• 전자공학회논문지A
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• 제33A권6호
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• pp.178-188
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• 1996

In this paper, we propose a new opto-digital object recognition system which has rotation, scale, and shift invariant characteristics. The fourier power spectrum of the object image is modified to get shift invariance. The log-polar transform is used for rotation and scale invariance. And the decision of similarities is performed by nonlinear joint transform correlator (NJTC) that can control the ratio of phase and amplitude signals. Experimental verification of th eproposed optical object recognition system is presented.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.17-34
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• 2021

This study aimed to examine the Factor Structure of the teacher satisfaction scale (TSS) with distance education during the Covid-19 pandemic, as well as affirming the (Factorial Invariance) according to gender variable. It also aimed at identifying the degree of satisfaction according to some demographic variables of the sample. The study population consisted of all teachers in public education and faculty members in higher education in the Kingdom of Saudi Arabia. The (TSS) was applied to a random sample representing the study population consisting of (2399) respondents. The results of the study showed that the scale consists of five main factors, with a reliability value of (0.94). The scale also showed a high degree of construct validity through fit indices of the confirmatory factor analysis. The results have shown a gradual consistency of the measure's invariance that reaches the third level (Scalar-invariance) of the Measurement Invariance across the gender variable. The results also showed that the average response of the study sample on the scale reached (3.74) with a degree of satisfaction, as there are no statistically significant differences between the averages of the study sample responses with respect to the gender variable. While there were statistically significant differences in the averages with respect to the variable of the educational level in favor of the middle school and statistically significant differences in the averages attributed to the years of experience variable in favor of those whose experience is less than (5) years.

• Journal of applied mathematics & informatics
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• 제2권1호
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• pp.11-16
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• 1995

Scale Invariant Transforms are defined for both one- and two- dimensioned input functions. These have the desirable properties of linearity and invariance to scale change of the input.

• 한국정보통신학회논문지
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• 제3권3호
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• pp.547-552
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• 1999

[1]에서 ETBF(extended threshold Boolean filter)의 부분군인 self-dual ETBF가 중앙값 부여파기 출력의 평균547으로 주어짐을 보였다. 이 논문에서는 ETBF의 입력을 실수로 확장하고 이를 이용하여 ETBF의 scale-preservation 성질 및 translation-invariance 성질을 고찰하였다. 특히, ETBF의 scale-preserving 성질이 self-dual 성질의 필요 충분 조건임을 보였다.

• 한국수자원학회논문집
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• 제41권9호
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• pp.873-884
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• 2008

본 연구에서는 서로 다른 지속기간을 가지는 강우자료간의 스케일 성질을 이용하여 확률강우량을 추정하는 방법을 알아보고, 국내 강우자료에 대해서 그 적용성을 살펴보고자 하였다. 기상청 지점중에서 기록기간이 비교적 장기간인 22개 지점에 대해서 지점빈도해석을 통한 확률강우량과 스케일 성질을 활용한 확률강우량간의 정확도를 비교해 본 결과, 시간단위자료를 사용할 경우 대부분 절대상대오차가 10 % 미만인 결과를 얻을 수 있었고, 각각의 지속 기간별로 적용가능한 재현기간을 살펴본 결과 연구에 사용된 총 14개의 재현기간 중에서 대부분 8개 이상의 재현기간에 대해서 적용이 가능한 것으로 나타났다. 적용예로서 대구지점의 지속기간 1시간 강우자료를 바탕으로 지속기간 10분${\sim}$50분까지의 확률강우량자료를 스케일 성질을 이용하여 산정한 결과 지속기간 10분을 제외하고는 모두 절대상대오차 10 % 내외의 정확도를 가지는 것으로 나타났다. 따라서 스케일 성질을 이용하여 미계측 강우지속기간의 확률강우량을 추정할 수 있을 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• 호남수학학술지
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• 제13권1호
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• pp.53-63
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• 1991

In this paper we first present the elements of the theory of families of distributions and corresponding estimators having structual properties which are preserved under certain groups of transformations, called "Invariance Principle". The invariance principle is an intuitively appealing decision principle which is frequently used, even in classical statistics. It is interesting not only in its own right, but also because of its strong relationship with several other proposal approaches to statistics, including the fiducial inference of Fisher [3, 4], the structural inference of Fraser [5], and the use of noninformative priors of Jeffreys [6]. Unfortunately, a space precludes the discussion of fiducial inference and structural inference. Many of the key ideas in these approaches will, however, be brought out in the discussion of invarience and its relationship to the use of noninformatives priors. This principle is also applied to the problem of finding the best scale invariant estimator in the scale parameter problem. Finally, several examples are subsequently given.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1420-1423
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• 2002

The classification and recognition of two-dimensional trademark patterns independently of their position, orientation, size and scale by proposing two feature vectors has been discussed. The paper presents experimentation on two feature vectors showing size- invariance and scale-invariance respectively. Both feature vectors are equally invariant to rotation as well. The feature extraction is based on local as well as global statistics of the image. These feature vectors have appealing mathematical simplicity and are versatile. The results so far have shown the best performance of the developed system based on these unique sets of feature. The goal has been achieved by segmenting the image using connected-component (nearest neighbours) algorithm. Second part of this work considers the possibility of using back propagation neural networks (BPN) for the learning and matching tasks, by simply feeding the feature vectosr. The effectiveness of the proposed feature vectors is tested with various trademarks, not used in learning phase.

• International Journal of Precision Engineering and Manufacturing
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• 제9권4호
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• pp.45-50
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• 2008

The Fourier-Mellin transform is the theoretical basis for the translation, rotation, and scale invariance of an image. However, its implementation requires a log-polar map of the original image, which requires logarithmic sampling of a radial variable in that image. This means that the mapping process is accompanied by considerable loss of data. To solve this problem, we propose a dual log-polar map that uses both a forward image map and a reverse image map simultaneously. Data loss due to the forward map sub-sampling can be offset by the reverse map. This is the first step in creating an invertible log-polar map. Experimental results have demonstrated the effectiveness of the proposed scheme.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.146-155
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• 1997

In order to achieve scale- and rotation-invariance in recognizing unoccluded objects in binary images using Zernike moment features, an image of an object has often been normalized first by its zeroth-order moment (ZOM) or area. With elongated objects such as characters, a stroke width varies with the threshold value used, it becomes one or two pixels wider or thinner. The variations of the total area of the character becomes significant when the character is relatively thin with respect to its overall size, and the resulting normalized moment features are no longer reliable. This dilation/erosion effect is more severe when the object is not focused precisely. In this paper, we analyze the ZOM method and propose as a normalization method, the maximum enclosing circle (MEC) centered at the centroid of the character. We compare both the ZOM and MEC methods in their performance through various experiments.