Computer Science

Security issues can be a barrier to make successful online businesses because the Internet can make critical information vulnerable . Non-repudiation is a security feature that is related to integrity and authenticity . Providing non-repudiation for online communication is a key factor to achieve a successful electronic business . Non-repudiation should ensure that each involvement in an online interaction cannot be denied. Besides, non-repudiation, fairness between the parties also plays an important role to achieve successful electronic businesses. One solution used to achieve fair non-repudiation services is by using a trusted third party (TTP) that implements fair non-repudiation protocols . This project uses work that has been done by Cook et al in 2006 [5] as a starting point, which was a non-repudiation service project that uses SOAP web service technology. However, this project aims to implement non-repudiation services using Representational State Transfer (REST) architecture style principles in order to obtain significant advantages that REST technology provides such as scalability and simplicity. 184 accountability of participating in a transaction can be guaranteed by using the two types of non-repudiation evidences which are non-repudiation of origin (NRO) and non-repudiation of receipt (NRR) . The NRO is digital evidence generated by the originator and entitled to the recipient; while the NRR is digital evidence generated by the recipient and entitled to the originator.

Noise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms. key words: salt/pepper noise removal, impulsive noise, engineering drawing, binary image, document analysis and recognition Y (i, j) = median X (i−s, j−t) |(s, t) ∈ W (1) where Y (i, j) is the new output value and W is the window and its coordinates is defined relative to the center. If a 3 * 3 window is used, W is given by:

This paper presents the final report of the outcome of the sixth edition of the Arc Segmentation Contest. The theme of this edition is segmentation of images with different scanning resolutions. The contest was held offline before the workshop. Nine document images were scanned with three resolutions each and the ground truth images were created manually. Four participants have provided the output of their research prototypes. Two prototypes are more established while the other two are still in development. In general, vectorization methods produces better results with low resolution scanned images. Participants' comments on the behavior of their methods are also included in this report. A website devoted for this edition of the contest to hold the newly created dataset and other materials related to the contest is also available.

In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time.

In this paper a noise removal algorithm is proposed by adding a procedure to enhance noise removal to a third party algorithm as a post processing
step. The procedure (TAMD) has been proposed to enhance salt and pepper
noise removal. TAMD analyzes thin line blobs before deciding to retain or remove them. It has been successfully applied previously in two noise removal
algorithms by integrating their algorithm logic with the procedure. In this paper,
a noise removal algorithm is proposed by utilizing it as a post processing step.
The performance of the proposed noise removal algorithm is compared to many
other algorithms including state of the art methods such as median and center
weighted median. Real scanned images of mechanical engineering drawings corrupted
by 20% salt and pepper noise are used in the experiment. Objective performance
evaluation (PSNR and DRDM) has shown that our proposed noise removal algorithm is better than other studied algorithms.

In this paper we study different factors that affect vector quality. Noise level, cleaning method, and vectorization software are three factors that may influence the resulting vector data. Real scanned images from GREC'03 contest are used in the experiment. Three different levels of satt-and-pepper noise (5olo, l0%o, and l5o/o) are used. Noisy images are cleaned by six clianing algorithms and then three different commercial raster to vector software are used to vectorize the cleaned images. vector Recovery Index (vRI) is the performance evaluation criteria used in this study to ludge ttre quality of ttre resulting vectors compared to their ground truth data. Statiiticat analysis on the vRI values shows that vectorization software have the biggest influence on the quality ofthe resulting vectors.

This paper presents a system for recognizing English fonts from character images. The distance profile is the feature of choice used in this paper. The system extracts a vector of 106 features and feeds it into a support vector machine (SVM) classifier with a radial basis function (RBF) kernel. The experiment is divided into three phases. In the first phase, the system trains the SVM with different Gamma and C parameters. In the second phase, the validation phase, we validate and select the pair of Gamma and C values that yield the best recognition rates. In the final phase, the testing phase, the images are tested and the recognition rate is reported. Experimental results based on 27,620 characters glyph images from three English fonts show a 94.82% overall recognition rate.

Junction detection plays a significant role in document image recognition. High recognition rate of graphical primitives is correlated with the proper detection of junctions. In this paper, a junction detection algorithm is presented in thinning-based raster to vector conversion process. The method has three stages that leverage junction representation from pixels to features (i.e. junctions). The input image is thinned to its skeleton. Edges were found next and pixels with many neighbours are designated as a low level junction. Polygonal approximation on edges is used to detect L-junctions while connected component analysis is used to find intermediate junctions. Intermediate junctions with a distance less than a threshold are combined to form a high level X-and Y-junctions. Performance evaluation on mechanical engineering drawings shows precision rate of 82.38% and a recall rate of 97.29%.

In this paper, we show that averaging of the Vector Recovery Index (VRI) score for a test involving many images is not accurate and leads to bias. We demonstrate that the higher the difference in primitive count between the data files in an experiment, the higher the bias in calculating the VRI. Normalizing VRI scores is proposed to remove the bias and to get VRI scores that precisely reflects the performance based on images under scrutiny. Empirical performance evaluation on three datasets from the arc segmentation contests attached to International Workshops on Graphics Recognition 2005, 2009, and 2011 shows that the proposed normalization score provides accurate and realistic performance results than the unweighted average of VRI scores. The results based on the modified VRI score show that the vectorisation methods have lower performance than was usually thought.

Engineering drawings contain many curves that intersect at a complex junction. Detection of these junctions is vital for correct recognition of line drawings. In this paper we study the detection of junctions in document images. A threestage junction detection is proposed to detect junctions in document images. The first stage works at the pixel level. Any pixel with more than two neighbors is labeled as low-level-junction. The second stage works at connected component level. All low-level-junctions adjacent to other low-level-junctions are merged into mid-level-junctions. The third stage combines mid-level-junction at a distance less than a threshold into a high-level-junction. Experimental results on real scanned images of mechanical engineering drawings show a precision rate of 82.38% and a recall rate of 97.29%.

Empirical performance evaluation of raster to vector methods is an
important topic in the area of graphics recognition. By studying automatic vectorization methods we can reveal the maturity of the tested methods whether as a
research prototype or a commercial software. Arc Segmentation Contest held in
conjunction with the eighth IAPR International Workshop on Graphics Recognition (GREC09) is an excellent opportunity for researchers to present the results of
their proposed raster to vector methods. The contest provides a uniform platform
where the output of different methods can be analyzed. The relevance of the contest is further revealed by the creation of new test images with their ground truth
data. Old documents were used in this contest. Five methods participated (two
research prototypes and three commercial software). Two tests were performed
namely between-methods test (participated by all methods) and within-method
test (participated by only one method). This paper presents the results of the contest.

Removing noise in engineering drawing images is important before applying image analysis processes. Noise should be removed while keeping the fine detail of the image intact. A noise removal algorithm that can remove noise while retaining fine graphical elements is presented in this paper. The algorithm studies the neighborhood of thin lines before choosing to remove or retain it. Real scanned images from GREC'03 and GREC'05 arc segmentation contests corrupted by 15% uniform salt/pepper noise are used in this experiment. Objective distortion measurements including PSNR and MSE show that our algorithm gives better quality images compared with other methods.

Empirical performance evaluation of raster to vector conversion is a means of judging the quality of line detection algorithms. Many factors may affect line detection. This paper aims to study scanning resolution of raster images and its effects on the performance of line detection. Test images with three different scanning resolutions (200, 300, and 400 DPI) are vectorised using available raster to vector conversion software. The Vector Recovery Index scores calculated with reference to the ground truth images and the detected vectors are then obtained. These values are analysed statistically in order to study the effects of different scanning resolutions. From the results, Vextractor is found to be better (on average) compared to VPstudio and Scan2CAD. For all the three resolutions, Vextractor and VPstudio perform better than Scan2CAD. Different scanning resolutions affect the software differently. The performance of Vextractor and VPstudio increases from low resolution to moderate resolution, and then decreases with high resolution. The performance of Scan2CAD decreases with the increase in the resolutions.

Many factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection.

Noise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms.

This paper presents the final report of the outcome of the sixth edition of the Arc Segmentation Contest. The theme of this edition is segmentation of images with different scanning resolutions. The contest was held offline before the workshop. Nine document images were scanned with three resolutions each and the ground truth images were created manually. Four participants have provided the output of their research prototypes. Two prototypes are more established while the other two are still in development. In general, vectorization methods produces better results with low resolution scanned images. Participants' comments on the behavior of their methods are also included in this report. A website devoted for this edition of the contest to hold the newly created dataset and other materials related to the contest is also available.