Information Of Neighboring Pixels Research Articles

An Effective Color-Difference-Based Demosaicking Method

This paper proposes an effective color-difference-based (ECDB) interpolation algorithm for color filter array (CFA) Image demosaicking. A CFA image consists of a set of spectrally selective filters which are arranged in an interleaved pattern such that only one color component is sampled at each pixel location. To improve the quality of reconstructed full-color images from CFA images, the ECDB algorithm first analyzes the samples immediately around a missing green pixel to determine suitable samples for interpolating the value of this missing green pixel. After finishing the interpolation operations of all the missing green pixels, a complete green plane (i.e G plane) is obtained. The ECDB algorithm then makes use of the high correlation between the R, G, and B planes to produce the red-green and blue-green color difference planes and further reconstructs the red and blue planes in successive operations. Because the green plane provides twice the information of the red and blue planes, the algorithm uses the information of the green plane more than that of the red/blue planes so that the full color image can be reconstructed more accurately. In essence, the ECDB algorithm uses the red-green and blue-green color difference planes, and develops different conditional operations according to the horizontal, vertical, and diagonal neighboring pixel information with a suitable weighting technique. The experimental results demonstrate that the proposed algorithm performed extremely well.

Hiding an Image inside another Image using Variable-Rate Steganography

A new algorithm is presented for hiding a secret image in the least significant bits of a cover image. The images used may be color or grayscale images. The number of bits used for hiding changes according to pixel neighborhood information of the cover image. The exclusive-or (XOR) of a pixel’s neighbors is used to determine the smoothness of the neighborhood. A higher XOR value indicates less smoothness and leads to using more bits for hiding without causing noticeable degradation to the cover image. Experimental results are presented to show that the algorithm generally hides images without significant changes to the cover image, where the results are sensitive to the smoothness of the cover image.

Evaluation of the MODIS snow cover fraction product

Eleven years of daily 500 m gridded Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD10A1) snow cover fraction (SCF) data are evaluated in terms of snow presence detection in Colorado and Washington states. The SCF detection validation study is performed using in-situ measurements and expressed in terms of snow and land detection and misclassification frequencies. A major aspect addressed in this study involves the shifting of pixel values in time due to sensor viewing angles and gridding artifacts of MODIS sensor products. To account for this error, 500 m gridded pixels are grouped and aggregated to different-sized areas to incorporate neighboring pixel information. With pixel aggregation, both the probability of detection (POD) and the false alarm ratios increase for almost all cases. Of the false negative (FN) and false positive values (referred to as the total error when combined), FN estimates dominate most of the total error and are greatly reduced with aggregation. The greatest POD increases and total error reductions occur with going from a single 500 m pixel to 3×3-pixel averaged areas. Since the MODIS SCF algorithm was developed under ideal conditions, SCF detection is also evaluated for varying conditions of vegetation, elevation, cloud cover and air temperature. Finally, using a direct insertion data assimilation approach, pixel averaged MODIS SCF observations are shown to improve modeled snowpack conditions over the single pixel observations due to the smoothing of more error-prone observations and more accurately snow-classified pixels. Copyright © 2012 John Wiley & Sons, Ltd.

Weighted-phase-gradient-based quality maps for two-dimensional quality-guided phase unwrapping

The two-dimensional phase unwrapping plays an important role in phase measurement applications. This paper proposes a simple but effective method to construct reliable quality maps for two-dimensional quality-guided phase unwrapping. A new group of reliable quality maps based on weighted-phase-gradient is proposed to overcome the shortcomings of traditional phase-gradient quality maps. When a quality map of a wrapped phase image is generated, the weighted gradient information of neighborhood pixels of the wrapped phase image is used to determine the quality of the current pixel. Thus the quality of the wrapped phase data can be accurately reflected in the resulting quality map. In order to evaluate the reliability, effectiveness and advantages of the proposed quality maps, a new performance evaluation model for quality maps is also proposed. Unwrapped results demonstrate that the weighted-phase-gradient-based quality maps are excellent indicators of the phase quality and can be used to retrieve a reliable phase profile in quality-guided phase unwrapping process.

A Neighborhood-Based Ratio Approach for Change Detection in SAR Images

This letter presents a novel neighborhood-based ratio (NR) operator to produce a difference image for change detection in synthetic aperture radar (SAR) images. In order to reduce the negative influence of speckle noise on SAR images, the proposed NR operator produces a difference image by combining gray level information and spatial information of neighbor pixels. The performance comparisons of the proposed operator with a traditional ratio operator and a log-ratio operator indicate that the NR operator is superior to these traditional methods and produces better detection results.

Quantitative prediction of acute ischemic tissue fate using support vector machine

Accurate and quantitative prediction of ischemic tissue fate could improve decision-making in the clinical treatment of acute stroke. The goal of the present study is to explore the novel use of support vector machine (SVM) to predict infarct on a pixel-by-pixel basis using only acute cerebral blood flow (CBF), apparent diffusion coefficient (ADC) MRI data. The efficacy of SVM prediction model was tested on three stroke groups: 30-min, 60-min, and permanent middle cerebral-artery occlusion (n=12 rats for each group). CBF, ADC and relaxation time constant (T2) were acquired during the acute phase up to 3h and again at 24h. Infarct was predicted using only acute (30-min) stroke data. Receiver-operating characteristic (ROC) analysis was used to quantify prediction accuracy. The areas under the receiver-operating curves were 86±2.7%, 89±1.4%, and 93±0.8% using ADC+CBF data for the 30-min, 60-min and permanent middle cerebral artery occlusion (MCAO) group, respectively. Adding neighboring pixel information and spatial infarction incidence improved performance to 88±2.8%, 94±0.8%, and 97±0.9%, respectively. SVM prediction compares favorably to a previously published artificial neural network (ANN) prediction algorithm operated on the same data sets. SVM prediction model has the potential to provide quantitative frameworks to aid clinical decision-making in the treatment of acute stroke.

An Adaptive Occluded Region Detection and Interpolation for Robust Frame Rate Up-Conversion

FRUC (Frame Rate Up-Conversion) technique needs an effective frame interpolation algorithm using motion information between adjacent neighboring frames. In order to have good visual qualities in the interpolated frames, it is necessary to develop an effective detection and interpolation algorithms for occluded regions. For this aim, this paper proposes an effective occluded region detection algorithm through the adaptive forward and backward motion searches and also by introducing the minimum value of normalized cross-correlation coefficient (NCCC). That is, the proposed scheme looks for the location with the minimum sum of absolute differences (SAD) and this value is compared to that of the location with the maximum value of NCCC based on the statistics of those relations. And, these results are compared with the size of motion vector and then the proposed algorithm decides whether the given block is the occluded region or not. Furthermore, once the occluded regions are classified, then this paper proposes an adaptive interpolation algorithm for occluded regions, which still exist in the merged frame, by using the neighboring pixel information and the available data in the occluded block. Computer simulations show that the proposed algorithm can effectively classify the occluded region, compared to the conventional SAD-based method and the performance of the proposed interpolation algorithm has better PSNR than the conventional algorithms.

A Kind of Sub-Pixel Grid Characters Thickness Adjustment Method

In order to ensure printing quality of text content in raster image, we usually need to adjust the thickness of characters in the pre-press. This paper presents a sub-pixel adjustment method, based on analysis of characters’ neighborhood pixel information, so that we can control the scope and direction of the adjustment. In our method, Image is considered as a combined result of a foreground character layer and a background image layer. By performing the steps of separating characters from background, calculating ink density of each pixel of characters, adjusting each pixel’s ink density by analysis of neighborhood stroke shape, we achieve a result so fine that could simulate the effects performed by vector methods. The method can be adapted to global or any certain direction (e.g. vertical, horizontal, diagonal, etc) thickness adjustment. Experimental results show that the method can achieve excellent results and is proved to be very practical.

색수차 개선을 위한 효율적인 컬러 에지 퍼지 보간 방법

모바일폰이나 디지털 카메라에서 사용하는 영상 센서의 수광 소자는 한 가지 색만을 기록하는 베이어 패턴이기 때문에 각각의 화소들은 여러 개의컬러 중에서단 하나만의 컬러에 대한 화소값을얻게 된다. 잃어버린화소의 정보는 CFA(Color Filter Array)를 이용하여 그 주위의 화소에 대한 정보를 이용함으로 완전한 컬러 영상을 추론한다. 본 논문은 영상 센서의 수광 소자로부터 획득한 데이터의 평균과 각 컬러 채널 데이터 평균 사이의 관계를 유도하고, 이 관계를 이용하여 색상 보간을 위한 효율적인 에지 컬러 퍼지 보간 방법을 제안하였다. 또한 휘도 신호 채널의 보간은 컬러 노이즈를 감소하고 효율적인 보간을 위해 에지 방향에 따라 퍼지 보간을 하였다. 그리고 본 논문에서 PSNR을 이용하여 제안된 방법이 기존 방법보다 평균 2.4dB 정도 개선됨을 증명하였으며, 시각적으로 원영상의 해상도와 유사한 수준이며, 기존 알고리즘의 영상보다 색수차가 감소된 것을 실험 결과를 통하여 확인하였다. Each pixels become got pixel value for color of only one from among colors because of bayer pattern that light receiving device of image sensor which is used in HHP and digital camera writes only one color. Information of the missing pixels could infer perfect color image from using information of neighbor pixels by using CFA(Color Filter Array). In this paper, we derive relation between the average of the data from the light receiving device of image sensor and each color channel data. And by using this relation, a new efficient edge color fuzzy method for color interpolation is proposed. Also, missing luminance signal channel interpolation was fuzzy interpolation along any edges direction for reducing color noise and interpolating efficiently it. And in this paper, the proposed method has been proved improving average 2.4dB than the conventional method by using PSNR. Also, resolution of the image of the proposed method was similar to the original image by visual images, we has been verified to be decreased a chromatic aberration than image of conventional algorithms with simulation result.

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters ( changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson–Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie–Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.

Increasing the reliability of skin detectors

Skin detection is a well-known image processing technique that has been used in many applications such as video surveillance, naked image filters, and face detection. This paper proposes a reliable skin detection method that integrates both color and texture features. To increase the reliability of the skin detection process, neighborhood pixel information is incorporated into the proposed method. Texture features were estimated using statistical measures as range, standard deviation, and entropy. Back propagation artificial neural network is then used to learn features and classify any given input. In this work, three skin detectors based on pre-defined rules of skin color tones, texture features only, and a combination of both color and texture features have been constructed. Furthermore, the paper analyzes and compares the obtained results from the proposed skin detector with pre-defined color tones and texture feature-based skin detectors to show the level of robustness of the proposed skin detector. It found that the proposed skin detection method achieved a true positive rate of approximately 95.6176% and a false positive rate of approximately 0.8795%. Experimental results showed that our approach is more efficient compared with other state-of-the-art color-based or texture-based skin detector approaches. Key words: Skin detector, supervised learning, back propagation ANN, Image texture features, Image segmentation.

Increasing the Reliability of Fuzzy Inference System-Based Skin Detector

Problem statement: Skin detection is a common primitive for many human-related image processing applications, such as video surveillance, naked image filters and face detection. Skin color is considered as a useful and discriminating spatial feature for many applications, but it is not robust enough to deal with complex image environments. Skin tones range from dark (some Africans) to light white (Caucasians and some Europeans). In addition, both the light-changing conditions and the existence of objects with skin-like colors could cause some major difficulties faced pixel-based skin detector depending only on a color feature. Approach: This study proposed a novel Fuzzy Inference System (FIS) for skin detection, which combines both color and texture features. To increase the reliability of the skin detection process, neighborhood pixel information is incorporated into the proposed method. The color feature is represented using RGB color model, while the texture feature is estimated using three statistical measures: standard deviation, entropy and range. The subtractive clustering-based fuzzy system method and the Sugeno type reasoning mechanism are used for modeling FIS-based skin detection. The proposed approach builds a fuzzy model of skin detection from existing images within skin and non-skin regions (output data) and from both color and texture features of the skin regions (input data). Results: The proposed skin detection method achieved a true positive rate of approximately 90% and a false positive rate of approximately 0.22%. Furthermore, this study analyzes and compares the obtained results from the proposed skin detection with threshold-based skin detector to show the level of robustness, using both color and texture features in the proposed skin detector. Conclusion: It was found that a skin detector based on both color and texture features can lead to an efficient and more reliable skin detection method compared with other state-of-the-art threshold-based skin detectors. The proposed detector reduces the FP rate to 0.22% compared with a skin detector based on predefined color rules.

Artificial Neural Network Prediction of Ischemic Tissue Fate in Acute Stroke Imaging

Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin-spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis.

Implicit regularization of the incomplete oblique projections method

AbstractThe aim of this paper is to improve the performance of the incomplete oblique projections method (IOP), previously introduced by the authors for solving inconsistent linear systems, when applied to image reconstruction problems. That method employs incomplete oblique projections onto the set of solutions of the augmented system Ax−r=b, and converges to a weighted least squares solution of the system Ax=b. Many tomographic image reconstruction problems are such that the limitation of the range of rays makes the model underdetermined, the discretized linear system is rank‐deficient, the nullspace is non‐trivial, and the minimal norm least squares solution may be far away from the true image. In a previous paper, we have added a quadratic term reflecting neighboring pixel information to the standard least squares model for improving the quality of the reconstructed images. In this paper we replace the quadratic function by a more general regularizing function avoiding the modification of the original system. The key idea is to perform a joint optimization of the norm of the residual and of the regularizing function in each iteration. The theoretical properties of this new algorithm are analyzed, and numerical experiments are presented comparing its performance with other well‐known methods. They show that the new approach improves the quality of the reconstructed images.

국부 영역 중앙값 기반의 가중치 부여 방법을 이용한 보간 영상의 화질 개선

저해상도 영상에서 고해상도 영상으로 영상을 확대할 경우 적용하는 보간법은 알고 있는 값을 기준으로 해서 보간 값을 추론한다. 이 과정에서 블러링과 블록화 현상 등이 발생한다. 기존의 보간법은 처리과정에서 주변의 값들에 적절한 가중치를 곱하여 더함으로 이러한 단점들을 보완하고자 한다. 제안방법은 원화소의 값을 주변의 정보를 고려하여 변형하고 이 화소값을 이용하여 보간 화소를 생성함으로 보간 영상의 화질을 개선한다. 먼저 대각 방향의 차이값을 이용하여 복잡영역과 평탄 영역으로 구분하고 복잡영역일 경우 주변 영역의 메디안 값과 원화소의 값을 이용하여 변형된 화소의 값을 구한다. 즉, 평탄 영역일 경우는 원화소의 값을, 복잡영역은 변형된 화소의 값을 이용하여 양선형 보간법을 적용하여 보간 값을 구한다. 제안 방법의 성능을 평가하기 위해 기존의 보간 방법과의 PSNR과 확대 영상의 화질을 비교하였다. 실험결과 제안 방법은 기존의 방법에 비해 PSNR을 개선하였고 화질도 우수함을 보여준다. Interpolation methods to get the magnified image from an image with low resolution use known pixels to make an interpolated pixel. This interpolation process usually generates blurred edges and blocking effect in the result image. To improve these defects, conventional methods multiply proper weights reflecting neighborhood pixels and add the values during interpolating process. The proposed method changes input pixels in consideration of information of neighborhood pixels, gets interpolated pixels by using these values and improves the quality of interpolated image. Firstly, we compute difference values of the diagonal directions of a pixel and classify flat regions and complex regions according to these values. If the regions is complex ones, the proposed method changes an original pixel into a new value using a input pixel and a median value of it's neighbor pixels. Therefore, the proposed method applies bilinear method to the original pixels in flat regions and the changed ones in complex regions and produces the interpolated images. We evaluate the performance of the proposed method with existing methods by using PSNR and the quality of enlarged image. The results show that the proposed method improves PSNR in comparing with conventional methods and that is superior to the existing methods in terms of the quality of the interpolated image.

Effects of including spatial information in clustering multivariate image data

Multivariate image data provide detailed information in variable and image space. Most traditional clustering methods are based on variable information only and ignore spatial information. A method based on both variable and spatial information could improve the results substantially. In this review, we study the benefits and the pitfalls of including spatial information in chemometric clustering techniques. Spatial information is taken into account in initialization of clustering parameters, during cluster iterations by adjusting the similarity measure or at a post-processing step. We illustrate the effect of taking spatial information into account by a univariate synthetic data set and two real-world multivariate data sets. We show that methods that include neighboring pixel information in the clustering procedure improve the performance accuracy of the clustering in most cases. Homogeneous regions in the image are better recognized and the amount of noise is reduced by these methods.

Fingerprint minutiae filtering using ARTMAP

Fingerprints are widely used for unique personal identification based on minutiae matching. Minutiae are the terminations and bifurcations of ridges in a fingerprint image. Generally fingerprint images are of low quality due to the presence of noise and contrast deficiency resulting in discontinuity in ridges producing false minutiae points. It is worth noting that there is a fundamental difference between a neural network (NN) approach for minutiae location and minutiae filtering. In this paper, the spurious minutiae points and the bug pixels introduced during the thinning process are eliminated based on the neighborhood pixel information. A new minutiae filtering algorithm using a NN is introduced to improve the accuracy of the extraction algorithm proposed in the literature. Each minutia, as detected by the algorithm, is classified through ARTMAP NN whose output indicates whether it is a termination, a bifurcation or a false minutia. Experimental results show that the efficiency of minutiae classification has significantly improved using the proposed filtering algorithm.

A new Hausdorff distance for image matching

Object matching in two-dimensional images has been an important topic in computer vision, object recognition, and image analysis. The Hausdorff distance plays an important role in image matching. In order to deal with image matching problems in random noisy situations, a new Hausdorff distance is proposed in this paper. Unlike the other methods that match two binary images, the proposed method can match the gray images that have a few of pixel values. An example of object recognition is used to demonstrate the efficiency of the proposed method. The results show that, compared with MHD, the new Hausdorff distance can dispose of the noisy image matching in a more desirable manner, due to the fact that the comprehensive reflection of the gray information of neighbor pixels in the determination of the Hausdorff distance is taken into account. In addition, the proposed method can be implemented in an easy way.

A new algorithm for detecting the transition region on noise image

An algorithm of ramp width reduction based on the gray information of neighborhood pixels is proposed, which can sharpen the ramp edge effectively. Then, a new gray-weighted gradient operator and the automatic method to determine its parameter are introduced when detecting the transition region of images. Gray-weighted gradient operator can not only make the correlation of gradient and gray information as big as possible, but also resist the noise in the images. Some experiments show that the algorithm in this paper can extract the transition region more effectively.