Discrete Cosine Transform

The spatial resolution of numerical weather prediction and climate models is generally determined by their grid spacing (∆x) or spectral truncation and the numerical implementation of dynamical core and model parametrisations. For example features of the scale 2∆x and 3∆x are smoothed to avoid numerical instabilities (e.g., aliasing effects) and parameterisations in connection to advection, pressure gradient force, and subgrid-scale diffusion can only be well represented at dimensions of at least four times the grid spacing. Some parametrisations, however, generate energy at the grid-spacing scale. These multiple effects on the effective resolution of models are investigated in this study for three high resolution regional climate models (RCMs) in dependence of their grid spacing.

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with high performance 1,2 , clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec 4 and the DCT based AGP image codec 1 (DCT-AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm 5 with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform 6 . The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT-AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low-complexity and high performance codec may provide a new direction for the implementation of image compression.

Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into textindependent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). In this work, melfrequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network. This work firstly used sequential minimum optimization (SMO) learning technique for SVM that improve performance over traditional techniques Chunking, Osuna. The cepstrum coefficients representing the speaker characteristics of a speech segment are computed by nonlinear filter bank analysis and discrete cosine transform. The speaker identification ability and convergence speed of the SVMs are investigated for different combinations of features. Extensive experimental results on several samples show the effectiveness of the proposed approach.

This paper discusses the feasibility of coding a robust, undetectable, digital water mark on a standard 512*512 intensity image with an 8 bit gray scale. The watermark is capable of carrying such information as authentication or authorisation codes, or a legend essential for image interpretation. This capability is envisaged to find application in image tagging, copyright enforcement, counterfeit protection, and controlled access. The method chosen is based on linear addition of the water mark to the image data. Originally, the authors made use of one dimensional encoding using m-sequences [1],[2], a process which, whilst showing promise, had considerable shortcomings. This paper analyses constructions to extend this work to two dimensions and discusses compatibility of the technique with JPEG image transmission.

This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

An overview of channel coding techniques for data hiding in still images is presented. Use of codes is helpful in reducing the bit error probability of the decoded hidden information, thus increasing the reliability of the system. First, the data hiding problem is statistically modeled for the spatial and DCT domains. Then, the bene"ts brought about by channel diversity are discussed and quanti"ed. We show that it is possible to improve on this basic scheme by employing block, convolutional and orthogonal codes, again giving analytical results. It is shown that the use of superimposed pulses does not produce any bene"t when applying them to data hiding. The possibility of using codes at the &sample level' (that is, without repeating every codeword symbol) is introduced and its potential analyzed for both hard-and soft-decision decoding. Concatenated and turbo coding are also discussed as ways of approaching the hidden channel capacity limit for the sample level case. Finally, experimental results supporting our theoretical approach are presented for some cases of interest.

This paper presents an ECG compressor based on optimized quantization of Discrete Cosine Transform (DCT) coefficients. The ECG to be compressed is partitioned in blocks of fixed size, and each DCT block is quantized using a quantization vector and a threshold vector that are specifically defined for each signal. These vectors are defined, via Lagrange multipliers, so that the estimated entropy is minimized for a given distortion in the reconstructed signal. The optimization method presented in this paper is an adaptation for ECG of a technique previously used for image compression. In the last step of the compressor here proposed, the quantized coefficients are coded by an arithmetic coder. The Percent Root-Mean-Square Difference (PRD) was adopted as a measure of the distortion introduced by the compressor. To assess the performance of the proposed compressor, 2-minute sections of all 96 records of the MIT-BIH Arrhythmia Database were compressed at different PRD values, and the corresponding compression ratios were computed. We also present traces of test signals before and after the compression/decompression process. The results show that the proposed method achieves good compression ratios (CR) with excellent reconstruction quality. An average CR of 9.3:1 is achieved for PRD equal to 2.5%. Experiments with ECG records used in other results from the literature revealed that the proposed method compares favorably with various classical and state-of-the-art ECG compressors. 

A fast and simple method for content based retrieval using the DC-pictures of H.264 coded video without full decompression is presented. Compressed domain retrieval is very desirable for content analysis and retrieval of compressed image and video. Even though, DCpictures are among the most widely used compressed domain indexing and retrieval methods in pre H.264 coded videos, they are not generally used in the H.264 coded video. This is due to two main facts, first, the I-Frame in the H.264 standard are spatially predicatively coded and second, the H.264 standard employs Integer Discrete Cosine Transform. In this paper we have applied color histogram indexing method on the DC-pictures derived from H.264 coded I-frames. Since the method is based on independent I-frame coded pictures, it can be used either for video analysis of H.264 coded videos, or image retrieval of the I-frame based coded images such as advanced image coding. The retrieval performance of the proposed algorithm is compared with that the fully decoded images. Simulation results indicate that the performance of the proposed method is very close to the fully decompressed image systems. Moreover the proposed method has much lower computational load.

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1-D signals to form a database of features, which can be used to train a neural network with these features. The target detection can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a target exists or not. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral approach for landmine detection from both acoustic and GPR images at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.

Two new fast algorithms for computing the inverse modified discrete cosine transform (IMDCT) as used in the adaptive spectral entropy coder (ASPEC) [2] are proposed. A fixed-point error analysis is presented to determine the number of significant bits required for fixed-point implementations.

In this paper, we present the IRIS architectural synthesis system for high-performance digital signal processing. This tool allows non-specialists to automatically derive VLSI circuit architectures from high-level, algorithmic representations, and provides a quick route to silicon implementation. By incorporating a novel synthesis methodology, called the Modular Design Procedure, within the IRIS system, parameterised models of complex and innovative DSP hardware can be derived and automatically assembled to create new DSP systems. ...

In this paper, an algorithm is developed to apply Hann, Hamming, Blackman and related windows directly in the transform domain for the discrete cosine transform and discrete sine transform. These algorithms are useful in applications where windowing is required in order to minimize edge effects caused by implicit symmetries in the transform domain that are not replicated in the real-world data. Examples of such applications include data communication, adaptive system identification and filtering, real-time analysis of financial market data, etc. Software implementations in C language are also given.

In the last ten years, there has been much research on active noise control (ANC) systems and transaural sound reproduction (TSR) systems. In those fields, multichannel FIR adaptive filters are extensively used. For the learning of FIR adaptive filters, recursive-least-squares (RLS) algorithms are known to produce a faster convergence speed than stochastic gradient descent techniques, such as the basic least-mean-squares (LMS) algorithm or even the fast convergence Newton-LMS, the gradient-adaptive-lattice (GAL) LMS and the discrete-cosine-transform (DCT) LMS algorithms. In this paper, multichannel RLS algorithms and multichannel fast-transversal-filter (FTF) algorithms are introduced, with the structures of some stochastic gradient descent algorithms used in ANC: the filtered-x LMS, the modified filtered-x LMS and the adjoint-LMS. The new algorithms can be used in ANC systems or for the deconvolution of sounds in TSR systems. Simulation results comparing the convergence speed, the numerical stability and the performance using noisy plant models for the different multichannel algorithms will be presented, showing the large gain of convergence speed that can be achieved by using some of the introduced algorithms.

This paper examines finite field trigonometry as a tool to construct digital trigonometric transforms. In particular, by using properties of k-cosine function over a Galois field, the finite field discrete cosine transform is introduced. The finite field DCT pair in GF(p) is defined, having blocklengths that are divisors of (p+1)/2. A special case is the Mersenne finite field DCT, defined when p is Mersenne prime. In this instance block lengths that are power of two are possible and radix-two fast algorithms can be used to compute the transform.

The main focus in this research paper is to experiment deeply with, and find alternative solutions to the image segmentation and character recognition problems within the License Plate Recognition framework. Three main stages are identified in such applications. First, it is necessary to locate and extract the license plate region from a larger scene image. Second, having a license plate region to work with, the alphanumeric characters in the plate need to be extracted from the background. Third, deliver them to an character system (BOX APPROACH)for recognition. In order to identify a vehicle by reading its license plate successfully, it is obviously necessary to locate the plate in the scene image provided by some acquisition system (e.g. video or still camera). Locating the region of interest helps in dramatically reducing both the computational expense and algorithm complexity. For example, a currently common1024x768 resolution image contains a total of 786,432pixels, while the region of interest (in this case a license plate) may account for only 10% of the image area. Also, the input to the following segmentation and recognition stages is simplified, resulting in easier algorithm design and shorter computation times. The paper mainly work with the standard license plates but the techniques, algorithms and parameters that is be used can be adjusted easily for any similar number plates even with other alphanumeric set.

An efficient method for computing the discrete cosine transform (DCT) is proposed. Based on direct decomposition of the DCT, the recursive properties of the DCT for even length input sequence is derived, which is a generalization of the radix 2 DCT algorithm. Based on the recursive property, a new DCT algorithm for even length sequence is obtained. The proposed algorithm is very structural and requires fewer computations when compared with others. The regular structure of the proposed algorithm is suitable for fast parallel algorithm and VLSI implementation.

This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works.

With rapid advances in digital information processing systems, and more specifically in digital image processing software, there is a widespread development of advanced tools and techniques for digital image forgery. One of the techniques most commonly used is the Copy-move forgery which proceeds by copying a part of an image and pasting it into the same image, in order to maliciously hide an object or a region. In this paper, we propose a method to detect this specific kind of counterfeit. Firstly, the color image is converted from RGB color space to YCbCr color space and then the R, G, B and Y-component are splitted into fixed-size overlapping blocks and, features are extracted from the R, G and B-components image blocks on one hand and on the other, from the DCT representation of the R, G, B and Ycomponent image block. The feature vectors obtained are then lexicographically sorted to make similar image blocks neighbors and duplicated image blocks are identified using Euclidean distance as similarity criterion. Experimental results showed that the proposed method can detect the duplicated regions when there is more than one copy move forged area in the image and even in case of slight rotations, JPEG compression, shift, scale, blur and noise addition.

Due to the ever-growing usage of the computer, it has led to a rapid increment in the data that is being exchanged, published and stored in an assortment of digital form. This phenomenon has urged an active participation in the research area of the security domain. One of the main areas of this security domain in providing confidentiality, authentication and data integrity is steganography. This paper proposes a steganography technique for hiding information in images through reparation technique in frequency domain. The purpose of reparation is to correct any static deviation from the cover image after embedding hidden message. The proposed technique is able to withstand visual attacks and statistical attacks. Thus, it can be used to strengthen the security of a steganography system. The proposed technique will be tested by using chi-square steganalysis.

Subband coding has become quite popular for the source encoding of speech. This paper presents a simple yet efficient extension of this concept to the source coding of images. We specify the constraints for a set o f two-dimensional quadrature mirror filters (QMF's) for a particular frequency-domain partition, and show that these constraints are satisfied by a separable combination of one-dimensional QMF's. Bits are then optimally allocated among the subbands to minimize the mean-squared error for DPCM coding of the subbands. Also, an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask. Optimum quantization is employed with quantizers matched to the Laplacian distribution. Subband coded images are presented along with their signal-to-noise ratios (SNR's). The SNR performance of the subband coder is compared to that of the adaptive discrete cosine transform (DCT), vector quantization,-and differential vector quantization for bit rates of 0.67, 1.0, and 2.0 bits per pixel for 256 X 256 monochrome images. The adaptive subband coder has the best SNR performance.

A Phonocardiogram or PCG is a plot of high fidelity recording of the sounds and murmurs made by the heart with the help of the machine called phonocardiograph. It has developed continuously to perform an important role in the proper and accurate diagnosis of the defects of the heart. As usually with the stethoscope, it requires highly and experienced physicians to read the phonocardiogram. A diagnostic system based on Artificial Neural Networks (ANN) is implemented as a detector and classifier of heart diseases. The output of the system is the classification of the sound as either normal or abnormal, if it is abnormal what type of abnormality is present. In this paper, Based on the extracted time domain and frequency domain features such as energy, mean, variance and Mel Frequency Cepstral Coefficients (MFCC) various heart sound samples are classified using Support Vector Machine (SVM), K Nearest Neighbour (KNN), Bayesian and Gaussian Mixture Model (GMM) Classifiers. The data used in this paper was obtained from Michigan university website.

Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a new technique for human face recognition. This technique uses an image-based approach towards artificial intelligence by removing redundant data from face images through image compression using the two-dimensional discrete cosine transform (2D-DCT). The DCT extracts features from face images based on skin color. Featurevectors are constructed by computing DCT coefficients. A self-organizing map (SOM) using an unsupervised learning technique is used to classify DCT-based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. Face recognition with SOM is carried out by classifying intensity values of grayscale pixels into different groups. Evaluation was performed in MATLAB using an image database of 25 face images, containing five subjects and each subject having 5 images with different facial expressions. After training for approximately 850 epochs the system achieved a recognition rate of 81.36% for 10 consecutive trials. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed and memory utilization.

Automatic Number Plate Recognition (ANPR) has many applications as (highway electronic toll collection, Police applications, Average Speed Enforcement, Bus Lane Enforcement, Parking Management Systems, Journey Time Systems, detection of stolen vehicles, etc.).It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition which depends on Arabic language.

For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG's proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT-based method is specified for "lossy'' compression, and a predictive method for "lossless'' compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. This article provides an overview of the JPEG standard, and focuses in detail on the Baseline method.

This thesis describes a processor architecture called OneChip, which combines a fixed logic processor core and reconfigurable logic resources. Using the variable components of this architecture, the performance of speed-critical applications can be improved by customizing

This paper aims at development and performance analysis of a speaker dependent speech recognition system using MATLAB®. The issues that were considered are 1) Can Matlab, be effectively used to complete the aforementioned task, 2) Accuracy of the Gaussian Mixture Model used for parametric modelling, 3) Performance analysis of the system, 4) Performance of the Gaussian Mixture Model as a parametric modelling technique as compared to other modelling technique and 5) Can a Matlab® based Speech recognition system be ported to a real world environment for recording and performing complex voice commands. The aforementioned system is designed to recognize isolated utterances of digits 0-9. The system is developed such that it can easily be extended to multisyllabic words as well.

Abstract – The development of digital imaging especially digital imaging cause greater storage utilization information. That resulting digital image quality is better, but also create new problems which the amount of storage required. With greater storage, data exchange is also increasingly difficult. Compression is a way to overcome this problem. Compression is generally divided into two kinds, lossless compression and lossy compression. Discrete Cosine Transform is one of the Fourier Transform method that provides a discrete function by simply taking the cosine of complex exponentials. In image processing, Discrete Cosine Transform array data intensity is converted into an array of data to determine how fast the frequency distribution of intensity.

Keywords : Lossy Compression, Discrete Cosine Transform

In the source-filter model of speech production, physiologically, the source corresponds to the vocal fold vibrations and the filter corresponds to the spectrum-shaping vocal tract. Vocal tract-based features like the mel-frequency cepstral coefficients (MFCCs) have been shown to contain speaker information. However, voice source (VS)-based features have also been shown to perform well in speaker recognition tasks, thereby revealing that the VS does contain speaker information. Moreover, a combination of the vocal tract and VS-based features has been shown to give an improved performance, showing that the latter contains supplementary speaker information.
In this study, the existing techniques for extracting speaker information from the VS are reviewed, and it is observed that parametric features perform poorly than non-parametric features. Here, an attempt is made to propose an alternate way of characterizing the VS to extract speaker information, and to study the merits and shortcomings of the proposed speaker-specific features.
The integrated linear prediction residual (ILPR) is used as the VS estimate. It is hypothesized here that a speaker’s voice may be characterized by the relative proportions of the harmonics present in the VS. The pitch synchronous discrete cosine transform (DCT) is shown to capture these, and the gross shape of the ILPR in a few coefficients. The ILPR and hence its DCT coefficients are visually observed to have both inter and intra-speaker variability, and thus it is hypothesized that the distribution of the DCT coefficients may capture speaker information, and this distribution is modeled by a Gaussian mixture model (GMM).
The DCT coefficients of the ILPR (termed the DCTILPR) are directly used as a feature vector in speaker identification (SID) tasks. By conducting SID experiments on three standard databases, it is found that the proposed DCTILPR features fare comparably with the existing VS-based features. It is also found that the gross shape of the VS contains most of the speaker information, and the very fine structure of the VS does not help in distinguishing speakers, and instead leads to more confusion between speakers. The major drawbacks of the DCTILPR are the session and handset variability, but they are also present in the existing state-of-the-art speaker-specific VS-based features and the MFCCs, and hence seem to be common problems. There are techniques to compensate these variabilities, which need to be used when the systems using these features are deployed in an actual application.
The DCTILPR is found to improve the SID accuracy of a system trained with MFCC features by 12%, indicating that the DCTILPR features capture speaker information which is missed by the MFCCs. It is also found that a combination of MFCC and DCTILPR features on a speaker verification task gives significant performance improvement in the case of short test utterances. Thus, on the whole, this study proposes an alternate way of extracting speaker information from the VS, and adds to the evidence for speaker information present in the VS.

Document Image processing and Optical Character Recognition (OCR) have been a frontline research area in the field of human-machine interface for the last few decades. Recognition of Indian language characters has been a topic of interest for quite some time. The earlier contributions were reported in [1] and [2]. A more recent work is reported in [3] and [9]. The need for efficient and robust algorithms and systems for recognition is being felt in India, especially in the post and telegraph department where OCR can assist the staff in sorting mail. Character recognition can also form a part in applications like intelligent scanning machines, text to speech converters, and automatic language-to-language translators.

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.

Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a new technique for human face recognition. This technique uses an image-based approach towards artificial intelligence by removing redundant data from face images through image compression using the two-dimensional discrete cosine transform (2D-DCT). The DCT extracts features from face images based on skin color. Featurevectors are constructed by computing DCT coefficients. A self-organizing map (SOM) using an unsupervised learning technique is used to classify DCT-based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. Face recognition with SOM is carried out by classifying intensity values of grayscale pixels into different groups. Evaluation was performed in MATLAB using an image database of 25 face images, containing five subjects and each subject having 5 images with different facial expressions. After training for approximately 850 epochs the system achieved a recognition rate of 81.36% for 10 consecutive trials. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed and memory utilization.

The ability to discriminate between original documents and their photocopies poses a problem when conducting automated forensic examinations of large numbers of confiscated documents. This paper describes a novel frequency domain approach for printing technique and copy detection of scanned document images. Tests using a dataset consisting of 49 laser-printed, 14 inkjet-printed and 46 photocopied documents demonstrate that the approach outperforms existing spatial domain methods for image resolutions exceeding 200 dpi. An increase in classification accuracy of approximately 5% is achieved for low scan resolutions of 300 dpi and 400 dpi. In addition, the approach has the advantage of increased processing speed.

This paper presents a solution for user-independent recognition of isolated Arabic sign language gestures. The video-based gestures are preprocessed to segment out the hands of the signer based on color segmentation of the colored gloves. The prediction errors of consecutive segmented images are then accumulated into two images according to the directionality of the motion. Different accumulation weights are employed to further help preserve the directionality of the projected motion. Normally, a gesture is represented by hand movements; however, additional user-dependent head and body movements might be present. In the user-independent mode we seek to filter out such user-dependent information. This is realized by encapsulating the movements of the segmented hands in a bounding box. The encapsulated images of the projected motion are then transformed into the frequency domain using Discrete Cosine Transformation (DCT). Feature vectors are formed by applying Zonal coding to the DCT coefficients with varying cutoff values. Classification techniques such as KNN and polynomial classifiers are used to assess the validity of the proposed user-independent feature extraction schemes. An average classification rate of 87% is reported.

The Hankel transform of a function by means of a direct Mellin approach requires sampling on an exponential grid, which has the disadvantage of coarsely undersampling the tail of the function. A novel modified Hankel transform procedure, not requiring exponential sampling, is presented. The algorithm proceeds via a three-step Mellin approach to yield a decomposition of the Hankel transform into a sine, a cosine and an inversion transform, which can be implemented by means of fast sine and cosine transforms.

This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation. It operates at a faster speed than current methods and can avoid halo effects by using the median operation. We then consider the dehazing effects in compression by investigating the coding artifacts and motion estimation in cases of applying any dehazing method before or after compression. We conclude that better dehazing performance with fewer artifacts and better coding efficiency is achieved when the dehazing is applied before compression. Simulations for Joint Photographers Expert Group images in addition to subjective and objective tests with H.264 compressed sequences validate our conclusion.

With rapid advances in digital information processing systems, and more specifically in digital image processing software, there is a widespread development of advanced tools and techniques for digital image forgery. One of the techniques most commonly used is the Copy-move forgery which proceeds by copying a part of an image and pasting it into the same image, in order to maliciously hide an object or a region. In this paper, we propose a method to detect this specific kind of counterfeit. Firstly, the color image is converted from RGB color space to YCbCr color space and then the R, G, B and Y-component are splitted into fixed-size overlapping blocks and, features are extracted from the R, G and B-components image blocks on one hand and on the other, from the DCT representation of the R, G, B and Ycomponent image block. The feature vectors obtained are then lexicographically sorted to make similar image blocks neighbors and duplicated image blocks are identified using Euclidean distance as similarity criterion. Experimental results showed that the proposed method can detect the duplicated regions when there is more than one copy move forged area in the image and even in case of slight rotations, JPEG compression, shift, scale, blur and noise addition.

This paper presents a novel approach of adaptive visual tuning of a watermark in Discrete Cosine Transform (DCT) domain. The proposed approach intelligently selects appropriate frequency bands as well as optimal strength of alteration. Genetic Programming (GP) is applied to structure the watermark by exploiting both the characteristics of human visual system and information pertaining to a cascade of conceivable attacks. The developed visual tuning expressions are dependent on frequency and luminance sensitivities, and contrast masking. To further enhance robustness, spread spectrum based watermarking and Bose-Chadhuri-Hocquenghem (BCH) coding is employed. The combination of spread spectrum sequence, BCH coding and GP based non-linear structuring makes it extremely difficult for an attacker to gain information about the secret knowledge of the watermarking system. Experimental results show the superiority of the proposed approach against the existing approaches. Especially, the margin of improvement in robustness will be of high importance in medical and context aware related applications of watermarking. ß

Plants are a major source of food stuff, medicine and industry. However it is an important and difficult task to recognize species of crop on earth. Therefore it is pertinent to design an appropriate recognition system of crops. As the frontier of space technology is progressed rapidly, remote sensing provides very convenient resource for development in agriculture. In this paper, we proposed a new method for crop classification from satellite imagery. In this method, first of all, we assembled satellite imagery database of different crops and then extract Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) features from each crop and then classify them by different individual classifiers. At the end we described analysis of different classifiers that which classifier performed best. Various techniques have been compared against our proposed model for determining the supremacy of our model and results have shown significant improvement in accuracy and reliability.

This paper presents an in-plane rotation (tilt), illumination invariant pattern recognition framework based on the combination of the features extracted using Radon and discrete cosine transforms and kernel based learning for face recognition. The use of Radon transform enhances the low frequency components, which are useful for face recognition and that of DCT yields low dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. DCT applied on Radon projections provides frequency features. Further, polynomial kernel Fisher discriminant analysis implemented on these features enhances discrimination capability of these features. The technique is also robust to zero mean white noise. The feasibility of the proposed technique has been evaluated using FERET, ORL, and Yale databases.

Abstract The extension of the Fourier transform operator to a fractional power has received much attention in signal theory and is finding attractive applications. The paper introduces and develops the fractional discrete cosine transform (DCT) on the same lines, discussing multiplicity and computational aspects. Similarities and differences with respect to the fractional Fourier transform are pointed out

Real-time implementation of an assistive human-machine interface system based around tongue-movement ear pressure (TMEP) signals is presented, alongside results from a series of simulated control tasks. The implementation of this system into an online setting involves short-term energy calculation, detection, segmentation and subsequent signal classification, all of which had to be reformulated based on previous off-line testing. This has included the formulation of a new classification and feature extraction method. This scheme utilises the discrete cosine transform to extract the frequency features from the time domain information, a univariate Gaussian maximum likelihood classifier and a two phase cross-validation procedure for feature selection and extraction. The performance of this classifier is presented alongside a real-time implementation of the decision fusion classification algorithm, with each achieving 96.28% and 93.12% respectively. The system testing takes into consideration potential segmentation of false positive signals. A simulation mapping commands to a planar wheelchair demonstrates the capacity of the system for assistive robotic control. These are the first real-time results published for a tongue-based human-machine interface that does not require a transducer to be placed within the vicinity of the oral cavity.