Van-Toi Nguyen

Complex Background Leaf-based Plant Identification Method Based on Interactive Segmentation and Kernel Descriptor

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.

A new hand representation based on kernels for hand posture recognition

Hand posture recognition is an extremely active research topic in Computer Vision and Robotics, with many applications ranging from automatic sign language recognition to human-system interaction. Recently, a new descriptor for object representation based on the kernel method (KDES) has been proposed. While this descriptor has been shown to be efficient for hand posture representation, across-the-board use of KDES for hand posture recognition has some drawbacks. This paper proposes three improvements to KDES to make it more robust to scale change, rotation, and differences in the object structure. First, the gradient vector inside the gradient kernel is normalized, making gradient KDES invariant to rotation. Second, patches with adaptive size are created, to make hand representation more robust to changes in scale. Finally, for patch-level features pooling, a new pyramid structure is proposed, which is more suitable for hand structure. These innovations are tested on three datasets; the results bring out an increase in recognition rate (as compared to the original method) from 84.4% to 91.2%.

An Efficient Combination of RGB and Depth for Background Subtraction

This paper describes a new method for background subtraction using RGB and depth data from a Microsoft Kinect sensor. In the first step of the proposed method, noises are removed from depth data using the proposed noise model. Denoising procedure help improving the performance of background subtraction and also avoids major limitations of RGB mostly when illumination changes. Background subtraction then is solved by combining RGB and depth features instead of using individual RGB or depth data. The fundamental idea in our combination strategy is that when depth measurement is reliable, the background subtraction from depth taken priority over all. Otherwise, RGB is used as alternative. The proposed method is evaluated on a public benchmark dataset which is suffered from common problems of the background subtraction such as shadows, reflections and camouflage. The experimental results show better performances in comparing with state-of-the-art. Furthermore, the proposed method is successful with a challenging task such as extracting human fall-down event in a RGB-D image sequence. Therefore, the foreground segmentation is feasibility for the further task such as tracking and recognition.

A combination of user-guide scheme and kernel descriptor on RGB-D data for robust and realtime hand posture recognition

This paper presents a robust and real-time hand posture recognition system. To obtain this, key elements of the proposed system contain an user-guide scheme and a kernel-based hand posture representation. We firstly describe a three-stage scheme to train an end-user. This scheme aims to adapt environmental conditions (e.g., background images, distance from device to hand/human body) as well as to learn appearance-based features such as hand-skin color. Thanks to the proposed user-guide scheme, we could precisely estimate heuristic parameters which play an important role for detecting and segmenting hand regions. Based on the segmented hand regions, we utilize a kernel-based hand representation in which three levels of feature are extracted. Whereas pixel-level and patch-level are conventional extractions, we construct image-level which presents a hand pyramid structure. These representations contribute to a Multi-class support vector machine classifier. We evaluate the proposed system in term of the learning time versus the robustness and real time performances. Averagely, the proposed system requires 14s in advanced to guide an end-user. However, the hand posture recognition rate obtains 91.2% accuracy. Performance of the proposed system is comparable with state-of-the-art methods (e.g. Pisharady et al., 2012) but it is a real time system. To recognize a posture, its computational cost is only 0.15s. This is significantly faster than works in Pisharady et al. (2012), which required approximately 2min. The proposed methods therefore are feasible to embed into smart devices, particularly, consumer electronics in domain of home-automation such as televisions, game consoles, or lighting systems.

An Evaluation of Hand Pyramid Structure for Hand Representation Based on Kernels

Hand posture recognition is an active research topic in computer vision and robotics with many applications ranging from automatic sign language recognition to human-system interaction. Recently, we have proposed a new descriptor for hand representation based on the kernel method (KDES) [1]. Our new descriptor inherits the main idea of KDES but we proposed three improvements to make it more robust. One of the improvements was that we introduced a new hand pyramid structure [14]. Intuitively, hand pyramid is more suitable to hand structure than conventional pyramid. In our previous work, we have demonstrated that the combination of improvements to KDES gives more accurate hand posture classification than using original KDES. However, it still lacks discussions and experimental evidences of the contribution of hand pyramid for hand representation. In this paper, we build specific hand dataset and conduct more experiments to show how hand pyramid contributes for hand representation. We will discuss deeply on the results and analyze the impact of this pyramid on hand posture classification.

Using Hand Postures for Interacting with Assistant Robot in Library

Visual interpretation of hand gesture for human-system interaction in general and human-robot interaction in particular is becoming a hot topic in computer vision and robotics fields. Hand gestures provide very intuitive and efficient means and enhance the flexibility in communication. Even a number of works have been proposed for hand gesture recognition, the use of these works for real human-robot interaction is still limited. Based on our previous works for hand detection and hand gesture recognition, we have built a fully automatic hand gesture recognition system and have applied it in a human-robot interaction application: service robot in library. In this paper, we describe in detail this application from the user requirement analysis to system deployment and experiments with end-users.

A method for hand detection using internal features and active boosting-based learning

Hand posture recognition has important applications in sign language, human machine interface, etc. In most such systems, the first and important step is hand detection. This paper presents a hand detection method based on internal features in an active boosting-based learning framework. The use of efficient Haar-like, local binary pattern and local orientation histogram as internal features allows fast computation of informative hand features for dealing with a great variety of hand appearances without background interference. Interactive boosting-based on-line learning allows efficiently training and improvement for the detector. Experimental results show that the proposed method outperforms the conventional methods on video data with complex background while using a smaller number of training samples. The proposed method is reliable for hand detection in the hand posture recognition system.