In this paper, we propose a new method for authentication, and recoverability of digital images using Singular Value Decomposition. Using this technique, we can detect any modification made to the image and indicate the specific locations where the modification was made. The new method is reversible in the sense that, if the image is deemed authentic, the distortion on the extraction procedure can be removed to obtain the original image data. Authentication data is produced from XOR operation between watermark image and binary bits obtained from singular value of one block image. If the unmodified watermarked image is used, the extraction process will return the correct watermark signal and exact original image. The techniques provide new assurance tools for integrity protection of sensitive imagery such as medical images.

Reversible Watermarking for Authentication using Singular Value Decomposition

In digital radiographic system the noise is originated from various elements of the system, such as camera, X-ray source, imaging screen, inspected object, electronic controller and etc. Radiographs are regarded as being obtained by corrupting a noiseless image. This noise influence image quality seriously and can significantly degrade it. Therefore, before applying denoising techniques, analytic noise model is to be investigated. In this paper, we propose an appropriate approach for the analysis of the noise characteristics in steel-tube radiographic images to verify how noise influence image formation. To achieve a noise model, both global and region-based local analysis is used due to special and statistic estimation of noise. The assumption of a distribution of Gaussian and Poisson distribution causes the robust estimation of the noise variance in regions of high and low intensity. Examples of noise characteristics and its model of steel-tube radiographs show the effectiveness of the proposed approach.

Statistical Noise Model in Digital Radiographic Images

It is difficult for gain tuning engineer to set gain value, because gain value is changeable according to distance and speed. In many case, engineer do not set the gain when it is dangerous to move object. So, We do DC motor modeling, root locus is satisfied to system stability. Because calculated gain has uncertainty, we remove it using RLS algorithm. This system finds gain values, and it sets values automatically. So, we obtained result of better performance. It is easy to operate many motors of humanlike robot. Results were explained in terms of advantage of this method. These experiments show that human-like robot engineer will use auto-gain tuning algorithm to many robots in the future.

PID Auto Gain Tuning of DC motor using RLS

Recently the remote controls of many types are used in the electrical component. The goal of this paper is the improvement of accuracy of remote control appearance visual inspection by using histogram equalization and specification method. The histogram is composed of horizontal axis of image intensity value and vertical axis of pixel number in image. And the histogram of reference image and input image are different because of minutely lighting distinction. The minutely lighting distinction is the main reason of visual inspection error in many cases. Also most of appearance visual inspection, appearance problems are detected by comparing of reference image and input image. Therefore we made an effort for optimization of remote control visual inspection by making the identical histogram of reference image and input image. As a result of the histogram equalization and specification algorithm, we could increase the exactness of remote control visual inspection and prevent inspection error from physical and spirit condition of human. Specifically, if the proposed algorithm is not used, the inspection error rate of 7.5[%] is showed. In case of proposed method, we could see the inspection error rate of 1.8[%]. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

A Histogram Modification Method for Improving Visual Inspection Accuracy

This paper is concerned with the detection of lip movement image signals from successive image frames. It is suggests the possibility that lip movement image signals can be utilized while detecting the speech block of speech recognition procedures. The image frames are acquired from the PC image camera with the assumption that facial movement is limited during the conversation. First of all, one new lip movement image frame generated with comparing two successive image frames each other. Second, the fine image noises are removed. And residual images are separated into different areas. Each fitness rate is calculated by comparing the lip feature data as objectly separated images. It is analyzed whether or not there is the lip movement image through verification to the objects, three images that their fitness rate is high. It was visually confirmed that lip movement image signals are tracked, and its utilization possibility in speech block detection process.

Detection of Lip Movement Image Signals from Successive Image Frames

The object recognition using image is one of the major concerned fields of the computer vision and still many researches are done. The feature based methods as a model based object recognition among several methods for object recognition applied to find out inherent feature point and recognize the object through feature point matching between the query images of stored in the database and the input images. But, the feature based object recognition has suffered of difficulties in real-time processing due to heavy calculations and problems that responds sensitive for change of parameter. This paper has focused on speed improvement in these problems, and proposing the methods of object recognition by dividing the images into uniform blocks and matching each blocks to identify the object for more efficient performance than the feature based object recognition. In this paper, it is confirmed that the matching time for object recognition has reduced significantly since only using minimal blocks in the proposed method.

The Matching Algorithm using Block Segmentation for Object Recognition

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The Matching Algorithm using Block Segmentation for Object Recognition