Moonsoo Ra

Hydrochromic conjugated polymers for human sweat pore mapping

Hydrochromic materials have been actively investigated in the context of humidity sensing and measuring water contents in organic solvents. Here we report a sensor system that undergoes a brilliant blue-to-red colour transition as well as ‘Turn-On’ fluorescence upon exposure to water. Introduction of a hygroscopic element into a supramolecularly assembled polydiacetylene results in a hydrochromic conjugated polymer that is rapidly responsive (<20 μs), spin-coatable and inkjet-compatible. Importantly, the hydrochromic sensor is found to be suitable for mapping human sweat pores. The exceedingly small quantities (sub-nanolitre) of water secreted from sweat pores are sufficient to promote an instantaneous colorimetric transition of the polymer. As a result, the sensor can be used to construct a precise map of active sweat pores on fingertips. The sensor technology, developed in this study, has the potential of serving as new method for fingerprint analysis and for the clinical diagnosis of malfunctioning sweat pores.

Lane detection system with around view monitoring for intelligent vehicle

A lane detection system using around view monitoring (AVM) images is presented in this paper. To provide safe driving condition, many lane detection approaches have been proposed. However, previous approaches cannot detect lanes stably in low visibility condition such as foggy or rainy days because of the use of frontal camera. The proposed lane detection system uses ego-vehicles surrounding road information to overcome this problem. The proposed method can be split into two stages: generation of AVM images from four fisheye cameras and lane detection using AVM images. To generate AVM images, we use four fisheye cameras mounted on sides, front, and rear of the vehicle. Top-view images covering the surround area of the vehicle are generated from four fisheye images by calibrations of each camera and their relative camera pose. The lane detection procedure consists of detecting and grouping lane responses, fitting lane responses by a linear model, and tracking lanes with Kalman filter to smooth the estimates. Experimental results on full lanes and dashed lanes show that the proposed method can achieve the detection accuracies of 98.78% and 90.88% respectively and processing speed of 1 ms per frame in a desktop computer.

Adaptive Potential guided directional-RRT

The Rapidly Exploring Random Tree Star (RRT*) is an extension of the Rapidly Exploring Random Tree path finding algorithm. RRT* guarantees an optimal, collision free path solution but is limited by slow convergence rates and inefficient memory utilization. This paper presents APGD-RRT*, a variant of RRT* which utilizes Artificial Potential Fields to improve RRT* performance, providing relatively better convergence rates. Simulation results under different environments between the proposed APGD-RRT* and RRT* algorithms demonstrate this marked improvement under various test environments.

Human tracking by a mobile robot using 3D features

Detection and Tracking of human being is a very important problem in Computer Vision. Human robot interaction is a very essential need for service robots where robots are required to detect and track human beings in order to provide the required service. In this paper we present an improved novel approach for tracking a target person in crowded environment. We used multi-sensor data fusion approach by combining the data of stereo camera and laser rangefinder (LRF) to perform human tracking. The system gathers the features of human upper body, face and legs in the target person selection phase and then the robot will start following the target person. Camera is used for upper body and face detection while laser rangefinder is used for gathering legs data. Template matching and triangulation is done in order to get the dimensions of upper body and face. Target person tracking is done using Cam shift tracker. Thus our method presents a novel approach that uses all these techniques to track a target person in a crowded environment.

Effective Trajectory Similarity Measure for Moving Objects in Real-World Scene

Trajectories of moving objects provide fruitful information for analyzing activities of the moving objects; therefore, numerous researches have tried to obtain semantic information from the trajectories by using clustering algorithms. In order to cluster the trajectories, similarity measure of the trajectories should be defined first. Most of existing methods have utilized dynamic programming (DP) based similarity measures to cope with different lengths of trajectories. However, DP based similarity measures do not have enough discriminative power to properly cluster trajectories from the real-world environment. In this paper, an effective trajectory similarity measure is proposed, and the proposed measure is based on the geographic and semantic similarities which have a same scale. Therefore, importance of the geographic and semantic information can be easily controlled by a weighted sum of the two similarities. Through experiments on a challenging real-world dataset, the proposed measure was proved to have a better discriminative power than the existing method.

Triangular geometry based optimal motion planning using RRT*-motion planner

RRT* is a recent and improved variant of the RRT path finding algorithm. While RRT concentrates on simply finding an initial obstacle-free path, RRT* guarantees eventual convergence to an optimum, collision-free path for any given geometrical environment. On the other hand, the main limitations of RRT* include its slow processing rate and high memory utilization due to the large number of iterations required to achieve optimal path solution. This paper presents Triangular Geometerised-RRT* (TG-RRT*) which incorporates Triangular geometrical methods in the RRT* algorithm and improves its processing time by decreasing the number of iterations required for optimal solution. Simulation results under different environments demonstrate an improved convergence rate of TG-RRT*, in comparison with RRT*.

A novel design of urine test strip with spatially adjacent reference color chart

Urine test strips are frequently used to analyze health status of patients due to its simplicity. However, in order to analyze the urine test strip, expensive hardware is required to match changed colors. In order to solve the problem, a novel design of the urine test strip is proposed. By using the proposed design, extra equipment is not required and the proposed design is robust against illumination changes. Through a series of experiments, the proposed design gives a correct decision in general, dim, and flash light condition.

Fingerprint pore matching method using polar histogram

Pores on the fingerprints have proven to be useful features for fingerprint recognition and successfully used in Automatic Fingerprint Recognition Systems (AFRS). Several methods for pore matching have been reported and these methods employ minutiae or ridge information. This paper presents a pore matching method using relative locations between extracted pore centers. The proposed pore matching method first generates a descriptor for each pore using polar histogram and establishes the correspondences between the pores from the reference image and the target image. Then, RANSAC (RANdom SAmple Consensus) algorithm is used to refine the correspondences and calculates the final matching score. A series of experiments show that fingerprint recognition could be achieved using only relative locations between the pores.

Part-based vehicle detection in side-rectilinear images for blind-spot detection

Abstract The blind-spot detection (BSD) system is designed to prevent accidents during lane changing and overtaking scenarios. Current BSD systems that use side- or rear-view cameras suffer from limited performance because of the severe distortion in the appearance of nearby vehicles depending on their positions relative to the host vehicle. To overcome such limitations, this manuscript introduces a side-rectilinear image to detect and use the side parts of the vehicles. In the side-rectilinear image, the side parts of the vehicles do not contain radial or perspective distortions; consequently, the appearance of the tires remains identical from different positions on the vehicle. By utilizing this rectilinear image, a rear-camera-based BSD system that detects both vehicles and motorcycles is constructed to prevent possible accidents occurring in blind spots. The proposed BSD system detects the vehicles in three stages: tire hypothesis generation/verification, front-rear tire classification, and vehicle hypothesis generation/verification. For motorcycle detection, the proposed system detects the lower parts of the motorcycle, which are not affected by the appearance of the drivers and cargos. Using the property of the side-rectilinear image, the detection procedures of the proposed system are straight-forward and resemble the object detection/recognition rules of humans. Based on the detection results, the system tracks nearby vehicles and gives a warning signal to the driver when there are obstacles in blind spots. An evaluation of the system performance demonstrates that the warning rate of the proposed system outperforms that of radar-based systems.

Stereo camera based real-time gaze correction method by matching and blending eye regions

In video-conference, the camera is placed at the top of the screen and the user looks at the other users image on the screen. This misalignment of eye gaze direction degrades the users experience. To solve this problem, this paper presents an eye gaze correction method using vertical stereo camera. The proposed method matches the eye regions in two images captured by stereo camera and generates new eye regions by alpha-blending these two eye regions. To obtain a gaze corrected image, we replace eye regions in top image with new eye regions. The proposed method requires fewer computations than conventional methods using stereo camera. Experiment results showed that the proposed method could generate eye gaze corrected images at 14 frames per second using images captured simultaneously by top and bottom cameras with the resolution of 640×480.