Reasearch Topics: Computer Vision in Agricultural Engineering and Biodiversity
Video Capsule Endoscopy Analysis |
Vision-based
system
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Human Computer Interaction |
Human in a Surveillance Camera Network |
| Multi-organ Plant Identication | |
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In this paper, we describe the system including image preprocessing, feature descriptor extraction, classi cation method and fusion techniques that we applied for LifeCLEF 2015 - multi-organ plant identication task. In the preprocessing step, we apply relevant preprocessing techniques for each type of plants' organs based on the characteristic of the organs. For the feature descriptor, we propose to use kernel descriptor (KDES) with di erent types of kernel for all organs types. For ower and entire images, we combine KDES with HSV histogram. At the image level, we apply Support Vector Machine (SVM) as a classi cation method. Finally, we investigate di erent late fusion techniques in order to build the retrieved observation list. |
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[1] LE Thi Lan, DUONG Nam Duong, Hai Vu, NGUYEN Thi Thanh Nhan, "MICA at LifeCLEF 2015: Multi-organ Plant Identication", CLEF 2015 Working Notes proceedings - 2015 [2] Duong Nam Duong, Thi-Lan Le, Hai Vu, "A Web-based Plant Identification Application Using Multi-Organ Images Query", in the Proceeding of the Regional Conference on Computer and Information Engineering (RCCIE), Hanoi, Oct., 2015 |
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| Complex background leaf-based plant identification method based on interactive segmentation and kernel descriptor |
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This paper presents a
plant identification method from the images of the simple leaf with
complex background. In order to extract leaf from the image, we
firstly develop an interactive image segmentation for mobile device
with tactile screen. This allows to separate the leaf region from
the complex background image in few manipulations. Then, we extract the kernel descriptor from the leaf region to build leaf representation. Since the leaf images may be taken at different scale and rotation levels, we propose two improvements in kernel descriptor extraction that makes the kernel descriptor to be robust to scale and rotation. Experiments carried out on a subset of ImageClef 2013 show an important increase in performance compared to the original kernel descriptor and automatic image segmentation. |
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[2] NGUYEN Thi Thanh Nhan, LE Thi Lan, Hai Vu, NGUYEN Huy Hoang, HOANG Van Sam, "A Combination of Deep Learning and Hand-Designed Feature for Plant Identification based on Leaf and Flower Images ", in the Proceeding of the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS), Japan - 2017 [1] Le Thi Lan, Duong Nam Duong, Nguyen Van Toi, Hai Vu, Van-Nam Hoang, Thi Thanh Nhan Nguyen, "Complex background leaf-based plant identification method based on interactive segmentation and kernel descriptor" , in the Proceeding of the 2nd International Workshop on Environmental Multimedia Retrieval (EMR 2015), Conjunction with ACM Conference on Multimedia Retrieval (ICMR), China, 2015 |
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| Rice seed quality inspection using Vision-based techniques |
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This paper presents a system for automated classification of rice variety for rice seed production using computer vision and image processing techniques. Rice seeds of different varieties are visually very similar in color, shape and texture that make the classification of rice seed varieties at high accuracy challenging. We investigated various feature extraction techniques for efficient rice seed image representation. We analyzed the performance of powerful classifiers on the extracted features for finding the robust one. Images of six different rice seed varieties in northern Vietnam were acquired and analyzed. Our experiments have demonstrated that the average accuracy of our classification system can reach 90.54% using Random Forest method with a simple feature extraction technique. This result can be used for developing a computer-aided machine vision system for automated assessment of rice seeds purity. |
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[1] Hai Vu, Christos Tachtatzis, Paul Murray, David Harle, Trung Kien Dao, Thi Lan Le, Ivan Andonovic, Stephen Marshall, "Spatial and Spectral Features Utilization on a HyperSpectral Imaging System for Rice Seed Varietal Purity Inspection", in the Proceeding of the 12th IEEE RIVF International Conference on Computing and Communication Technology, HaNoi, Vietnam, November 7-9, 2016. [2] Hai Vu, Christos Tachtatzis, Paul Murray, David Harle, Trung Kien Dao, Thi Lan Le, Ivan Andonovic, Stephen Marshall, "Rice seed varietal purity inspection using hyperspectral imaging", in the Proceeding of the fifth Hyperspectral Imaging and Applications Conference (HSI 2016), Conventry, UK [2] Phan Thi Thu Hong, Tran Thi Thanh Hai, Le Thi Lan, Vo Ta Hoang, Hai Vu, Thuy Thi Nguyen,"Comparative Study on Vision Based Rice Seed Varieties Identification", in the Proceeding of The 1st International Workshop on Pattern Recognition for Multimedia Content Analysis (PR4MCA 2015), in conjunction with the 7th International Conference on Knowledge and System Engineering - 2015 |
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Multimodal Vietnamese medicinal plants retrieval system |
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Previous studies have found
that Vietnam has a rich source of medicinal plants and the
Vietnamese people has used these plants as a traditional medicine in
an efficient and safe manner. However, on one side, not all
people know and could recognize medicinal plants in reality. On the
other, even being able to recognize it, it is not easy to access all
the plant information. In this paper, we present a multimodal Vietnamese medicinal plants retrieval system. Our main objective is to bring to users a robust means to retrieve Vietnamese medicinal plants. Our system provides three main possibilities for searching a plant of interest: a conventional keyword based search engine, a fully automatic leaf images based plant identification and interactive plant identification with aids of the graphical tool. These search modes are integrated inside a generic framework with interactive user interface that facilitates the use of the system on a common Android platform. |
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A Vision-based method for automatizing tea shoots detection |
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Counting tender tea shoots in a sampled area is required before making a decision for plucking. However, it is a tedious task and requires a large amount of time. In this paper, we propose a vision-based method for automatically detecting and counting the number of tea shoots in an image acquired from a tea field. First, we build a parametric model of a tea-shoot’s color distribution in order to roughly separate Regions-of-Interest (ROIs) of tea shoots from a complicated background. For each ROI, we then extract supportive (local) features with expectations that these features will only appear around an apical bud of tea shoots thanks to two measurements: the density of edge pixels and a statistic of gradient directions. Consequently, the extracted features are put into a mean shift cluster to locate the position of tea shoots. The proposed method is evaluated on a set of testing images with different species of tea plants and ages. The results show 86% correct tea shoots detected, whereas 25% of a false alarm rate exists. It offers an elegant way to build an assisting tool for tea harvesting |
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