An automatic pptimum kernel-size selection technique for edge enhancement

Edge enhancement is a technique that can be considered, to a first order, a correction for the modulation transfer function of an imaging system. Digital imaging systems sample a continuous function at discrete intervals so that high-frequency information cannot be recorded at the same precision as lower frequency data. Because of this, fine detail or edge information in digital images is lost. Spatial filtering techniques can be used to enhance the fine detail information that does exist in the digital image, but the filter size is dependent on the type of area being processed. A technique has been developed by the authors that uses the horizontal first difference to automatically select the optimum kernel-size that should be used to enhance the edges that are contained in the image.     

An expert system based on fuzzy entropy for automatic threshold selection in image processing

In pattern recognition and image processing, the selection of appropriate threshold is a very significant issue. Especially, the selecting gray-level thresholds is a critical issue for many pattern recognition applications. Here, the maximum fuzzy entropy and fuzzy c-partition methods are used for the aim of the gray-level automatic threshold selection method. The fuzzy theory has been successfully applied to many areas, such as image processing, pattern recognition, computer vision, medicine, control, etc. The images have some fuzziness in nature. In this study, expert maximum fuzzy-Sure entropy (EMFSE) method for the maximum fuzzy entropy and fuzzy c-partition processes in automatic threshold selection is proposed. The experimental studies were conducted on many images by testing maximum fuzzy-Sure entropy against maximum fuzzy-Shannon entropy (MFSHE), maximum fuzzy-Havrada and Charvat entropy (MFHCE) methods for selecting optimum 2-level threshold value, respectively. The obtained experimental results show that the used MFSE method is superior to other MFSHE and MFHCE methods on selecting the 2-level threshold value automatically and effectively.     

An automatic procedure for image segmentation

A procedure for image segmentation involving no image-dependent thresholds is described. The method involves not only detection of edges but also production of closed region boundaries. The method has been developed and tested on head and shoulder images.     

A multi-resolution area-based technique for automatic multi-modal image registration

To allow remotely sensed datasets to be used for data fusion, either to gain additional insight into the scene or for change detection, reliable spatial referencing is required. With modern remote sensing systems, reliable registration can be gained by applying an orbital model for spaceborne data or through the use of global positioning (GPS) and inertial navigation (INS) systems in the case of airborne data. Whilst, individually, these datasets appear well registered when compared to a second dataset from another source (e.g., optical to LiDAR or optical to radar) the resulting images may still be several pixels out of alignment. Manual registration techniques are often slow and labour intensive and although an improvement in registration is gained, there can still be some misalignment of the datasets. This paper outlines an approach for automatic image-to-image registration where a topologically regular grid of tie points was imposed within the overlapping region of the images. To ensure topological consistency, tie points were stored within a network structure inspired from Kohonen’s self-organising networks [24]. The network was used to constrain the motion of the tie points in a manner similar to Kohonen’s original method. Using multiple resolutions, through an image pyramid, the network structure was formed at each resolution level where connections between the resolution levels allowed tie point movements to be propagated within and to all levels. Experiments were carried out using a range of manually registered multi-modal remotely sensed datasets where known linear and non-linear transformations were introduced against which our algorithm’s performance was tested. For single modality tests with no introduced transformation a mean error of 0.011 pixels was identified increasing to 3.46 pixels using multi-modal image data. Following the introduction of a series of translations a mean error of 4.98 pixels was achieve across all image pairs while a mean error of 7.12 pixels was identified for a series of non-linear transformations. Experiments using optical reflectance and height data were also conducted to compare the manually and automatically produced results where it was found the automatic results out performed the manual results. Some limitations of the network data structure were identified when dealing with very large errors but overall the algorithm produced results similar to, and in some cases an improvement over, that of a manual operator. We have also positively compared our method to methods from two other software packages: ITK and ITT ENVI.     

An automatic shape independent clustering technique

This article describes a clustering technique that can automatically detect any number of well-separated clusters which may be of any shape, convex and/or non-convex. This is in contrast to most other techniques which assume a value for the number of clusters and/or a particular cluster structure. The proposed technique is based on an iterative partitioning of the relative neighborhood graph, coupled with a post-processing step for merging small clusters. Techniques for improving the efficiency of the proposed scheme are implemented. The clustering scheme is able to detect outliers in data. It is also able to indicate the inherent hierarchical nature of the clusters present in a data set. Moreover, the proposed technique is also able to identify the situation when the data do not have any natural clusters at all. Results demonstrating the effectiveness of the clustering scheme are provided for several data sets.     

An automatic image registration and overlay system

In this paper we present an automatic algorithm for registering and overlaying imagery. The algorithm basically attempts to find by successive approximations the best affine transformation or second order polynomial relating to the two images. The method requires the specification of only a matching pair of control points, then new control points are found approximately by extrapolating the old affine transformation to larger areas and then using correlation to find the best match. Thus an obvious advantage of this algorithm lies in its automatic features in locating and matching more potential ground control points. This paper also discusses the effect of the distribution of control points on the affine transformation. Finally, the method is tested on Landsat data and the results are discussed.     

An image retrieval system with automatic query modification

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.     

An automatic fuzzy c-means algorithm for image segmentation

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.     

Design patterns selection: An automatic two-phase method

Over many years of research and practices in software development, hundreds of software design patterns have been invented and published. Now, a question which naturally arises is how software developers select the right design patterns from all relevant patterns to solve design problems in the software design phase. To address this issue, in this paper, we propose a two-phase method to select a right design pattern. The proposed method is based on a text classification approach that aims to show an appropriate way to suggest the right design pattern(s) to developers for solving each given design problem. There are two advantages of the proposed method in comparison to previous works. First, there is no need for semi-formal specifications of design patterns and second, the suitable design patterns are suggested with their degree of similarity to the design problem. To evaluate the proposed method, we apply it on real problems and several case studies. The experimental results show that the proposed method is promising and effective.     

An adult image identification system employing image retrieval technique

As the development of the Internet, children are easily exposed on the pornography through web browsers. To block adult images, content-based image retrieval technique is employed for adult image identification. First, the background is removed to obtain the rectangular region of interesting based on the detection of skin-like pixels. For each input image, the MPEG-7’s color, texture, and the proposed shape feature is used to retrieve 100 most similar images from the image database which contains both adult and non-adult images. If the retrieved images contains more than T_ad adult images, the input one is identified as an adult image. Otherwise, it is identified as a non-adult image. Experiment results have shown the effectiveness of the proposed method.     

An edge preserved image compression technique

An image compression technique that can preserve edges of objects is proposed here. This technique is developed by looking at small local regions of the image and classifying these regions as uniform, point, line or edge patterns from statistical likelihood ratio tests. The compression ratio depends on the size of the local region considered and the quality of the image will degrade as the size of the local region increases. Experimental results show good quality preservation when the sizes of the local region are 3 × 3, 4 × 4, 5 × 5 with the corresponding compression ratios less than or equal to 7/24, 1/5, 13/100 respectively. A comparison with discrete cosine transform is given here and shows that the new technique produces better quality in most cases.     

一种改进的航拍图像序列自动排序算法

为实现无人机航拍图像中图像序列自动排序,提出了一种基于相位相关法改进的图像序列自动排序算法。该算法利用对数极坐标的方式来表示图像间的平移、旋转、尺度缩放的关系,并利用最大相关度准则以及峰值坐标判断相邻图像的位置关系。实验结果表明,此算法能有效地解决全景图像拼接中序列图像混乱的问题,避免了人工干预,增强了算法的应用范围,具有很强的实用价值。     

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.     

An adaptive neuro-fuzzy system for automatic image segmentation andedge detection

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priori assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an "universal" algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, "as is," or as a basic building block by a more sophisticated and/or application-specific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels     

An automatic image-text alignment method for large-scale web image retrieval

Multimedia Tools and Applications - For reducing huge uncertainty on the relatedness between the web images and their auxiliary text terms, an automatic image-text alignment algorithm is developed...     

The Weibull manifold in low-level image processing: An application to automatic image focusing

In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve low-level image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.     

An automatic region-based image segmentation algorithm for remote sensing applications

Object-based image analysis has proven its potentials for remote sensing applications, especially when using high-spatial resolution data. One of the first steps of object-based image analysis is to generate homogeneous regions from a pixel-based image, which is typically called the image segmentation process. This paper introduces a new automatic Region-based Image Segmentation Algorithm based on k-means clustering (RISA), specifically designed for remote sensing applications. The algorithm includes five steps: k-means clustering, segment initialization, seed generation, region growing, and region merging. RISA was evaluated using a case study focusing on land-cover classification for two sites: an agricultural area in the Republic of South Africa and a residential area in Fresno, CA. High spatial resolution SPOT 5 and QuickBird satellite imagery were used in the case study. RISA generated highly homogeneous regions based on visual inspection. The land-cover classification using the RISA-derived image segments resulted in higher accuracy than the classifications using the image segments derived from the Definiens software (eCognition) and original image pixels in combination with a minimum-distance classifier. Quantitative segmentation quality assessment using two object metrics showed RISA-derived segments successfully represented the reference objects.     

An automatic incrementation technique for contact problems with friction

A numerical method for analysis of elastostatic contact problems with friction has been developed. This class of problems are load history dependent because of the irreversible nature of frictional forces. An automatic incrementation technique of the applied load has been developed and implemented in the algorithm. The method is a direct method based on an iterative procedure applied to a set of linear equations established with the finite element method. The size of the applied load increments, automatically chosen by the algorithm, is in general influenced both by the nature of the problem and of the discretization of the bodies involved. The frictional forces occurring in the slip zone of the contact area are treated as known tangential forces calculated from the normal forces in the previous iteration. This piecewise linear treatment of the frictional contact problem requires an innermost iteration loop over the applied tangential force.The tangential force must coincide with the coefficient of friction times the normal force obtained in the last iteration, otherwise a new tangential force has to be calculated and the system of equations must be solved for a new right hand side vector. The automatic incrementation technique is based on the fact that each iteration is a linear problem. A tentative load increment is used in the solution of a certain iteration. A linear scaling of this solution is performed afterwards. A load scale factor is calculated in each contact node pair where a change of contact condition will occur. The change in contact status corresponding to the node pair with the smallest load scale factor is the only change which is accomplished in a certain iteration. The uniqueness of this kind of contact problem with friction has not been mathematically proven for a general case.The method has been applied to a case of loading and unloading of an elastic halfspace by a rigid cylindrical stamp and compared to solutions by Spence and Turner.