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Author

Le Van Chung

Bio: Le Van Chung is an academic researcher from Duy Tan University. The author has contributed to research in topics: Facial recognition system & Image segmentation. The author has an hindex of 3, co-authored 3 publications receiving 100 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
Abstract: Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.

102 citations

Book ChapterDOI
TL;DR: The proposed online video contextual advertisement user-oriented system is a combination of video-based face recognition using machine learning models from the camera with multimedia communications and networking streaming architecture using Meta-data structure to video data storage.
Abstract: In this research, we propose the online video contextual advertisement user-oriented system. Our system is a combination of video-based face recognition using machine learning models from the camera with multimedia communications and networking streaming architecture using Meta-data structure to video data storage. The real images captured by the camera will be analyzed based on predefined set of conditions to determine the appropriate object classes. Based on the defined object class, the system will access the multimedia advertising contents database and automatically select and play the appropriate contents. We analyse existing face recognition in videos and age estimation from face images approaches. Our experiment was analyzed and evaluated in performance when we integrate analyze age from the face identification in order to select the optimal approach for our system.

9 citations

Journal ArticleDOI
TL;DR: The authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition that can be easily implemented and without any priori information of features.
Abstract: Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.

5 citations


Cited by
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Journal ArticleDOI
TL;DR: This study attempts to provide a comprehensive review of the fundamental processes required for change detection with a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.
Abstract: Change detection captures the spatial changes from multi temporal satellite images due to manmade or natural phenomenon. It is of great importance in remote sensing, monitoring environmental changes and land use –land cover change detection. Remote sensing satellites acquire satellite images at varying resolutions and use these for change detection. This paper briefly analyses various change detection methods and the challenges and issues faced as part of change detection. Over the years, a wide range of methods have been developed for analyzing remote sensing data and newer methods are still being developed. Timely and accurate change detection of Earth’s surface features provides the basis for evaluating the relationships and interactions between human and natural phenomena for the better management of resources. In general, change detection applies multi-temporal datasets to quantitatively analyse the temporal effects of the phenomenon. As such, this study attempts to provide a comprehensive review of the fundamental processes required for change detection. The study also gives a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.

196 citations

Journal ArticleDOI
TL;DR: A hybrid semi-automated image processing methodology is proposed to inspect the ischemic stroke lesion using the MRI recorded with flair and diffusion-weighted modality to estimate the stroke severity and also to plan for further treatment process.
Abstract: Stroke is one of the widespread causes of morbidity worldwide and is also the foremost reason for attained disability in human community. Ischemic stroke can be confirmed by investigating the interior brain regions. Magnetic resonance image (MRI) is one of the noninvasive imaging techniques widely adopted in medical discipline to record brain malformations. In this paper, a hybrid semi-automated image processing methodology is proposed to inspect the ischemic stroke lesion using the MRI recorded with flair and diffusion-weighted modality. The proposed approach consists of two sections, namely the preprocessing based on the social group optimization monitored Fuzzy-Tsallis entropy and post-processing technique, which consists of a segmentation algorithm to extract the ISL from preprocessed image in order to estimate the stroke severity and also to plan for further treatment process. The proposed hybrid approach is experimentally investigated using the ischemic stroke lesion segmentation challenge database. This work also presents a detailed investigation among well-known segmentation approaches, like watershed algorithm, region growing technique, principal component analysis, Chan–Vese active contour, and level set approaches, existing in the literature. The results of the experimental work executed using ISLES 2015 challenge dataset confirm that proposed methodology offers superior average values for image similarity indices like Jaccard (78.60%), Dice (88.54%), false positive rate (3.69%), and false negative rate (11.78%). This work also helps to achieve improved value of sensitivity (99.65%), specificity (78.05%), accuracy (91.17%), precision (98.11%), BCR (90.19%), and BER (6.09%).

107 citations

Journal ArticleDOI
TL;DR: A two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities is proposed and it is confirmed that AC offers enhanced results compared with other segmentation procedures considered in this article.

61 citations

Journal ArticleDOI
TL;DR: The experimental study established that the proposed two stage approach extracted efficiently the contrast enhanced regions from the MRA and T1C brain images.

57 citations