CAD Model-based 3D Object Pose Estimation using an Edge-Based Nonlinear Model Fitting Algorithm

[[abstract]]This paper addresses the design of a model-based 3D object pose estimation algorithm, which is one of the major techniques to develop a robust robotic vision system using a monocular camera. The proposed system first extracts line features of a captured image by using edge detection and Hough transform techniques. Given a CAD model of the object-of-interest, the 6-DOF pose of the object can then be estimated via a novel edge-based nonlinear model fitting algorithm, which is a nonlinear optimization process for estimating the optimal object pose based on an edge-based distance metric. Experimental results validate the performance of the proposed system.[[notice]]補正完

A novel simultaneous dynamic range compression and local contrast enhancement algorithm for digital video cameras

[[abstract]]This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE) is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.[[incitationindex]]SCI[[booktype]]電子

SDALA: Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm

[[abstract]]This paper presents a novel simultaneous dynamic range compression and local contrast enhancement algorithm, termed as SDALA, to resolve low dynamic range (LDR) image enhancement problem. The proposed SDALA is able to combine with any differentiable intensity transfer function, which greatly increases the applicability of the proposed method. Moreover, the proposed method can separately control the level of enhancement on the overall lightness and contrast achieved at the output. Experimental results validate the performance of the proposed method by comparing with two existent methods, both quantitatively and visually.[[conferencetype]]國際[[conferencedate]]20110911~20110914[[conferencelocation]]Brussels, Belgiu

Real-time automatic multilevel color video thresholding using a novel class-variance criterion

[[abstract]]Color image segmentation is a crucial preliminary task in robotic vision systems. This paper presents a novel automatic multilevel color thresholding algorithm to address this task efficiently. The proposed algorithm consists of a learning process and a multi-threshold searching process. The learning process learns the color distribution of an input video sequence in HSV color space, and the multi-threshold searching process automatically determines the optimal multiple thresholds to segment all colors-of-interest in the video based on a novel class-variance criterion. For the learning process, a simple and efficient color-distribution learning algorithm operating with a color-pixel extraction method is proposed to learn a color distribution model of all colors-of-interest in the video images, which simplifies the search for optimal thresholds for the colors-of-interest through a conventional multilevel thresholding method. For the multi-threshold searching process, a nonparametric multilevel color thresholding algorithm with an extended within-class variance criterion is proposed to automatically find the optimal upper bound and lower bound threshold values of each color channel. Experimental results validate the performance and computational efficiency of the proposed method by comparing with three existing methods, both visually and quantitatively.[[booktype]]紙

A Novel Translation, Rotation, and Scale-Invariant Shape Description Method for Real-Time Speed-Limit Sign Recognition

[[abstract]]Speed-limit sign (SLS) recognition is an important function to realize automatic driving assistance systems (ADAS). This paper presents a novel design of an image-based SLS recognition algorithm, which can efficiently detect and recognize SLS in real-time. To improve the robustness of the proposed SLS algorithm, this paper also proposes a new shape description method to describe the detected SLS using centroid-to-contour (CtC) distances of the sign content. The proposed CtC descriptor is invariant to translation, rotation, and scale variations of the SLS in the image. This advantage increases the recognition rate of a linear support vector machine classifier. The proposed SLS recognition method had been implemented and tested on an ARM-based embedded platform. Experimental results validate the SLS recognition accuracy and real-time performance of the proposed method.[[notice]]補正完

Real-Time Feature Descriptor Matching via a Multi-Resolution Exhaustive Search Method

[[abstract]]Feature descriptor matching plays an important role in many computer vision applications. This paper presents a novel fast linear exhaustive search algorithm combined with a multi-resolution candidate elimination technique to deal with this problem efficiently. The proposed algorithm is inspired from the existing multi-resolution image retrieval approaches, but releasing the requirement on a norm-sorted database with pre-computed multi-resolution tables. This helps to increase the applicability of the proposed method. Moreover, the computations of candidate elimination are fully performed using a simple L1 distance metric, which is able to speedup the entire search process without loss of accuracy. This property leads to an accurate feature descriptor matching algorithm with real-time performance, which will be validated in the experiments by testing with the matching of SURF descriptors.[[booktype]]紙

Design and Validation of an Augmented Reality Teaching System for Primary Logic Programming Education

[[abstract]]Programming is a skill that requires high levels of logical thinking and problem-solving abilities. According to the Curriculum Guidelines for the 12-Year Basic Education currently implemented in Taiwan, programming has been included in the mandatory courses of middle and high schools. Nevertheless, the guidelines simply recommend that elementary schools conduct fundamental instructions in related fields during alternative learning periods. This may result in the problem of a rough transition in programming learning for middle school freshmen. To alleviate this problem, this study proposes an augmented reality (AR) logic programming teaching system that combines AR technologies and game-based teaching material designs on the basis of the fundamental concepts for seventh-grade structured programming. This system can serve as an articulation curriculum for logic programming in primary education. Thus, students are able to develop basic programming logic concepts through AR technologies by performing simple command programming. This study conducted an experiment using the factor-based quasi-experimental research design and questionnaire survey method, with 42 fifth and sixth graders enrolled as the experimental subjects. The statistical analysis showed the following results: In terms of learning effectiveness, both AR-based and traditional learning groups displayed a significant performance. However, of the two groups, the former achieved more significant effectiveness in the posttest results. Regarding learning motivation, according to the evaluation results of the Attention, Relevance, Confidence, and Satisfaction (ARCS) motivation model, the AR-based learning group manifested significantly higher levels of learning motivation than the traditional learning group, with particularly significant differences observed in the dimension of Attention. Therefore, the experimental results validate that the proposed AR-based logic programming teaching system has significant positive effects on enhancing students’ learning effectiveness and motivation.[[notice]]補正完

Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning

[[abstract]]This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.[[notice]]補正完

Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms

[[abstract]]Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.[[notice]]補正完

Design and Validation of a Virtual Chemical Laboratory – An Example of Natural Science in Elementary Education

[[abstract]]In the natural science curriculum, chemistry is a very important domain. However, when conducting chemistry experiments, safety issues need to be taken seriously, and excessive material waste may be caused during the experiment. Based on the 11-year-old student science curriculum, this paper proposed a virtual chemistry laboratory, which was designed by combining a virtual experiment application with physical teaching materials. The virtual experiment application was a virtual experiment laboratory environment created by using selected experimental equipment cards in combination with augmented reality (AR) technology. The physical teaching materials included all virtual equipment required for experiment units. Each piece of equipment had corresponding cards for learners to choose from and utilize in specific experimental operations. It was hoped that students were able to achieve the desired learning effectiveness of experimental teaching while reducing the waste of experimental materials through the virtual experimental environment. This study employed the quasi-experimental and questionnaire survey methods to evaluate both learning effectiveness and learning motivation. Eighty-one students and eight elementary school teachers were surveyed as research subjects. The experimental results revealed that significant differences in learning effectiveness existed between the experimental group and control group, indicating that the application of AR technology to teaching substantively helped enhance students’ learning effectiveness and motivation. In addition, the results of the teacher questionnaire demonstrated that the virtual chemistry laboratory proposed in this study could effectively assist with classroom teaching.[[notice]]補正完