In the last decade, watermarking applications have increased considerably. The main reason is that watermarking has emerged as a prospective technique, which can provide copyright protection and authentication of digital content. However, the disadvantage of watermarking is that it introduces small modifications in the original work and thus causes slight degradation. These modifications may be undesirable in some sensitive applications, like medical imagery, 3D reconstruction, and military applications. As a remedy to this problem, researchers have introduced the concept of reversible watermarking. The main objective of reversible watermarking scheme is to restore the watermarked image to its original state after watermark extraction. In this thesis, new reversible watermarking techniques as well as their novel applications are presented. In some of these techniques, computational intelligence (CI) approaches have been employed to improve the watermark capacity versus imperceptibility tradeoff. The research work is carried out in four phases. In the first phase, reversible watermarking is employed on medical imagery, which comprises regions of sensitive information. Slight modification in these regions affects the diagnostic analysis and thus can lead to wrong decisions. For this purpose, a novel reversible watermarking technique has been developed that utilizes genetic algorithm (GA) to improve capacity versus imperceptibility tradeoff. The algorithm makes use of block based companding technique, which helps in increasing the watermark capacity. Experimental analysis depicts that the developed watermarking technique provides good performance compared to the existing approaches. As the technique is reversible, therefore, it is even capable of embedding in the sensitive regions of the image. Reversible watermarking of images with depth information is discussed in the second phase of this work. 3D imaging is widely used in 3D gaming, robotics, controlling and routing devices etc. Different techniques and algorithms are reported to compute the depth information of an object. In this phase, depth information is computed through shape from focus algorithm. The depth information is reversibly embedded in its corresponding 2D image. This technique also utilizes GA to compute near-optimal threshold matrix for performance improvement in terms of capacity versus imperceptibility tradeoff. An additional attribute is achieved by using the threshold matrix for authentication purpose. The third phase focuses on reversible watermarking of 3D camera images. 3D cameras work on different principles for depth map computation. Cameras working on time of flight principle for depth Reversible Watermarking and Its Applications Page xviii map calculation are used in the experimental analysis of the proposed technique. The developed technique utilizes 3D information to embed as a watermark. In this way, protection and secure transmission of an image along with its corresponding depth map is provided. Two CI approaches, namely, differential evolution and a hybrid approach (comprising particle swarm optimization and differential evolution) are utilized to optimize the capacity and imperceptibility tradeoff. This technique is also able to provide authentication capability against manipulation and collage attacks. In the first three phases, CI is exploited to improve the performance of the proposed reversible watermarking techniques. However, CI approaches are more time and resource consuming. Therefore, in fourth phase, a novel and fast reversible watermarking technique is proposed based on histogram processing and down sampling. Histogram based reversible watermarking techniques are easy to implement and are computationally less expensive. A concept of down sampling is employed to generate a reference image and thus create more space for hiding bits. Block selection is used to generate a location map. However, the location map is required at the receiving side to perform extraction and recovery processes. An additional use of location map is devised, which makes the technique capable of authenticating digital images.
جسٹس بشیر احمد سعید افسوس ہے چند ماہ ہوئے جسٹس بشیر احمد سعید صاحب کابھی کم وبیش ۸۴ برس کی عمر میں دل کا دورہ پڑنے سے ان کے وطن مدراس میں انتقال ہوگیا۔ مرحوم بڑے فعال ومتحرک اورسرگرم وپُرجوش مسلمان تھے انہوں نے مسلمانوں کی تعلیمی ترقی کے لیے مدراس میں وہی کیا جوڈاکٹر عبدالغفور نے کیرالا میں کیا تھا، ’’جنوبی ہند کے مسلمانوں کی تعلیمی انجمن‘‘کے صدر کی حیثیت سے انہوں نے مدراس میں دو عظیم الشان کالج تعمیر کرائے ایک لڑکوں کے لیے اوردوسرا لڑکیوں کے لیے، آج یہ دونوں کالج مدراس یونیورسٹی کے نمایاں اورترقی یافتہ کالج سمجھے جاتے ہیں۔ مرحوم آل انڈیا شخصیت کے بزرگ تھے، سالہا سال وہ علی گڑھ یونیورسٹی کی مرکز کونسل اور کورٹ کے نہایت بااثر اورفعال ممبر رہے، آل انڈیا مسلم مجلس مشاورت اورآل انڈیا مسلم پرسنل لابورڈ دونوں کے سرگرم رکن تھے۔ سرکاری ملازمت سے سبکدوشی کے بعد ان کے شب و روز قومی کاموں میں ہی بسر ہوتے تھے۔ نماز،روزہ کے بڑے پابند،قرآن مجید کی تلاوت ان کے مشاغل یومیہ میں شامل، ندوۃ المصنفین کے شروع سے معاون تھے، برہان بڑے شوق سے پڑھتے اوراس کی بڑی قدر کرتے تھے، غرض کہ بڑی خوبیوں اوراعلیٰ اخلاق و صفات کے بزرگ تھے۔ حق گوئی اورحق پژوہی ان کاجوہر فطری تھا، انگریزی کے بڑے اچھے مقرر تھے، اردو میں بھی اظہار مدعاپر قادر تھے، تقریر بڑے جوشیلے انداز میں کرتے تھے۔ اس میں شک نہیں کہ ان کا نفس وجود مسلمانوں کے لیے بڑی تقویت کاسبب تھا کیونکہ وہ قانون دان بھی تھے اورمسلمانوں کے سچے ترجمان ووکیل بھی۔ [جولائی۱۹۸۴ء]
Sir Syed Ahmad Khan was interested in history and history-writing. His areas of interest were Islamic and South Asian history. In this essay, we examine certain relevant facets of this interest and its manifestation. There are a vast number of studies on the life, achievements and goals of Sir Syed Ahmad Khan [1817-1898]. His essays have been collected from his journalism in 18 volumes by Muhammad Ismail Panipati1 and his other writings, lectures and letters have also received editorial attention. A useful tool in this regard is the compilation titled Kitabiyat-i-Sir Syed made by Ziauddin Lahori.2 As this paper focuses on Sir Syed’s historical writings, we may mention here a recent doctoral thesis of Syeda Jabin Zehra on this very topic.3 As a matter of fact, any and all of Sir Syed’s writings be they on education or religion now come under the ambit of history but our narrower focus will be on those writings that deal with history as history alone though they may also admit of other classifications.
Facial expressions are the most effective form of non-verbal communication which is used to express one’s emotions. It not only expresses our expressions, but also communicates a wealth of information during social interaction. Facial expressions play an important and effective role in Human-Computer Interaction. Unlike humans, facial expression recognition is a challenging task for computers and research work in this domain can still be considered in seminal form. The prevalent facial expression techniques tend to provide accurate and impressive results for the images captured under controlled environment with a cooperative subject. However, majority of these techniques fail to provide adequate results for the images captured in uncontrolled environment. Achieving a higher classification accuracy rate for the images captured in uncontrolled environments is a major challenge being faced by researchers in this domain. There are many factors such as illumination, image orientations, noise and low-resolution, which hinder the development of facial expression recognition system in uncontrolled environment. Even the datasets prepared under the constrained environments sometimes need preprocessing before the classification stage. The core objective of this dissertation is to analyze existing techniques and develop an effective framework that is capable of classification of human facial expressions both under constrained and unconstrained environments. In this dissertation, we have explored, investigated and proposed frameworks to deal with two of the most common, influential and challenging issues that affect the accuracy of a facial expression recognition for images captured in an uncontrolled environment, namely illumination and low-resolution. First, a novel framework for feature extraction named Weber Local Binary Image Cosine Transform has been developed which not only extracts significant features by integrating features extracted using local binary pattern and weber local descriptor but also utilizes most discriminant features by applying the frequencybased components. Low-resolution and multi-orientation facial images have been used for the classification and a significant improvement in the classification accuracy rate has been achieved with the proposed framework. The developed framework has proved to be not only reliable but also computationally efficient across multiple datasets in the presence of noise and orientations. The Proposed framework has been tested on four datasets including JAFEE, MMI, CK+ and SFEW datasets. The second major contribution is the development of an illumination invariant technique. The proposed framework has been named Weber Local Binary Image Cosine Transform (WLBI-CT) and it advocates for the need of simultaneous contrast enhancement and brightness preservation for datasets containing real world images in unconstrained environment. The empirical results for the SFEW dataset are promising. In order to evaluate the performance of the proposed framework, rigorous set of experiments are presented in this thesis. The empirical results meet the standard quantitative measure criteria. The comparison of our work with various other stateof-the-art techniques is also presented using various benchmarks for these factors. The results are impressive even in the case of inclusion of noise and occlusion effects.