Jik-Han Jung

A Novel Template Matching Scheme for Fast Full-Search Boosted by an Integral Image

A new template matching method accelerated by an integral image is proposed. In contrast to the conventional winner-update template matching algorithm, the proposed scheme uses an integral image instead of a block sum pyramid to represent the search area. When an integral image is used, block sums on the lowest level are evaluated very fast. As a result, the speed with which nonbest candidates are rejected is nearly double that of the conventional scheme. Moreover, the proposed scheme needs less memory than the conventional approach to maintain block sums of candidates and can be easily extended to nonsquare (rectangular) template matching.

Image fusion in infrared image and visual image using normalized mutual information

In this paper, we propose an image fusion for open and unknown environments using normalized mutual information (NMI) in an infrared (IR) and visual vision system. Image fusion is a field of study of image processing, and it creates a new image to extract information from various different sensors. And also it gets effective information for a special object. This can get object types, sensitive characteristic, and information which it not to get characteristic of object from a single sensor. Image fusion in multi-sensors is two advantages. First, multi-sensor image has inherent redundancy for each sensor because it can be fused each image from a various multi-band sensor. Second, multi-sensor differs from a single sensor because it is included information of each sensor and is separated information of object easily in real environments. Proposed method consists of extraction and comparison of feature point, image registration, and pseudo color for display. Extraction of feature point is stage which it looks for a similar feature points between each sensor. Then, the extraction of a similar feature point uses a corner detector. A detected correspondence point from multi-sensor is compared feature point by using NMI. An acquired image in multi-sensor needs an image registration between two images. Because it needs transformation from reference image to a coordinated system of sensed image. And this represents each coordinated system independently between two images. Image registration use transformation of H matrix. Method for overlay between two images uses blending based on HSV. Based on experimental results, the proposed method shows high precision for fused pseudo image in multi-sensor, and can be represented image registration by using probability-based method.

Object recognition in infrared image sequences using scale invariant feature transform

In this paper, we propose an automated target recognition by using scale-invariant feature transform (SIFT) in PowerPC-based infrared (IR) imaging system. An IR image can be acquired more feature values at night than in the daytime, but visual image can be acquired more feature values in the daytime. IR-based object recognition puts application into digital surveillance system because it exist some more feature values at night than in the daytime. Feature of IR image in its system appears a little feature value in the daytime. It is not comprised within an effective feature values at a visual image from an IR of the daytime. Proposed method consists of two stages. First, we must localize the interest point in position and scale of moving objects. Second, we must build a description of the interest point and recognize moving objects. Proposed method uses SIFT for an effective feature extraction in PowerPC-based IR imaging system. Proposed SIFT method consists of scale space, extrema detection, orientation assignment, key point description, and feature matching. SIFT descriptor sets up extensive range about 1.5 times than visual image when feature value of SIFT in IR image is less than visual image. Because an object in IR image is analogized by field test that it exist more expanse form than visual image. Therefore, proposed SIFT descriptor is constituted at more expanse term for a precise matching of object. Based on experimental results, the proposed method is extracted objects feature values in PowerPC-based IR imaging system, and the result is presented by experiment.

Development of work assistant mobile robot system for the handicapped in a real manufacturing environment

We develop a work assistant mobile robot to help the handicapped. Mission statements for developing an assistant robot are derived based on survey to assist effectively the handicapped as many as possible in a real employment situation. According to the mission statements, work assistant mobile robot system type I and type II are developed, and the robots are performed user-trials by the disabled who is working in a manufacturing labor.

Fast block matching algorithm using spatial intensity distribution

Block matching algorithm is useful in many applications such as stereo vision, visual tracking and so on. And the heavy computational burden of the full-search algorithm induces many faster algorithms, which can be classified into two. One class reduces the search area where the other reduces the unnecessary computation of each candidate block. In this paper, a new matching algorithm, which reduces the computational burden by using vote strategy, is proposed. With the observation of the property that the best match also has the similar spatial intensity distribution, block matching algorithm is described. The proposed matching algorithm is fast and robust to speckle noise or object occlusion.

2:1 Candidate Position Subsampling Technique for Fast Optimal Motion Estimation

The candidate position subsampling technique (CPST) basically chooses candidates in a search window at a sampling rate. The 2:1 CPST chooses half the candidates, and then selects one or more candidates that are considered as to be close to the optimal motion vector before conducting a fine search. The fine search is conducted by checking four neighbors of the chosen candidate(s) referred to as winner(s). The CPST can be combined with a fast optimal block-matching algorithm, such as the multilevel successive elimination algorithm (MSEA), in order to reduce the number of computations used in rejecting the nonbest candidate. We propose a new 2:1 CPST fitted to the MSEA. The proposed algorithm adopts a new condition for the winner which helps to find the best candidate efficiently. Moreover, a fast motion estimation step is used to reduce the number of computations of the MSEA, and the peak signal-to-noise ratio (PSNR) compensation step is adopted to guarantee that the PSNR performance of the proposed algorithm is very close to that of the full search. Experimental results show that the proposed algorithm reduces the computational loads of the MSEA by 47.26% on average with only -0.027 dB PSNR degradation in the worst case.

An effective successive elimination algorithm for fast optimal block-matching motion estimation

The successive elimination technique is used widely in the successive elimination algorithm (SEA), the multilevel successive elimination algorithm (MSEA), and the fine granularity successive elimination (FGSE) for fast optimal block-matching motion estimation. The computational cost of a series of these algorithms is primarily affected by a set of lower bounds. In this paper, an efficient scheme to build a set of lower bounds is proposed. This scheme efficiently produces the same set of lower bounds as those of MSEA. Moreover, the optimal number and positions of checking the validity of a candidate block using the proposed scheme is studied. Based on our study, a new algorithm is proposed: the effective successive elimination algorithm (ESEA). Experimental results are given to show the superiority of the proposed algorithm over previous approaches.

Automatic Extraction of Corresponding-Points for Image Registration

Recently, multi-sensor image fusion systems and related applications have been widely investigated. In an image fusion system, robust and accurate multi-modal image registration is essential. In the conventional method, the image registration process starts with manually-pointed corresponding pairs in both sensored images. Using these corresponding pairs, a transform matrix is initialized and refined through an optimization process. In this paper, we propose a new automatic extraction method for such corresponding pairs. The Harris corner detector is employed to extract feature points in both EO/IR images individually. Patches around the detected feature points are matched with a probabilistic criterion, mutual information (MI), which is a preferred measure for image registration due to its robust and accurate performance. Simulation results show that the proposed scheme has a low time complexity and extracts corresponding pairs well.

Wireless Digital Packet Communication and Analog Image Communication Systems for Fire Fighting Robot

Frequent occurrences of a fire cause tremendous loss of human lives and their property. Recently, in order to cope with such catastrophic accidents, researches on fire-fighting robots are carried out in developed countries. Under the dangerous situations, it is sometimes impossible for fire-fighting men to access the firing place because of explosive materials, smoke, high temperature and so on. In such an environment, fire-fighting robots can be useful to extinguish the fire. It is usually very dangerous place where fire-fighting robots operate. Hence, these robots should be controlled by remote users who are for away from the firing place exploiting remote communication systems. This paper considers the communication systems between fire-fighting robots and remote users. The communication systems consist of two parts; digital packet communication systems and analog image communication systems. Digital packet communication systems transfer data packets in order to control fire-fighting robots and to check the state of the fire-fighting robots. Remote users watch the video around the fire-fighting robots by exploiting the analog image communication systems. In the future, the more prosperous the commercial communication network systems will be, the more evolved the communication systems for fire-fighting robots are.

Single-Molecule Co-Immunoprecipitation Reveals Functional Inheritance of EGFRs in Extracellular Vesicles

Cancer cells actively release extracellular vesicles (EVs) as important carriers of cellular information to tumor microenvironments. Although the composition and quantity of the proteins contained in EVs are characterized, it remains unknown how these proteins in EVs are related to those in the original cells at the functional level. With epidermal growth factor receptor (EGFR) in lung adenocarcinoma cells as a model oncoprotein, it is studied how distinct types of EVs, microvesicles and exosomes, represent their original cells at the protein and protein-protein interaction (PPI) level. Using the recently developed single-molecule immunolabeling and co-immunoprecipitation schemes, the quantity and PPI strengths of EGFRs derived from EVs and the original lung adenocarcinoma cells are determined. It is found that the microvesicles exhibit higher correlations with the original cells than the exosomes in terms of the EGFR levels and their PPI patterns. In spite of these detailed differences between the microvesicles and exosomes, the EGFR PPI strengths measured for EVs generally show a tight correlation with those determined for the original cells. The results suggest that EGFRs contained in EVs closely reflect the cellular EGFR in terms of their downstream signaling capacity.