NDLI: Automatic object extraction from images using deep neural networks and the level-set method

Content Provider	IET Digital Library
Author	Wu, Kan Yu, Yizhou
Abstract	The authors propose an automatic method for extracting objects with fine quality from photographs. The authors’ method starts with finding bounding boxes that enclose potential objects, which is achievable by state-of-the-art object proposal methods. To further segment objects within obtained bounding boxes, the authors propose a new multi-pass level-set method based on saliency detection and foreground pixel classification. The level-set function is initially constructed with respect to the automatically detected salient parts within the bounding box, which eliminates potential user interaction and predicts an initial set of pixels on the object. The input features for foreground pixel classifiers are constructed as a combination of classical texture features from the Gabor filter banks and convolutional features from a pre-trained deep neural network. Through multi-pass evolution of the level-set function and re-training of the foreground pixel classifier, the authors’ method is able to overcome possible inaccuracies in the initial level-set function and converge to the real object boundary.
Starting Page	1131
Ending Page	1141
Page Count	11
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 7, Jul (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1144
Journal	IET Image Processing
Publisher Date	2018-02-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Method Automatic Object Extraction Automatically Detected Salient Parts Classical Texture Features Computer Vision And Image Processing Technique Convolutional Features Feature Extraction Filtering Method in Signal Processing Foreground Pixel Classification Foreground Pixel Classifier Gabor Filter Gabor Filter Banks Image Filtering Image Recognition Image Texture Initial Level-set Function Level-set Method Multipass Evolution Multipass Level-set Method Neural Computing Technique Neural Nets Object Detection Object Segmentation Obtained Bounding Boxes Pre-trained Deep Neural Network Real Object Boundary Saliency Detection
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Efficient image segmentation method based on an adaptive selection of Gabor filters

Interest point detection method based on multi-scale Gabor filters

Multi-scale deep neural network for salient object detection

Palm vein recognition scheme based on an adaptive Gabor filter

Directional relative total variation for structure–texture decomposition

Bio-inspired contour detection model based on multi-bandwidth fusion and logarithmic texture inhibition

Salient object detection via reciprocal function filter

Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis

Orthogonal curved-line Gabor filter for fast fingerprint enhancement

Automatic object extraction from images using deep neural networks and the level-set method

Similar Documents

Efficient image segmentation method based on an adaptive selection of Gabor filters

Interest point detection method based on multi-scale Gabor filters

Multi-scale deep neural network for salient object detection

Palm vein recognition scheme based on an adaptive Gabor filter

Directional relative total variation for structure–texture decomposition

Bio-inspired contour detection model based on multi-bandwidth fusion and logarithmic texture inhibition

Salient object detection via reciprocal function filter

Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis

Orthogonal curved-line Gabor filter for fast fingerprint enhancement

Automatic object extraction from images using deep neural networks and the level-set method