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M. H. Saad

Bio: M. H. Saad is an academic researcher from Egyptian Atomic Energy Authority. The author has contributed to research in topics: Color histogram & Image texture. The author has an hindex of 4, co-authored 7 publications receiving 60 citations.

Papers
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Journal ArticleDOI
TL;DR: A new image retrieval system, which uses color and texture information to form the feature vectors and Bhattacharyya distance and new similarity measure to perform the feature matching to enhance the retrieval results.
Abstract: Content-Based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Content-based image retrieval has many application areas such as, education, commerce, military, searching, biomedicine and web image classification. This paper proposes a new image retrieval system, which uses color and texture information to form the feature vectors and Bhattacharyya distance and new similarity measure to perform the feature matching. This framework integrates the ycbcr color histogram which represents the global feature and edge histogram as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard image databases such as Wang and UCID databases. Experimental work shows that the proposed approach improves the precision and recall of retrieval results compared to other approaches reported in literature.

22 citations

Journal ArticleDOI
TL;DR: An optimized robust watermarking method is proposed using Fractional Fourier Transform and Singular Value Decomposition and it is noticed that the proposed method can achieve a higher robustness degree when decreasing the quality threshold value.
Abstract: Digital watermarking is one of the most effective methods for protecting multimedia from different kind of threats. It has been used for many purposes, like copyright protection, ownership identification, tamper detection, etc. Many watermarking applications require embedding techniques that provide robustness against common watermarking attacks, like compression, noise, filtering, etc. In this paper, an optimized robust watermarking method is proposed using Fractional Fourier Transform and Singular Value Decomposition. The approach provides a secure way for watermarking through the embedding parameters that are required for the watermark extraction. It is a block-based method, where each watermark bit is embedded in its corresponding image block. First, the transform is applied to each block, and then the singular values are evaluated through which the embedding modification is performed. The optimum fractional powers, of the transform, and the embedding strength factor are evaluated through a Meta-heuristic optimization to optimize the watermark imperceptibility and robustness. The Artificial Bee Colony is used as the Meta-heuristic optimization method. A fitness function is employed, at the optimization process, through which the maximum achievable robustness can be provided without degrading the watermarking quality below a predetermined quality threshold Qth. The effectiveness of the proposed method is demonstrated through a comparison with recent watermarking techniques in terms of the watermarking performance. The watermarking quality and robustness are evaluated for different quality threshold values. Experimental results show that the proposed approach achieves a better quality compared to that of other existing watermarking methods. On the other hand, the robustness is examined against the most common applied attacks. It is noticed that the proposed method can achieve a higher robustness degree when decreasing the quality threshold value.

19 citations

Proceedings ArticleDOI
01 Dec 2011
TL;DR: A new image retrieval system, which uses color and Shape descriptions information to form the feature vectors and integrates the ycbcr color histogram which represents the global feature and Fourier descriptor as local descriptor to enhance the retrieval results.
Abstract: Content-Based Image Retrieval (CBIR) considers the characteristics of the image itself, for example its shapes, colors and textures. The Current approaches to CBIR differ in terms of which image features are extracted. Recent work deals with combination of distances or scores from different and independent representations. Content-based image retrieval has many application areas such as, education, commerce, military, searching, biomedicine and web image classification. This paper proposes a new image retrieval system, which uses color and Shape descriptions information to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the yc b c r color histogram which represents the global feature and Fourier descriptor as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard image databases such as Wang and UCID database. Experimental work show that the proposed approach improves the precision and recall of retrieval results compared to other approaches reported in literature.

18 citations

01 Jan 2012
TL;DR: A new image retrieval system, which uses color and geometric moment feature to form the feature vectors to enhance the retrieval results and a combination of this approach as a local image descriptor with other global descriptors outperforms other approaches.
Abstract: Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work d eals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

5 citations

Journal ArticleDOI
TL;DR: In this paper, a PSD method relied on a Fractional Discrete Cosine Transform (FrDCT) and a Support Vector Machine (SVM) classifier, which can be used to differentiate between two arbitrary types of scintillation pulses where each type has a different decay constant.
Abstract: One of the most serious errors in PET systems is the parallax error, which occurs due to spatial resolution limitation in PET. This error can be solved by using multilayer scintillation detector (phoswish detector) where each layer has a different decay constant. Each phoswish detector is optically coupled to a single PMT. Hence, by applying pulse shape discrimination (PSD) methods to identify the scintillated layer, the parallax error can be reduced. This paper proposes a PSD method relied on a Fractional Discrete Cosine Transform (FrDCT) and a Support Vector Machine (SVM) classifier. The proposed method can be used to differentiate between two arbitrary types of scintillation pulses where each type has a different decay constant. For demonstration of its performance, the proposed FrDCT-based method is applied to a data set of scintillation pulses of LSO and LuYAP crystals. Furthermore, FrDCT with different factors as well as different kernels of SVM; such as Linear, Quadratic, and Radial Basis Function (RBF) kernels, are used to study their effects on the discrimination efficiency. The highest achieved efficiency is equal to 95.9% for the 0.9 factor of FrDCT using a quadratic kernel SVM. On the other hand, the linear kernel consumes the lowest execution time at the expense of slightly reducing the discrimination efficiency.

4 citations


Cited by
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01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations

Journal ArticleDOI
TL;DR: This work introduces a novel approach of quantifying colors of colorimetric diagnostic assays with a smartphone that allows high accuracy measurements in a wide range of ambient conditions, making it a truly portable system.
Abstract: Paper-based immunoassays are becoming powerful and low-cost diagnostic tools, especially in resource-limited settings. Inexpensive methods for quantifying these assays have been shown using desktop scanners, which lack portability, and cameras, which suffer from the ever changing ambient light conditions. In this work, we introduce a novel approach of quantifying colors of colorimetric diagnostic assays with a smartphone that allows high accuracy measurements in a wide range of ambient conditions, making it a truly portable system. Instead of directly using the red, green, and blue (RGB) intensities of the color images taken by a smartphone camera, we use chromaticity values to construct calibration curves of analyte concentrations. We demonstrate the high accuracy of this approach in pH measurements with linear response ranges of 1–12. These results are comparable to those reported using a desktop scanner or silicon photodetectors. To make the approach adoptable under different lighting conditions, we developed a calibration technique to compensate for measurement errors due to variability in ambient light. This technique is applicable to a number of common light sources, such as sun light, fluorescent light, or smartphone LED light. Ultimately, the entire approach can be integrated in an “app” to enable one-click reading, making our smartphone based approach operable without any professional training or complex instrumentation.

516 citations

Journal ArticleDOI
TL;DR: A new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension, which overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based water marking schemes.

63 citations

Journal ArticleDOI
TL;DR: A robust double-encrypted watermarking algorithm based on the fractional Fourier transform and discrete cosine transform in invariant wavelet domain is proposed, which exhibits high robustness under the premise of satisfying security, reliability and invisibility.

49 citations

Proceedings ArticleDOI
03 Apr 2014
TL;DR: A novel algorithm for Content Based Image Retrieval (CBIR) based on Color Edge Detection and Discrete Wavelet Transform (DWT) is described, different from the existing histogram based methods.
Abstract: Color is one of the most important low-level features used in image retrieval and most content-based image retrievals (CBIR) systems use color as an image features. However, image retrieval using only color features often provide very unsatisfactory results because in many cases, images with similar colors do not have similar content. As the solution of this problem this paper describes a novel algorithm for Content Based Image Retrieval (CBIR) based on Color Edge Detection and Discrete Wavelet Transform (DWT). This method is different from the existing histogram based methods. The proposed algorithm generates feature vectors that combines both color and edge features. This paper also uses wavelet transform to reduce the size of the feature vector and simultaneously preserving the content details. The robustness of the system is also tested against query image alterations such as geometric deformations and noise addition etc. Wang's image database is used for experimental analysis and results are shown in terms of precision and recall.

47 citations