1. Introduction 1.1 An Overview 1.2 Some Examples 1.3 A Brief History 1.4 A Chapter Summary 2. Probability 2.1 Introduction 2.2 Sample Spaces and the Algebra of Sets 2.3 The Probability Function 2.4 Conditional Probability 2.5 Independence 2.6 Combinatorics 2.7 Combinatorial Probability 2.8 Taking a Second Look at Statistics (Monte Carlo Techniques) 3. Random Variables 3.1 Introduction 3.2 Binomial and Hypergeometric Probabilities 3.3 Discrete Random Variables 3.4 Continuous Random Variables 3.5 Expected Values 3.6 The Variance 3.7 Joint Densities 3.8 Transforming and Combining Random Variables 3.9 Further Properties of the Mean and Variance 3.10 Order Statistics 3.11 Conditional Densities 3.12 Moment-Generating Functions 3.13 Taking a Second Look at Statistics (Interpreting Means) Appendix 3.A.1 MINITAB Applications 4. Special Distributions 4.1 Introduction 4.2 The Poisson Distribution 4.3 The Normal Distribution 4.4 The Geometric Distribution 4.5 The Negative Binomial Distribution 4.6 The Gamma Distribution 4.7 Taking a Second Look at Statistics (Monte Carlo Simulations) Appendix 4.A.1 MINITAB Applications Appendix 4.A.2 A Proof of the Central Limit Theorem 5. Estimation 5.1 Introduction 5.2 Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments 5.3 Interval Estimation 5.4 Properties of Estimators 5.5 Minimum-Variance Estimators: The Crami?½r-Rao Lower Bound 5.6 Sufficient Estimators 5.7 Consistency 5.8 Bayesian Estimation 5.9 Taking A Second Look at Statistics (Beyond Classical Estimation) Appendix 5.A.1 MINITAB Applications 6. Hypothesis Testing 6.1 Introduction 6.2 The Decision Rule 6.3 Testing Binomial Dataâ H0: p = po 6.4 Type I and Type II Errors 6.5 A Notion of Optimality: The Generalized Likelihood Ratio 6.6 Taking a Second Look at Statistics (Statistical Significance versus â Practicalâ Significance) 7. Inferences Based on the Normal Distribution 7.1 Introduction 7.2 Comparing Y-i?½ s/ vn and Y-i?½ S/ vn 7.3 Deriving the Distribution of Y-i?½ S/ vn 7.4 Drawing Inferences About i?½ 7.5 Drawing Inferences About s2 7.6 Taking a Second Look at Statistics (Type II Error) Appendix 7.A.1 MINITAB Applications Appendix 7.A.2 Some Distribution Results for Y and S2 Appendix 7.A.3 A Proof that the One-Sample t Test is a GLRT Appendix 7.A.4 A Proof of Theorem 7.5.2 8. Types of Data: A Brief Overview 8.1 Introduction 8.2 Classifying Data 8.3 Taking a Second Look at Statistics (Samples Are Not â Validâ !) 9. Two-Sample Inferences 9.1 Introduction 9.2 Testing H0: i?½X =i?½Y 9.3 Testing H0: s2X=s2Yâ The F Test 9.4 Binomial Data: Testing H0: pX = pY 9.5 Confidence Intervals for the Two-Sample Problem 9.6 Taking a Second Look at Statistics (Choosing Samples) Appendix 9.A.1 A Derivation of the Two-Sample t Test (A Proof of Theorem 9.2.2) Appendix 9.A.2 MINITAB Applications 10. Goodness-of-Fit Tests 10.1 Introduction 10.2 The Multinomial Distribution 10.3 Goodness-of-Fit Tests: All Parameters Known 10.4 Goodness-of-Fit Tests: Parameters Unknown 10.5 Contingency Tables 10.6 Taking a Second Look at Statistics (Outliers) Appendix 10.A.1 MINITAB Applications 11. Regression 11.1 Introduction 11.2 The Method of Least Squares 11.3 The Linear Model 11.4 Covariance and Correlation 11.5 The Bivariate Normal Distribution 11.6 Taking a Second Look at Statistics (How Not to Interpret the Sample Correlation Coefficient) Appendix 11.A.1 MINITAB Applications Appendix 11.A.2 A Proof of Theorem 11.3.3 12. The Analysis of Variance 12.1 Introduction 12.2 The F Test 12.3 Multiple Comparisons: Tukeyâ s Method 12.4 Testing Subhypotheses with Contrasts 12.5 Data Transformations 12.6 Taking a Second Look at Statistics (Putting the Subject of Statistics togetherâ the Contributions of Ronald A. Fisher) Appendix 12.A.1 MINITAB Applications Appendix 12.A.2 A Proof of Theorem 12.2.2 Appendix 12.A.3 The Distribution of SSTR/(kâ 1) SSE/(nâ k)When H1 is True 13. Randomized Block Designs 13.1 Introduction 13.2 The F Test for a Randomized Block Design 13.3 The Paired t Test 13.4 Taking a Second Look at Statistics (Choosing between a Two-Sample t Test and a Paired t Test) Appendix 13.A.1 MINITAB Applications 14. Nonparametric Statistics 14.1 Introduction 14.2 The Sign Test 14.3 Wilcoxon Tests 14.4 The Kruskal-Wallis Test 14.5 The Friedman Test 14.6 Testing for Randomness 14.7 Taking a Second Look at Statistics (Comparing Parametric and Nonparametric Procedures) Appendix 14.A.1 MINITAB Applications Appendix: Statistical Tables Answers to Selected Odd-Numbered Questions Bibliography Index

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An introduction to mathematical statistical and its applications / Richard J. Larsen, Morris L. Marx

digital signal processing a computer based approach is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the digital signal processing a computer based approach is universally compatible with any devices to read.

Digital Signal Processing A Computer Based Approach

Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda.

Copyright protection for the electronic distribution of text documents : Identification and protection of multimedia information

In this paper, different well-known features for image retrieval are quantitatively compared and their correlation is analyzed. We compare the features for two different image retrieval tasks (color photographs and medical radiographs) and a clear difference in performance is observed, which can be used as a basis for an appropriate choice of features. In the past a systematic analysis of image retrieval systems or features was often difficult because different studies usually used different data sets and no common performance measures were established.

Features for image retrieval: A quantitative comparison

An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture information. Then, Fisher vector encoding is used to encode the local patch descriptors (i.e., patch-based CLBP features) into a discriminative representation. To improve the discriminative power of feature representation, multiple sets of parameters are used for CLBP to generate multiple FVs that are concatenated as the final representation for an image. A kernel-based extreme learning machine (KELM) is then employed for classification. The proposed method is extensively evaluated on two public benchmark remote sensing image datasets (i.e., the 21-class land-use dataset and the 19-class satellite scene dataset) and leads to superior classification performance (93.00% for the 21-class dataset with an improvement of approximately 3% when compared with the state-of-the-art MS-CLBP and 94.32% for the 19-class dataset with an improvement of approximately 1%).

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

Digital linear phase notch filter design based on IIR all-pass filter application

We present a method for real-time scene classification which achieves high accuracy without a time consuming descriptor learning step and kernelized classifiers. Robustness of the classification is achieved by combining the powerful multi-channel Gabor-based descriptors and an ensemble of Extreme Learning Machines (ELM). We first extend the recently introduced Binary Gabor Patterns (BGP) to multi-channel images. This is done by extracting BGP over several color channels and embedding an additional compact color layout descriptor. Then we propose an effective method for the aggregation of multiple ELMs into a single classification system, which leads to significant classification accuracy improvements. The experimental evaluation demonstrates that multi-channel color information constantly improves classification results. The integration of multiple ELMs into an ensemble using the proposed aggregation strategy significantly outperforms linear SVM in terms of accuracy, and reaches results similar to the non-linear SVM while operating in real time. Therefore, an ensemble of ELMs with the proposed aggregation strategy could be used as an efficient alternative to the non-linear SVM for remote sensing image classification tasks.

Multi-channel descriptors and ensemble of Extreme Learning Machines for classification of remote sensing images

Most of the methods for video summarization relay on complicated clustering algorithms that makes them too computationaly complex for real time applications. In this paper we propose an efficient approach for video summary generation that does not relay on complex clustering algorithms and does not require frame length as a parameter. Our method combines MPEG-7 Color Layout Descriptors with adaptive threshold technique to detect shot boundaries. For each shot a keyframe is extracted and similar keyframes are eliminated in a simple manner. A MOS measure evaluation on a standard dataset show that the method produces video summaries of highest visual quality. Streszczenie. W artykule zaproponowano nieskomplikowany algorytm do tworzenia skrotow materialow wideo. Metoda lączy w sobie deskryptor warstwy koloru MPEG-7 z techniką progu adaptacyjnego, co pozwala na wykrywanie granic stopklatki. Dla wielu takich samych lub podobnych klatek, pozostawiana jest tylko jedna z nich. (Tworzenie skrotu wideo z wykorzystaniem wlaściwości koloru oraz technik progu adaptacyjnego).

Video summarization using color features and efficient adaptive threshold technique

A method for design of linear phase notch IIR filters is presented in this paper. The filter is realized in the form of a parallel connection of two all-pass sub-filters. In order to achieve the resulting filter with an approximately linear phase in the pass-band, the applied all-pass sub-filters should be also with a linear phase. The desired amplitude characteristic of a resulting filter is obtained by all-pass sub-filter phase approximation. It can be achieved by applying either a pure delay or an IIR all-pass filter in one branch. If a pure delay is positioned in one branch, equiripple phase of all-pass sub-filter from the other branch is guaranteed for an equiripple amplitude characteristic of the resulting filter. Efficiency of the proposed algorithm is demonstrated on a few examples. The algorithm exhibits a fast convergence and an easy determination of initial values. In the case where IIR all-pass sub-filters are placed in both branches, the resulting amplitude characteristic satisfies all the prescribed demands, but the amplitude characteristic is not equiripple even when both all-pass sub-filters have equiripple phase.

Design of IIR Notch Filter with Approximately Linear Phase

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors which is characterized by efficient extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The optimal descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets.

http://www.doiserbia.nb.rs/ft.aspx?id=0353-36701403399C

Saša V. Nikolić

Papers

Digital linear phase notch filter design based on IIR all-pass filter application

Multi-channel descriptors and ensemble of Extreme Learning Machines for classification of remote sensing images

Video summarization using color features and efficient adaptive threshold technique

Design of IIR Notch Filter with Approximately Linear Phase

Effective combining of color and texture descriptors for indoor-outdoor image classification