Isto ? feito atrav?s da redu??o de dimensionalidade dos histogramas das imagens coloridas e do uso da Diverg?ncia Skew no c?lculo das fun??es de a nidade fuzzy. Esse tipo de abordagem ? adequada ? segmenta??o de imagens coloridas que possuam caracter?sticas texturais, como imagens geol?gicas, dermatosc?picas e outras imagens naturais, como imagens que contenham montanhas, grama ou orestas. Al?m disso, resultados experimentais do agrupamento de texturas coloridas usando imagens de rochas sedimentares porosas s?o apresentados e analisados em termos de precis?o para comprovar sua efetividade
7
Souza, Jackson Gomes de. "T?cnicas de computa??o natural para segmenta??o de imagens m?dicas." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15282.
Full textAbstract:
Made available in DSpace on 2014-12-17T14:55:35Z (GMT). No. of bitstreams: 1
JacksonGS.pdf: 1963039 bytes, checksum: ed3464892d7bb73b5dcab563e42f0e01 (MD5)
Previous issue date: 2009-09-28Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth
Segmenta??o de imagens ? um dos problemas de processamento de imagens que merece especial interesse da comunidade cient?fica. Neste trabalho, s?o estudado m?todos n?o-supervisionados para detec??o de algomerados (clustering) e reconhecimento de padr?es (pattern recognition) em segmenta??o de imagens m?dicas M?todos baseados em t?cnicas de computa??o natural t?m se mostrado bastante atrativos nestas tarefas e s?o estudados aqui como uma forma de verificar a sua aplicabilidade em segmenta??o de imagens m?dicas. Este trabalho trata de implementa os m?todos GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm) PSOKA (Algoritmo de clustering baseado em PSO (Particle Swarm Optimization) e K means) e PSOFCM (Algoritmo de clustering baseado em PSO e FCM (Fuzzy C Means)). Al?m disso, como forma de avaliar os resultados fornecidos pelos algoritmos s?o utilizados ?ndices de valida??o de clustering como forma de medida quantitativa Avalia??es visuais e qualitativas tamb?m s?o realizadas, principalmente utilizando dados do sistema BrainWeb, um gerador de imagens do c?rebro, como ground truth
8
Pinheiro, Daniel Nobre. "O problema de clustering heterog?neo fuzzy: modelos e heur?sticas." PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/22566.
Full textAbstract:
Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-04-03T19:47:15Z
No. of bitstreams: 1
DanielNobrePinheiro_DISSERT.pdf: 900596 bytes, checksum: 82c38f5d0fc71d5fb71fb0c1acd283c6 (MD5)Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-04-06T19:26:01Z (GMT) No. of bitstreams: 1 DanielNobrePinheiro_DISSERT.pdf: 900596 bytes, checksum: 82c38f5d0fc71d5fb71fb0c1acd283c6 (MD5)
Made available in DSpace on 2017-04-06T19:26:01Z (GMT). No. of bitstreams: 1 DanielNobrePinheiro_DISSERT.pdf: 900596 bytes, checksum: 82c38f5d0fc71d5fb71fb0c1acd283c6 (MD5) Previous issue date: 2017-01-27
Este trabalho prop?e formula??es para o Problema de Clustering Heterog?neo Fuzzy, assim como um m?todo heur?stico de Busca em Vizinhan?a Vari?vel para resolv?-lo. O Problema de Clustering Heterog?neo Fuzzy ? um problema de agrupamento de dados modelado em dois n?veis. O primeiro identifica grupos de indiv?duos cujas percep??es acerca dos objetos envolvidos sejam similares. O segundo n?vel identifica parti??es fuzzy de objetos para cada grupo de indiv?duos. O segundo n?vel ? baseado no problema das p-medianas, cujo objetivo ? particionar um conjunto de objetos em subconjuntos menores e definir um objeto para cada subconjunto como mediana, de modo que a soma das dissimilaridades entre cada objeto e sua mediana seja m?nima. O Problema de Clustering Heterog?neo Fuzzy generaliza o problema das p-medianas para ambientes fuzzy, permitindo que os n?veis de pertin?ncia de cada objeto em rela??o a cada cluster sejam fracion?rios. Essa generaliza??o permite novas interpreta??es dos resultados, como a identifica??o de rela??es simult?neas de objetos com diferentes clusters.
This work proposes formulations for the Fuzzy Heterogeneous Clustering Problem, as well as a heuristic method of Variable Neighborhood Search to solve it. The Fuzzy Heterogeneous Clustering Problem is a clustering problem that is formulated in two levels. The first identifies groups of individuals whose perceptions about the objects involved are similar. The second level identifies fuzzy partitions of objects for each group of individuals. The second level is based on the p-median problem, whose objective is to partition a set of objects into smaller subsets and to define an object as median for each subset, such that the sum of dissimilarities between each object and its median is minimal. The Fuzzy Heterogeneous Clustering Problem generalizes the p-median problem to fuzzy environments, allowing the degrees of membership between each object and each cluster to be fractionary. This generalization allows new interpretations about the results, such as the identification of simultaneous relationships of objects with different clusters.
9
Cosme, ?ria Caline Saraiva. "Utilizando mapas de conectividade fuzzy no desenvolvimento de algoritmos reparadores de imagens bin?rias 3D." Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17991.
Full textAbstract:
Made available in DSpace on 2014-12-17T15:47:49Z (GMT). No. of bitstreams: 1
IriaCSC.pdf: 926529 bytes, checksum: aa23848c0d07c85faded67f0781041fc (MD5)
Previous issue date: 2008-08-04A 3D binary image is considered well-composed if, and only if, the union of the faces shared by the foreground and background voxels of the image is a surface in R3. Wellcomposed images have some desirable topological properties, which allow us to simplify and optimize algorithms that are widely used in computer graphics, computer vision and image processing. These advantages have fostered the development of algorithms to repair bi-dimensional (2D) and three-dimensional (3D) images that are not well-composed. These algorithms are known as repairing algorithms. In this dissertation, we propose two repairing algorithms, one randomized and one deterministic. Both algorithms are capable of making topological repairs in 3D binary images, producing well-composed images similar to the original images. The key idea behind both algorithms is to iteratively change the assigned color of some points in the input image from 0 (background)to 1 (foreground) until the image becomes well-composed. The points whose colors are changed by the algorithms are chosen according to their values in the fuzzy connectivity map resulting from the image segmentation process. The use of the fuzzy connectivity map ensures that a subset of points chosen by the algorithm at any given iteration is the one with the least affinity with the background among all possible choices
Uma imagem bin?ria 3D ? considerada bem-composta se, e somente se, a uni?o das faces compartilhadas pelos voxels do foreground e do background da referida imagem ? uma superf?cie em R3 . Imagens bem-compostas se beneficiam de propriedades topol?gicas desej?veis, as quais nos permitem simplificar e otimizar algoritmos amplamente usados na computa??o gr?fica, vis?o computacional e processamento de imagens. Estas vantagens t?m motivado o desenvolvimento de algoritmos para reparar imagens bi e tridimensionais que n?o sejam bem-compostas. Estes algoritmos s?o conhecidos como algoritmos reparadores. Nesta disserta??o, propomos dois algoritmos reparadores, um aleat?rio e um determin?stico. Ambos s?o capazes de fazer reparos topol?gicos em imagens bin?rias 3D, produzindo imagens bem-compostas similares ?s imagens originais. A id?ia fundamental por tr?s de ambos algoritmos ? mudar iterativamente a cor atribu?da de alguns pontos da imagem de entrada de 0 (background) para 1 (foreground) at? a imagem se tornar bem-composta. Os pontos cujas cores s?o mudadas pelos algoritmos s?o escolhidos de acordo com seus valores no mapa de conectividade fuzzy, resultante do processo de segmenta??o da imagem. O uso do mapa de conectividade fuzzy garante que um subconjunto dos pontos escolhidos pelo algoritmo em qualquer itera??o seja um com a menor afinidade com o background dentre todas as escolhas poss?veis
10
Vale, Alessandra Mendes Pacheco Guerra. "T?cnica para segmenta??o autom?tica de imagens microsc?picas de componentes sangu?neos e classifica??o diferencial de leuc?citos baseada em l?gica fuzzy." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br/handle/123456789/19642.
Full textAbstract:
Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-01-20T20:57:18Z
No. of bitstreams: 1
AlessandraMendesPachecoGuerraVale_TESE.pdf: 6083940 bytes, checksum: 50490507cf0394240eea06786d58ff08 (MD5)Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-01-21T19:07:52Z (GMT) No. of bitstreams: 1 AlessandraMendesPachecoGuerraVale_TESE.pdf: 6083940 bytes, checksum: 50490507cf0394240eea06786d58ff08 (MD5)
Made available in DSpace on 2016-01-21T19:07:52Z (GMT). No. of bitstreams: 1 AlessandraMendesPachecoGuerraVale_TESE.pdf: 6083940 bytes, checksum: 50490507cf0394240eea06786d58ff08 (MD5) Previous issue date: 2014-12-26
A detec??o autom?tica de componentes sangu?neos em imagens microsc?picas ? um importante t?pico da ?rea hematol?gica. A segmenta??o permite que os componentes sangu?neos sejam agrupados em ?reas comuns e a classifica??o diferencial dos leuc?citos possibilita que os mesmos sejam analisados separadamente. Com a segmenta??o autom?tica e classifica??o diferencial, contribui-se no processo de an?lise dos componentes sangu?neos, fornecendo ferramentas que propiciem a diminui??o do trabalho manual e o aumento da sua precis?o e efici?ncia. Utilizando t?cnicas de processamento digital de imagens associadas a uma abordagem fuzzy gen?rica e autom?tica, este trabalho apresenta dois Sistemas de Infer?ncia Fuzzy, definidos como I e II, para a segmenta??o autom?tica de componentes sangu?neos e classifica??o diferencial de leuc?citos, respectivamente, em imagens microsc?picas de esfrega?os. Utilizando o Sistema de Infer?ncia Fuzzy I, a t?cnica desenvolvida realiza a segmenta??o da imagem em quatro regi?es: n?cleo e citoplasma leucocit?rios, eritr?citos e ?rea de plasma e utilizando o Sistema de Infer?ncia Fuzzy II e os leuc?citos segmentados (n?cleo e citoplasma leucocit?rios), os classifica diferencialmente em cinco tipos: bas?filos, eosin?filos, linf?citos, mon?citos e neutr?filos. Foram utilizadas para testes 530 imagens contendo amostras microsc?picas de esfrega?os sangu?neos corados com m?todos variados. As imagens foram processadas e seus ?ndices de Acur?cia e Gold Standards foram calculados e comparados com os resultados manuais e com outros resultados encontrados na literatura para os mesmos problemas. Quanto ? segmenta??o, a t?cnica desenvolvida demonstrou percentuais de acur?cia de 97,31% para leuc?citos, 95,39% para eritr?citos e 95,06% para plasma sangu?neo. Quanto ? classifica??o diferencial, os percentuais variaram entre 92,98% e 98,39% para os diferentes tipos leucocit?rios. Al?m de promover a segmenta??o autom?tica e classifica??o diferencial, a t?cnica desenvolvida contribui ainda com defini??o de novos descritores e a constru??o de um banco de imagens utilizando diversos processos de colora??o hematol?gicos
Automatic detection of blood components is an important topic in the field of hematology. The segmentation is an important stage because it allows components to be grouped into common areas and processed separately and leukocyte differential classification enables them to be analyzed separately. With the auto-segmentation and differential classification, this work is contributing to the analysis process of blood components by providing tools that reduce the manual labor and increasing its accuracy and efficiency. Using techniques of digital image processing associated with a generic and automatic fuzzy approach, this work proposes two Fuzzy Inference Systems, defined as I and II, for autosegmentation of blood components and leukocyte differential classification, respectively, in microscopic images smears. Using the Fuzzy Inference System I, the proposed technique performs the segmentation of the image in four regions: the leukocyte?s nucleus and cytoplasm, erythrocyte and plasma area and using the Fuzzy Inference System II and the segmented leukocyte (nucleus and cytoplasm) classify them differentially in five types: basophils, eosinophils, lymphocytes, monocytes and neutrophils. Were used for testing 530 images containing microscopic samples of blood smears with different methods. The images were processed and its accuracy indices and Gold Standards were calculated and compared with the manual results and other results found at literature for the same problems. Regarding segmentation, a technique developed showed percentages of accuracy of 97.31% for leukocytes, 95.39% to erythrocytes and 95.06% for blood plasma. As for the differential classification, the percentage varied between 92.98% and 98.39% for the different leukocyte types. In addition to promoting auto-segmentation and differential classification, the proposed technique also contributes to the definition of new descriptors and the construction of an image database using various processes hematological staining
Conference papers on the topic "Fuzzy segmentace"
1
Sampaio, Filipe A., and Rodrigo M. S. Veras. "Detecção e Segmentação Automática de Estruturas em Imagens de Exames Oftalmológicos." In Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação (SBC), 2020. http://dx.doi.org/10.5753/sbcas.2020.11568.
Full textAbstract:
Este artigo propõe um método semiautomático para segmentar lesões em imagens da córnea, visando auxiliar os especialistas no monitoramento da evolução da lesão. Tais lesões podem ser categorizadas como patologias que afetam a estrutura ocular na forma de ulceração, infecção, erosão ou algum outro tipo de trauma. Assim, o método proposto utiliza regiões marcadas pelo especialista para treinar o classificador Random Forest e o agrupador semisupervisionado Seed Fuzzy C-means. Na extração de atributos, foi realizado agrupamentos de pixels com textura similar, chamado superpixel. Assim, foi obtido bons resultados com as métricas sensibilidade: 99,48% e similaridade Dice: 80,03%. Por fim, concluimos que os resultados mostraram que o classificador Random Forest obteve um melhor desempenho.