Changhan Park

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.

Quasi-Feature based Panoramic Video Creation for Multiview Object Tracking System

In this paper we present an efficient approach to building a panoramic video from mutiview cameras and to tracking objects. The proposed panoramic video creation module consists of two functions: (i) selecting four quasi-feature points in two adjacent frames acquired by corresponding multiple cameras and (ii) mosaicing the two images. Four pairs of selected quasi-feature points play a role of similarity reference in registering two adjacent frames. The mosaicing step uses the direct linear transformation (DLT) algorithm. The proposed tracking algorithm uses the active contour model (ASM), which is robust against partial occlusion. The proposed tracking module consists of four functions: (i) landmark point assignment, (ii) principal component analysis (PCA), (iii) modeling of local structure, and (iv) model fitting. While most conventional panoramic image creation methods are pixel-based, the proposed feature-based method provides more accurate tracking result. In the experiment, the created panoramic images exhibit high quality, which enables robust, real-time video tracking.

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.

Suppression of fixed pattern noise for infrared image system

In this paper, we propose suppression of fixed pattern noise (FPN) and compensation of soft defect for improvement of object tracking in cooled staring infrared focal plane array (IRFPA) imaging system. FPN appears an observable image which applies to non-uniformity compensation (NUC) by temperature. Soft defect appears glittering black and white point by characteristics of non-uniformity for IR detector by time. This problem is very important because it happen serious problem for object tracking as well as degradation for image quality. Signal processing architecture in cooled staring IRFPA imaging system consists of three tables: low, normal, high temperature for reference gain and offset values. Proposed method operates two offset tables for each table. This is method which operates six term of temperature on the whole. Proposed method of soft defect compensation consists of three stages: (1) separates sub-image for an image, (2) decides a motion distribution of object between each sub-image, (3) analyzes for statistical characteristic from each stationary fixed pixel. Based on experimental results, the proposed method shows an improved image which suppresses FPN by change of temperature distribution from an observational image in real-time.

Next generation network based intermediate-view reconstruction using variable block matching algorithm

In this paper, next generation network (NGN) based intermediate-view reconstruction (IVR) using variable block matching algorithm (VBMA) is proposed. In the proposed system, the stereoscopic images are estimated by VBMA, and they are transmitted to receiver through dynamic bandwidth allocation (DFA), this scheme improves a priority-based access network converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR. From some experimental results, it is found that the proposed system improves peak signal-to-noise ratio (PSNR) up to 4.86 dB. Also, network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results with mathematical analyses obtained by this scheme are also provided.

Computer-based integrated imaging using normalized cross-correlation for resolution enhancement

A computer-based integrated imaging system (CIIS) using normalized cross correlation (NCC) for resolution enhancement is proposed to extract accurate location data of 3-D objects. Elemental images (EI) of the target and reference objects are picked up by lenslet arrays, and then target and reference plane images with enhanced resolution are reconstructed at the output plane by using the CIIS technique. Through cross correlations between the reconstructed reference and target plane images, 3-D location data of the target objects in a scene can then be robustly extracted. As a result of our experiments, we see that the proposed correlation scheme provides good discrimination and detection performance for 3-D object recognition.