Hai semuanya, pada
kesempatan kali ini saya akan menjelaskan beberapa poin yang terdapat pada
abstrak sebuah dokumen atau laporan dan juga menunjukan contohnya yg saya ambil
dari link: http://ieeexplore.ieee.org/document/7353157/.
Berikut merupakan poin poin yg terdapat pada abstraksi sebuah laporan atau
dokumen.
- . Latar Belakang
Latar belakang
merupakan penjelasan tentang sesuatu permasalahan yang akan kita angkat menjadi
sebuah rumusan masalah dan menyelesaikannya. Contohnya seperti kalimat yg
digaris bawahi berikut ini:
Edge-based active contour models 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
from any classification algorithm and applied to any edge-based model using a
level set method. Experiments on medical 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.
- . Tujuan Penelitian
Tujuan
Penelitian merupakan solusi penyelesaian atau hasil akhir yang akan kita
dapatkan. Contohnya seperti kalimat yg digaris bawahi berikut ini:
Edge-based
active contour models 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 from any classification algorithm and applied to any
edge-based model using a level set method (method). Experiments on medical
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.
- . Metode yang digunakan
Merupakan
tahapan atau proses yang akan digunakan untuk
menyelesaikan permasalahan. Contohnya seperti kalimat yg digaris bawahi berikut
ini:
Edge-based
active contour models 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 from any classification algorithm and applied to any
edge-based model using a level set method. Experiments on medical
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.
- . Kesimpulan/Hasil
Merupakan
Hasil yg kita dapatkan dari peyelesaian masalah tersebut.
Contohnya seperti
kalimat yg digaris bawahi berikut ini:
Edge-based
active contour models 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 from any
classification algorithm and applied to any edge-based model using a level set
method. Experiments on medical 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.
Berikut merupakan penjelasan
mengenai poin poin apa saja yang harus ada pada abstrak.
