An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> The CEDAR real-time address block location system, which determines candidates for the location of the destination address from a scanned mail piece image, is described. For each candidate destination address block (DAB), the address block location (ABL) system determines the line segmentation, global orientation, block skew, an indication of whether the address appears to be handwritten or machine printed, and a value indicating the degree of confidence that the block actually contains the destination address. With 20-MHz Sparc processors, the average time per mail piece for the combined hardware and software system components is 0.210 seconds. The system located 89.0% of the addresses as the top choice. Recent developments in the system include the use of a top-down segmentation tool, address syntax analysis using only connected component data, and improvements to the segmentation refinement routines. This has increased top choice performance to 91.4%. > <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> A system for handwritten text localization in document images is proposed. Our system performs skew angle correction using the Wigner-Ville distribution and localizes the handwritten areas of the document based on several measures concerning regularity in shape (self-correlation, horizontal and vertical symmetry) and in dimensions (aspect ratio, distribution of heights and widths). The proposed technique was tested on a variety of documents and successfully handled more than 88% of the set while the misclassified areas in the rest of the documents did not exceed six in any document. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> The systems currently available for content based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localize artificial text in images and videos using a measure of accumulated gradients and morphological post processing to detect the text. The quality of the localized text is improved by robust multiple frame integration. Anew technique for the binarization of the text boxes is proposed. Finally, detection and OCR results for a commercial OCR are presented. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films. <s> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> The current paper presents a novel texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used, but rather the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, text regions are identified by applying a continuously adaptive mean shift algorithm (CAMSHIFT) to the results of the texture analysis. The combination of CAMSHIFT and SVMs produces both robust and efficient text detection, as time-consuming texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be texture-analyzed. <s> BIB005 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> In this paper, Delaunay triangulation is applied for the extraction of text areas in a document image. By representing the location of connected components in a document image with their centroids, the page structure is described as a set of points in two-dimensional space. When imposing Delaunay triangulation on these points, the text regions in the Delaunay triangulation will have distinguishing triangular features from image and drawing regions. For analysis, the Delaunay triangles are divided into four classes. The study reveals that specific triangles in text areas can be clustered together and identified as text body. Using this method, text regions in a document image containing fragments can also be recognized accurately. Experiments show the method is also very efficient. <s> BIB006 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> Abstract The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions , is introduced. Extremal regions possess highly desirable properties: the set is closed under (1) continuous (and thus projective) transformation of image coordinates and (2) monotonic transformation of image intensities. An efficient (near linear complexity) and practically fast detection algorithm (near frame rate) is presented for an affinely invariant stable subset of extremal regions, the maximally stable extremal regions (MSER). A new robust similarity measure for establishing tentative correspondences is proposed. The robustness ensures that invariants from multiple measurement regions (regions obtained by invariant constructions from extremal regions), some that are significantly larger (and hence discriminative) than the MSERs, may be used to establish tentative correspondences. The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes. Significant change of scale (3.5×), illumination conditions, out-of-plane rotation, occlusion, locally anisotropic scale change and 3D translation of the viewpoint are all present in the test problems. Good estimates of epipolar geometry (average distance from corresponding points to the epipolar line below 0.09 of the inter-pixel distance) are obtained. <s> BIB007 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> A fully affine invariant image comparison method, Affine-SIFT (ASIFT) is introduced. While SIFT is fully invariant with respect to only four parameters namely zoom, rotation and translation, the new method treats the two left over parameters : the angles defining the camera axis orientation. Against any prognosis, simulating all views depending on these two parameters is feasible. The method permits to reliably identify features that have undergone very large affine distortions measured by a new parameter, the transition tilt. State-of-the-art methods hardly exceed transition tilts of 2 (SIFT), 2.5 (Harris-Affine and Hessian-Affine) and 10 (MSER). ASIFT can handle transition tilts up 36 and higher (see Fig. 1). <s> BIB008 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> VIII. DISCUSSION AND FUTURE DIRECTIONS <s> The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. <s> BIB009
In this work, we reviewed BN-based techniques for image processing, medical domain, system reliability and estimation and PPDM, and in miscellaneous domains. We have summarized the literature on each domain and present the way of representing BN, drawing inferences and the learning of BN with an aim to quantify uncertainty issue. The study has also sought to know whether BN use in the aforementioned reviewed research fields lead us to improve results in challenging research area currently. In this perspective, we present application of BN in image processing for detecting text in scene images, system engineering with reliability estimation and PPDM, medical domain, decision making and miscellaneous BN-based approaches. BN-based MRI, image segmentation, shape descriptor, semantic features, video de-interlacing, and flaws in weldments, traffic knowledge management and Signature verification techniques are presented. Besides these BN-based techniques in image processing, text localization is a major challenge for researchers to recognize text in scene images. In recent years, features of image descriptor regions for text detection have emerged as a powerful tool in matching and recognition BIB004 ,  . The most popular feature descriptors are scale-invariant feature transform (SIFT) BIB009 and maximally stable extremal region (MSER) BIB008 , BIB007 . SIFT locates points in linear scale-space. However, SIFT is not affine-invariant BIB005 . MSER is an alternative to SIFT that extracts stable regions of an image which are connected and characterized by the uniform intensity. For this purpose, features of descriptor regions are extracted with the use of maximally stable extremal region (MSER) BIB008 , BIB007 . MSER is an efficient method to extract features in distorted images for text localization. However, features of an image descriptor region extracted by MSER are limited. Based on above BN-based techniques, BN can be computationally inefficient. Therefore, a combination of features and BN of a descriptor region extracted through MSER can help in improving accuracy to localize text in scene images. The advantage of accuracy can help visually impaired persons with head mounted device on sign boards in streets  . In future, BN in conjunction with features of MSER-based extracted descriptor region can also be applied to detect text in scene image video sequences BIB003 , postal address box localization BIB001 , automatic forms reading BIB002 , document image analysis BIB006 . Sensitivity analysis is an essential issue in system components which are working together to achieve a given target. To avoid abnormalities in a system, every component of a system and its operation is monitored to establish a relationship to make a reliable decision. The effective sensitivity analysis of system components according to their operation can provide a support to estimate the overall behavior of a system. In future, sensitivity analysis and abnormality detection Minimum Spanning Resource Tree (MSRT) can be analyzed by BN. Similarly, association rules mined from a supermarket database using data mining techniques can disclose sensitive information to business competitors. To restrict sensitive information, supermarket database should be perturbed for BN-based identified sensitive attributes in a highest degree transaction. The advantage of perturbing supermarket database, in this way, is to keep the minimum effect to original database with the maximum benefit with no side effects. Admitted patients in hospitals for treatment is also a sensitive research area to select the best medicine according to their clinical tests under an intensive care from nurses. In case of critical conditions, nurses look after by patrolling is not helpful to make a quick decision to choose the right medicine. The correct medicine under intensive care patient can be selected without any delay to wait for physicians. Usually, physicians make decisions on the historical data of a patient to give medicine. To make better decisions over time, BN can be incorporated to make predictions of medicine depending upon the current symptoms and historical data of a patient to eliminate delayed risks. Also, the best suitable clinical tests can be decided amongst a wide range of alternatives. Clinical tests are still a challenge especially to diagnose dental pain and exposure to X-ray radiation. As a result, a BN-based OPD tool is a critical need for better health care of people. Additionally, BN incorporation into Decision Support System (DSS) can automate law resources to predict the best possible decisions depending upon problem and previous judgments to support lawyers. BN can play an important role in decision making because of a significant involvement of uncertainty in this domain. The competitive businesses make decisions upon their own customer data increase profits by targeting new potential customers. Based on expenditure of customers, a major challenge is to select an appropriate attributes to predict new potential customers with minimum risk. Therefore, integration of BN into Decision Support Systems (DSS) can help in making decisions with multi-criteria decision making (MCDM) in a quick and consistent manner. In addition, water resources are diminishing and have been recycled in developed countries to fulfill the needs of their citizens. In future, water resource share management can be a challenging research problem in which every stakeholder is satisfied. BN incorporation to fair management of water resource share can involve all the active stakeholders. Currently, EU water framework is a challenging research problem. Consequently, all stakeholders around the world can lead us to save, recycle and distribute equal share of water based on EU water framework. BN represents a flexible framework that can be extended to combining domain knowledge from spatial data. BN can be made more effective by adding the temporal factor to analyze and assess earthquake hazards with accurate prediction. In addition, natural disasters such as flood, typhoon and landslide with risks involvement can be assessed based on historical data. Besides this, BN-based student engineering model is reviewed that support people in academia. Thus, BN allows determining the perception style of a student to discover his learning style over internet-based courses. To discover learning style of a student, BN can be applied over the features and semantic features to assess students' knowledge. Finally, accuracy and scalability of BN-based cross media analysis can be a major concern in future works.