Image fusion techniques merge the complementary information of several images (multi-focus, multi-exposure and multi-modal). Each of these scenarios poses different challenges for image fusion techniques, which are being extensively researched. However, most of these works assume that source images are preregistered, which is a less practical scenario. Both registered and unregistered image fusion algorithms are considered in this thesis. The registration involves the geometrical / spatial alignment of source images taken using different sensors or a sensor in different operating conditions. This research is concerned with the reliable fusion schemes of several scenario images (including muti-focus, Infra Red (IR) and visible, Computed Tomography (CT) and Magnetic Resonance (MR), and multi-exposure images) demonstrating high quality fused results without loss of useful information. The first scheme is a textural registration based multi-focus scheme involving the Gabor filtering (with specific frequency and orientation) for extracting texture features from the images. The filtered images are aligned/registered using affine transformation. Noise and blur play an important role in image fusion and need to be classified and treated for quality image fusion. The next two fusion schemes deal with multi-exposure noisy (real and synthetic both) and blur images. In the first algorithm, the noisy, blurry and clean images are classified using Laplacian filter and histogram spread. The noise is reduced in the frequency domain. Heavy weights are assigned to noise free pixels and the blur images are passed through the Wiener filter. In the second algorithm, a noise resistant image fusion scheme for multi-exposure sensors using color dissimilarity (for motion detection and removal), median and noise maps is proposed. A well exposed image is obtained as a result of weighted average of multi-exposure source images. Higher valued weights are assigned to pixels containing low values of noises, high values of color dissimilarity and median maps. The next work (two schemes) involve pre-registered visible and IR images. In the first one, a three stage image fusion scheme using Genetic Algorithm (GA) is presented. In the first stage, it segments the image into homogeneous regions and generates segmentation maps. In the second stage, the segmentation maps are combined by an adaptive weight adjustment procedure. The third stage fuses the input images and segmentation maps via GA based multi- objective optimization strategy. The second image fusion scheme uses Un-Decimated Dual Tree Complex Wavelet Transform (UDTCWT) for astronomical images. The UDTCWT reduces noise effects and improves object classification due to its inherited shift invariance property. Local standard deviation and distance transforms are used to extract useful information, especially small objects. In the medical (CT and MR) image fusion scheme, the source images are contrast enhanced using histogram equalization. It is a sparse decomposition based fusion technique that uses the dictionary learnt from input images and k-mean singular value decomposition algorithm. The scheme splits CT and MR images into texture and gradient images. The texture decomposition improves the overall performance of the sparse representation based fusion. The quantitative analysis performed using mutual information, structural similarity measure and edge dependent based performance metrics, yields improved results for proposed schemes, as compared to existing schemes.
جبر پر جو کتاب لکھوں گا پیار میں انتخاب لکھوں گا بے بسی دیکھ کر میری تو نے درد کو لذتوں کا رنگ دے کر حکم آیا غبار راہ ہو جا ہر ادا خود میں سمو لیتا ہے پھرے اویس آزردہ و آوارہ سا
صرف تم ،انتساب لکھوں گا تیری ایک ایک بات لکھوں گا جو کیا احتساب لکھوں گا وہی ہے امر خواب لکھوں گا بند قسمت کا باب لکھوں گا شیشہ بے حجاب لکھوں گا ہونگے وہ بھی بیتاب لکھوں گا
During Zulfiqar Ali Bhutto's term as Prime Minister, the law and order situation deteriorated throughout the country in general, and in the provinces of NorthWest Frontier Province (now Khyber Pakhtunkhwa) and Balochistan in particular. For multiple reasons, the mutual distrust between Zulfiqar Ali Bhutto and the political leaders of the respective provinces never allowed them to overcome their personal and party interests and establish a harmonious working relationship. Accusations of conspiracy, worsening law and order, and political assassinations, were the dilemmas that agitated the whole country. One of the victims of the political assassination was Hayat Muhammad Khan Sherpao, a leading activist of the Pakistan People's Party and former Governor of Khyber Pakhtunkhwa. The incident of his murder commenced the series of legal disputes and cases of treason filed against the opposition leaders in courts and tribunals, which marred the political scenario of the country till the very end of the Bhutto era. The lack of research on political assassinations in Pakistan is a crucial oversight, especially considering the frequency of the phenomenon and its implications. This study attempts to highlight the main theoretical and political implications of assassinations and identifies some promising directions for further research, in the hope that this unique type of political violence will be better understood in the future.
The thesis examines the valuation dynamics of Pakistani Initial Public Offerings (IPOs): their practices, motivations and implications. This study examines the pre-IPO valuation dynamics and the post-IPO price performance paradigms using 88 IPOs floated from 2000 to 2016 on the Pakistan Stock Exchange. The main objectives of this study includes: (1) to provide insights of preferred valuation methods when valuing IPOs, (2) to compare the bias and accuracy attached to each valuation methods, (3) to provide the usefulness of prospectus information on the initial valuations, the underpricing and the long-run underperformance, and finally, (4) to validate the long-run underperformance using calendar-time approaches. The binary logit model, the signed predictcion errors (SPE) and the absolute prediction errors (APE) were used to explain the choice, bias and accuracy attached to each valuation methods respectively. The accounting-based valuation model was used to estimate the impact of fundamental, risk and signaling factors on the post-IPO performance. The capital asset pricing model (CAPM), Fama-French three- (FF3F) and five-factor (FF5F) models were used as robust measures to affirm the long-run underperformance anomaly. The findings document that the Pakistani underwriters repeatedly used dividend discount model (DDM), discounted cash flow (DCF) model and the comparable multiples valuation methods when valuing IPOs. The findings of SPE reveal that the DDM and DCF methods seem to be unbiased value estimators than the comparable multiples. The findings of APE document that the DCF produce more valuation accuracy than the other valuation methods. The average underpricing of 32.85% was observed in the Pakistani primary market. This research extends the underpricing analysis in various aspects such as: (1) the level of underpricing was negatively related to the firm size, (2) the underpricing of IPOs issued in the hot-issue market was significantly higher than the IPOs issued in the cold-issue market, (3) the underpricing of IPOs issued through bookbuilding was lower than the IPOs issued through the fixed price auction, (4) the underpricing of privatization IPOs was higher than the underpricing of non-privatization IPOs, (5) the underpricing of survivor IPOs was higher than the underpricing of non-survivor IPOs, XI (6) the IPOs offered in the Oil & Gas and Chemicals sectors produce more underpricing than the other sectors. The finding of initial excess returns (IER) regression analysis reveals that the earnings, financial leverage, efficiency risk, firm beta and the underwriter reputation were the key determinants to explain variation in the level of underpricing. In the long-run returns (LRR) analysis, the buy and hold abnormal returns (BHAR) produce negative returns of -23.52% and -65.22% in year 3 and year 5 respectively. On the similar pattern, the cumulative abnormal returns (CAR) produce negative returns of -24.62% and -29.37% in year 3 and year 5 respectively. This study extend the long-run performance analysis in various aspects such as: (1) the IPOs issued in hot-issue market produce more negative returns than the IPOs issued during coldissue market, (2) the Automobile & Electrical Goods sector IPOs produce worst negative returns, while the Modaraba & Foods sector IPOs outperform the market in the long run, (3) the privatization IPOs outperform the market in the long run than the non-privatization IPOs. The finding of LRR regression analysis reveals that the book value of shareholder’s equity, earnings, capital availability risk, firm beta, underwriter reputation, the percentage of shares offered and initial excess returns were the significant determinants that explain the variation in the long-run returns.In the Calendar-time approach, the negative values of intercepts of CAPM, FF3F & FF5F validate the negative performance in the long run. The market risk premium was the most significant determinant in all asset pricing models, while HML-value factor (in equally-weighted FF5F) and CMA-investment factor (in value-weighted FF5F) were also significant determinants in the Fama-French five-factor models. This study is one of the few studies in IPO valuation literature that is being accomplished in a growing and transforming from loose regulated capital market to synchronize the state of affairs and first in Pakistan to investigate the explanatory power of prospectus information on IPO valuation dynamics.