Analisis Abstrak dan Melakukan Klasifikasi

Assalamulaikum warahmatullahi wabarakatuh

wah sudah lama tidak aktif dalam dunia bloging. tapi hari ini saya akan mengerjakan tugas kuliah dari dosen metodelogi penelitian walaupun baru hari pertama masuk ke rutinitas kuliah kembali. tugas ini adalah melakukan klasifikasi pada abstract beliau dengan memisahkan antara latar belakang, tujuan, metode yang digunakan, dan hasil dari abstract beliau seperti dibawah :

Edge-based active contourmodels are effective in segmenting

images with intensity inhomogeneity but often fail when

applied to images containing poorly defined boundaries, such as

in medical images. Traditional edge-stop functions (ESFs) utilize

only gradient information, which fails to stop contour evolution at

such boundaries because of the small gradient magnitudes. To address

this problem, we propose a framework to construct a group

of ESFs for edge-based active contour models to segment objects

with poorly defined boundaries. In our framework, which incorporates

gradient information as well as probability scores from a

standard classifier, the ESF can be constructed fromany classification

algorithm and applied to any edge-based model using a level

setmethod. Experiments onmedical images using the distance regularized

level set for edge-based active contour models as well as

the k-nearest neighbours and the support vector machine confirm

the effectiveness of the proposed approach.

Dibawah Ini Adalah Hasil Analisa dari abstract diatas

1. Latar belakang

Edge-based active contourmodels are effective in segmenting
images with intensity inhomogeneity but often fail when
applied to images containing poorly defined boundaries, such as
in medical images. Traditional edge-stop functions (ESFs) utilize
only gradient information, which fails to stop contour evolution at
such boundaries because of the small gradient magnitudes.

2.Tujuan Penelitian 

 To address this problem, we propose a framework to construct a group
of ESFs for edge-based active contour models to segment objects
with poorly defined boundaries

3. Metode Penelitian

In our framework, which incorporates
gradient information as well as probability scores from a
standard classifier, the ESF can be constructed fromany classification
algorithm and applied to any edge-based model using a level
setmethod

4. kesimpulan

Experiments onmedical images using the distance regularized
level set for edge-based active contour models as well as
the k-nearest neighbours and the support vector machine confirm
the effectiveness of the proposed approach.

mungkin cuma sekian yang bisa saya analisis dari abstract dari bapak agus. untuk melihat dokumen selengkapnya bisa dilihat di http://ieeexplore.ieee.org/document/7353157/
dan sekian terima kasih.