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Robust feature based image watermarking process :
Abstract :
A digital image watermarking scheme must be robust against a variety of possible attacks. A robust digital image watermarking scheme that combines image feature extraction and image normalization is proposed. The goal is to resist both geometric distortion and signal processing attacks.Here used feature extraction method called Mexican Hat wavelet scale interaction. The extracted feature points can survive a variety of attacks and be used as reference points for both watermark embedding and detection. The normalized image of an image (object) is nearly invariant with respect to rotations. As a result, the watermark detection task can be much simplified when it is applied to the normalized image. However, because image normalization is sensitive to image local variation, we apply image normalization to nonoverlapped image disks separately. The disks are centered at the extracted feature points. Several copies of a 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks.
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A new approach for image segmentation using pillar k-means algorithm :
Abstract :
This project presents a new approach for image segmentation by applying Pillar-Kmeans algorithm. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and red uce computation time. The system applies K-means clustering to the image segmentation after optimized by Pillar Algorithm. The Pillar algorithm considers the pillars placement which should be located as far as possible from each other to withstand against the pressure distribution of a roof, as identical to the number of centroids amongst the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in aspects of precision and computation time. It designates the initial centroids positions by calculating the accumulated distance metric between each data point and all previous centroids, and then selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric.
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Content-Based Image Retrieval using Color Moment and Gabor Texture Feature :
Abstract :
Content based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many indexing techniques are based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. Here proposed a content-based image retrieval method which combines color and texture features. To improve the discriminating power of color indexing techniques, we encode a minimal amo unt of spatial information in the color index. As its color features, an image is divided horizontally into three equal non-overlapping regions. From each region in the image, we extract the first three moments of the color distribution, from each color channel and store them in the index i.e., for a HSV color space, we store 27 floating point numbers per image. As its texture feature, Gabor texture descriptors are adopted. We assign weights to each feature respectively and calculate the similarity with combined features of color and texture using Canberra distance as similarity measure.
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A New Approach For Lossless Visible Watermarking :
Abstract :
A novel method for generic visible watermarking with a capability of lossless image recovery is proposed. The method is based on the use of deterministic one-to-one compound mappings of image pixel values for overlaying a variety of visible watermarks of arbitrary sizes on cover images. The compound mappings are proved to be reversible, which allows for lossless recovery of original images from watermarked images. The mappings may be adjusted to yield pixel values close to those of desired visible watermarks. Different types of visible watermarks, including opaque monochrome and translucent full color ones, are embedded as applications of the proposed generic approach. A two-fold monotonically increasing compound mapping is created and proved to yield more distinctive visible watermarks in the watermarked image. Security protection measures by parameter and mapping randomizations have also been proposed to deter attackers from illicit image recoveries.
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Automatic image segmentation using wavelets :
Abstract :
Model-Based image segmentation plays a dominant role in image analysis and image retrieval. To analyze the features of the image, model based segmentation algorithm will be more efficient compared to non-parametric methods. Hereproposed Automatic Image Segmentation using Wavelets (AISWT) to make segmentation fast and simpler. The approximation band of image Discrete Wavelet Transform is considered for segmentation which contains significant information of the input image. The Histogram based algorithm is used to obtain the number of regions and the initial parameters like mean, variance and mixing factor. The final parameters are obtained by using the Expectation and Maximization algorithm. The segmentation of the approximation coefficients is determined by Maximum Likelihood function.
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Block based feature level multi focus image fusion :
Abstract :
Here block-based multi-focus image fusion has been proposed. In the past it is impossible to obtain an image in which all the objects are in focus. Image fusion deals with creating an image in which all the objects are in focus. Thus it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. In block-based multi-focus fusion technique follow three steps. Firstly, ten pairs of images divided in to M×N block. Second, Extract the features of each block and feed forward to neural network. Last step is the trained neural network is then used to fuse any pair of multi-focus images.
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A moving target detection algorithm based on the dynamic background :
Abstract :
Here an unsupervised algorithm for detection of moving targets in highly dynamic scenes. These are scenes whose background is subject to stochastic motion, due to the presence of multiple moving objects (crowds), water, trees swaying in the wind, etc. The algorithm is inspired by biological vision. Target detection is posed as a problem of center surround saliency, which aims to identify the locations of the visual field of maximal contrast with the background. Contrast is defined in terms of both appearance and motion dynamics, and measured using mutual information between stochastic models, known as dynamic textures, which can account for complex motion. This enables very robust target detection in the classes of scenes which have traditionally proven most adverse to tracking. Extensive tests in the context of dynamic background subtraction have shown significantly superior performance to previous techniques.
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High Capacity Data Hiding based on Predictor and Histogram Modification :
Abstract :
Here proposed a high capacity image hiding technology based on pixel prediction and the difference of modified histogram. This approach is used the pixel prediction and the difference of modified histogram to calculate the best embedding point. This approach can improve the predictive accuracy and increase the pixel difference to advance the hiding capacity. We also use the histogram modification to prevent the overflow and underflow.
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Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
Abstract :
Here using the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method—the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.
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An Indian Coin Recognition System :
Abstract :
The system is proposed to design coin recognition by applying heuristic approach, based on the coin table.This table stores parameters of each coin. This method yields 97% of result in recognizing the coin image. It is also proposed to apply HT algorithm combining the features of a) Straight line detection HT algorithm, b) Curve detection HT algorithm and c) Circle detection HT algorithm. Using these three algorithms edge of the coin is recognized. The features of old coins and new coins of different denominations are considered for classification. Some coins are used in different countries have same parameters, but it has different value. This project concentrates on affine transformations such as simple gray level scaling, shearing, rotation etc. The coins are well recognized by zooming processes by which a coin size of the image is increased. This project presents a coin recognition method with rotation invariance. Indian Coins are classified based on different parameters for various values of coin such as shape, size, surface design, weight and so on. Hence, it is easy for the automatic machine to classify Indian coins.. There are many coin classification machines are available, but , the machine has to be designed for recognition of Indian coin. To increase the efficiency of the machine, they are to be embedded with proper source code.In this project, Sobel Filter, HA and HT are used to classify the coin image. It is proposed a method for realizing a simple automatic coin recognition system more effectively. The HT technique is used to recognize almost 100% of the coin image. Comparing to Sobel edge detection method, HA the HT gives better result.
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Comparison and Improvement of wavelet based image fusion :
Abstract :
A multi-resolution fusion algorithm, which combines aspects of region and pixel-based fusion. We use multi resolution decompositions to represent the input images at different scales, and introduce multi resolution or multi modal segmentation to partition the image domain at these scales. This segmentation is then used to guide the subsequent fusion process using wavelets. A region-based multi resolution approach allows us to consider low-level as well as intermediate- level structures, and to impose data-dependent consistency constraints based on spatial, inter- and intra-scale dependencies.The wavelets used in image fusion can be classified into three categories Orthogonal, Bi-orthogonal and non-orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decompression and reconstruction characteristics that lead to different fusion results. In this project the above three classes are being compared for their fusion results. Normally, when a wavelet transformation alone is applied the results are not so good. However if a wavelet transform and a traditional transform such as IHS transform or PCA transform are integrated for better fusion results may be achieved. Hence we introduce a new novel approach to improve the fusion method by integrating with IHS or PCA transforms.
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Video watermarking using discrete wavelet transforms :
Abstract :
Due to the extensive use of digital media applications, multimedia security and copyright protection has gained tremendous importance. Digital Watermarking is a technology used for the copyright protection of digital applications. Here, a comprehensive approach for watermarking digital video is introduced. We propose a hybrid digital video watermarking scheme based on Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). PCA helps in reducing correlation among the wavelet coefficients obtained from wavelet decomposition of each video frame thereby dispersing the watermark bits into the uncorrelated coefficients. The video frames are first decomposed using DWT and the binary watermark is embedded in the principal components of the low frequency wavelet coefficients. The imperceptible high bit rate watermark embedded is robust against various attacks that can be carried out on the watermarked video, such as filtering, contrast adjustment, noise addition and geometric attacks.
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Soft Morphological Color Image Processing :
Abstract :
Mathematical morphology is a powerful tool for image processing and analysis of binary, gray-scale and color images. Soft morphological filters form a class of filters with many desirable properties. They were introduced to improve the behavior of standard morphological filters in detail preservation and noise elimination. In this project a framework for soft morphological color image processing using a fuzzy model is presented. The basic morphological operations of soft erosion and dilation are defined by means of a vector ordering scheme that uses fuzzy if-then rules. Application to real color images demonstrate that the proposed operators are less sensitive to image distortion and to small variations in the shape of the objects and perform significantly better in impulse noise removal problems, compared to standard morphological operators.
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Video enhancement using spatial filtering method :
Abstract :
Video is the technology of electronically capturing, recording, processing, storing, transmitting, and reconstructing a sequence of still images representing scenes in motion. Digital video, and its associated technology, presents some significant advantages over analogical solutions mainly due to its potential and capability to become just another type of data that can be manipulated, stored and transmitted along with other types of digital data. Noise is any undesired information that contaminates an video. Noise appears in videos from a variety of sources. In typical videos the noise can be modeled with a salt-and-pepper (“impulse”) distribution.Noise is everywhere and thus we have to learn to live with it. Noise gets introduced into the data via any electrical system used for storage, transmission, and/or processing. In addition, nature will always plays a “noisy” trick or two with the data under observation.When encountering a video corrupted with noise then it needs to improve its appearance for a specific application. The techniques applied are application-oriented. Also, the different procedures are related to the types of noise introduced to the video. The enhanced process will be implementing by using MATLAB tool with GUI.
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Spatial Median Filter for Noise Removal in Digital Images:
Abstract :
Here , six different image filtering algorithms are compared based on their ability to reconstruct noise affected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. A new algorithm, the Spatial Median Filter, is introduced and compared with the current image smoothing techniques. Experimental results demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms. In addition, a modification to this algorithm is introduced to achieve more accurate reconstructions over other popular techniques.
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Bus passenger counting based on frame difference and improved Hough transform :
Abstract :
In order to improve the efficiency and accuracy of the bus passenger counting, an extracting method of moving object based on frame difference is proposed. This method can segment the moving area form the original image make use of the parallel projection and vertical projection of the subtraction between two adjacent frames. In this condition, a circles’ detection based on improved Hough transform is used to detect human’s heads and recognize body, in this way to improve operating rate and save memory overhead. The advantage of this method is easy to operate, high accuracy and is not affected by surroundings, weather and light.
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Pedestrian Detection in Crowded Scenes
Abstract :
we address the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. Our basic premise is that this problem is too difficult for any type of model or feature alone. Instead, we present a novel algorithm that integrates evidence in multiple iterations and from different sources. The core part of our method is the combination of local and global cues via a probabilistic top-down segmentation. Altogether, this approach allows to examine and compare object hypotheses with high precision down to the pixel level. Qualitative and quantitative results on a large data set confirm that our method is able to reliably detect pedestrians in crowded scenes, even when they overlap and partially occlude each other. In addition,the flexible nature of our approach allows it to operate on very small training sets.
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Artifact removal from EEG signals using adaptive filters in cascade:
Abstract :
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this project, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancel EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in olysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.
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Detection and classification of brain cancer using artificial neural network in MRI images :
Abstract :
Brain cancer detection in magnetic resonance images (MRI) is important in medical diagnosis because it provides information associated to anatomical structures as well as potential abnormal tissues necessary to treatment planning and patient follow up.Here, a Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement, morphological operations and feature extraction have been developed for detection of the brain tumor in the MRI images of the cancer affected patients. The extraction of texture features in the detected tumor has been achieved by using Gray Level Co-occurrence Matrix (GLCM). These features are compared with the stored features in the Knowledge Base. Finally a Neuro Fuzzy Classifier has been developed to recognize different types of brain cancers. The whole system has been tested in two phases firstly Learning/Training Phase and secondly Recognition/Testing Phase. The known MRI images of affected brain cancer patients obtained from Radiologist of Shah Hospital. The unknown samples of brain cancer affected MRI images are also obtained from Radiologist of Shah Hospital and were used to test the system. The system was found efficient in classification of these samples and responds any abnormality.
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Wavelet based palm print authentication system :
Abstract :
Palm print based personal verification has quickly entered the biometric family due to its ease of acquisition, high user acceptance and reliability. This project proposes a palm print based identification system using the textural information, employing different wavelet transforms. The transforms employed have been analyzed for their individual as well as combined performances at feature level. The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer. The analysis of these wavelets is carried out on 500 images, acquired through indigenously made image acquisition system. 500 palm print obtained from 50 users with 10 samples each have been collected over a period of six months and have been evaluated for the performance of the proposed system.
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Face Tracking in real time videos :
Abstract :
We present a real-time face tracker in this project. The system has achieved a rate of 30+ frames/second using an HP-9000 workstation with a frame grabber and a Canon VC-C1 camera. It can track a person’s face while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system.First, we present a stochastic model to characterize skin- color distributions of human faces. The information provided by the model is sufficient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second,a motion model is used to estimate image motion and to predict search window. Third, a camera model is used to predict and to compensate for camera motion. The system can be applied to tele-conferencing and many HCI applications including lip-reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.
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Denoising of Surveillance Video Using Adaptive Gaussian Mixture Model Based Segmentation Towards Effective Video Parameters Measurement :
Abstract :
In recent times, capturization of video became more Feasible with the advanced technologies in camera. Those videos get easily contaminated by noise due to the characteristics of image sensors. Surveillance sequences not only have static scenes but also dynamic scenes. Many efforts have been taken to reduce video noise. Averaging the frame as an image had limited denoising effect and resulted in blur. Such result will be avoided if we separate foreground and background, and make background to be averaged only. Recently, a number of video object segmentation algorithms have been discussed and unfortunately most existing segmentation algorithms are not adequate and robust enough to process noisy video sequences.Since the target video contains noise, a large area of background is incorrectly classified as moving objects and obvious segmentation error will appears. Therefore for robust separation, a segmentation algorithm based on Gaussian Mixture Models adaptive to light illuminations, shadow and white balance is proposed here. This segmentation algorithm processes the video with or without noise and sets up adaptive background models based on the characteristics of surveillance video to accomplish segmentation, reducing background noise by averaging and foreground noise by ML3D filter.
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Surveillance video denoising based on background modeling :
Abstract :
Because of the characteristics of photoelectric sensors and the working environment of cameras, real-time surveillance video contains much noise, which does not only decrease the subjective visual quality, but also increases the output bitrate of video encoder. The effect of partial spatio-temporal smoothing is not evident. According to the characteristics of surveillance video, we propose a novel algorithm based on video content, setting up adaptive background models to accomplish foreground segmentation, reducing background noise via model parameters and foreground noise via 3D median filter. To the sequences of “hall_monitor” polluted with Gaussian or Poisson noise, the results show that the new algorithm increases PSNR about 8 dB, and saves over 90% of encoder output bitrate.
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A Fast Image Compression Algorithm Based on SPIHT :
Abstract :
SPIHT and NLS (Not List SPIHT) are efficient compression algorithms, but the algorithms application is limited by the shortcomings of the poor error resistance and slow compression speed in the aviation areas. In this paper, the error resilience and the compression speed are improved. The remote sensing images are decomposed by Le Gall5/3 wavelet, and wavelet coefficients are indexed, scanned and allocated by the means of family blocks. The bit-plane importance is predicted by bitwise OR, so the N bit-planes can be encoded at the same time. Compared with the SPIHT algorithm, this improved algorithm is easy implemented by hardware, and the compression speed is improved. The PSNR of reconstructed images encoded by fast SPIHT is higher than SPIHT and CCSDS from 0.3 to 0.9db, and the speed is 4-6 times faster than SPIHT encoding process. The algorithm meets the high speed and reliability requirements of aerial applications.
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IRIS Recognition Using Neural Network :
Abstract :
Iris recognition is one of important biometric recognition approach in a human identification is becoming very active topic in research and practical application. Iris recognition system consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN.
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A Lossless Data Compression and Decompression Algorithm :
Abstract :
we propose a new two-stage hardware architecture that combines the features of both parallel dictionary LZW (PDLZW) and an approximated adaptive Huffman (AH) algorithms. In this architecture, an ordered list instead of the tree based structure is used in the AH algorithm for speeding up the compression data rate. The resulting architecture shows that it not only outperforms the AH algorithm at the cost of only one-fourth the hardware resource but it is also competitive to the performance of LZW algorithm (compress). In addition, both compression and decompression rates of the proposed architecture are greater than those of the AH algorithm even in the case realized by software.
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Human Motion Detection in video Surveillance :
Abstract :
This system aims at tracking an object in motion and classifying it as a Human or Non-Human entity, which would help in subsequent human activity analysis. The system employs a novel combination of an Adaptive Background Modeling Algorithm (based on the Gaussian Mixture Model) and a Human Detection for Surveillance (HDS) System. The HDS system incorporates a Histogram of Oriented Gradients based human detector which is well known for its performance in detecting humans in still images.
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Infrared Image enhancement using AWT :
Abstract :
This technique combines the benefits of homomorphic image processing and the additive wavelet transform. The idea behind this technique is based on decomposing the image into subbands in an additive fashion using the additive wavelet transform. This transform gives the image as an addition of subbands of the same resolution. The homomorphic processing is performed on each subband, separately. It is known that the homomorphic processing on images is performed in the log domain which transforms the image into illumination and reflectance components. Enhancement of the reflectance reinforces details in the image. So, applying this process in each subband enhances the details of the image in each subband. Finally, an inverse additive wavelet transform is performed on the homomorphic enhanced subbands to get an infrared image with better visual details.
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Implementation of Fingerprint Recognition :
Abstract :
Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint verification through extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of image enhancement and binarization is first applied on fingerprints before they are evaluated. Many methods have been combined to build a minutia extractor and a minutia matcher. Minutia marking with special consideration of the triple branch counting and false minutiae removal methods are used in the work. An alignment-based elastic matching algorithm has been developed for minutia matching. This algorithm is capable of finding the correspondences between input minutia pattern and the stored template minutia pattern without resorting to exhaustive search. Performance of the developed system is then evaluated on a database with fingerprints from different people.
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A Wavelet approach for Edge Detection :
Abstract :
Here presents a new approach to edge detection using wavelet transforms.First, we briefly introduce the development of wavelet analysis. Then, some major classical edge detectors are reviewed and interpreted with continuous wavelet transforms. The classical edge detectors work fine with high-quality pictures, but often are not good enough for noisy pictures because they cannot distinguish edges of different significance. The proposed wavelet based edge detection algorithm combines the coefficients of wavelet transforms on a series of scales and significantly improves the results. Finally, a cascade algorithm is developed to implement the wavelet based edge detector.
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Image denoising using discrete wavelet transformation :
Abstract :
The image de-noising naturally corrupted by noise is a classical problem in the field of signal or image processing. Additive random noise can easily be removed using simple threshold methods. De-noising of natural images corrupted by Gaussian noise using wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transform values. The wavelet de-noising scheme thresholds the wavelet coefficients arising from the standard discrete wavelet transform.In this paper, it is proposed to investigate the suitability of different wavelet bases and the size of different neighborhood on the performance of image de-noising algorithms in terms of PSNR.
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Image compression using different types of wavelets :
Abstract :
Data compression which can be lossy or lossless is required to decrease the storage requirement and better data transfer rate. One of the best image compression techniques is using wavelet transform. It is comparatively new and has many advantages over others. Wavelet transform uses a large variety of wavelets for decomposition of images. The state of the art coding techniques like EZW, SPIHT (set partitioning in hierarchical trees) and EBCOT (embedded block coding with optimized truncation) use the wavelet transform as basic and common step for their own further technical advantages. The wavelet transform results therefore have the importance which is dependent on the type of wavelet used. In this paper, different wavelets have been used to perform the transform of a test image and their results have been discussed and analyzed. The analysis has been carried out in terms of PSNR (peak signal to noise ratio) obtained and time taken for decomposition and reconstruction. SPIHT coding algorithm is considered as a basic standard in compression field using wavelet transform. In addition to wavelet analysis for simple decomposition, analysis of SPIHT coding algorithm in terms of PSNR for different wavelets is also carried out here. This analysis will help in choosing the wavelet for decomposition of images as per their application.
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Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform :
Abstract :
The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Eye-blinks and movement of the eyeballs produce electrical signals that are collectively known as Ocular Artifacts (OA). These are of the order of milli-volts and they contaminate the EEG signals which are of the order of micro-volts. The frequency range of EEG signal is 0 to 64 Hz and the OA occur within the range of 0 to 16 Hz. If the wavelet based EOG correction algorithm is applied to the entire length of the EEG signal, it results in thresholding of both low frequency and high frequency components even in the non-OA zones. This leads to considerable loss of valuable background EEG activity. Though the detection of OA zones can be done by visual inspection, the OA time zones need to be given as input to the EOG correction procedure, which is a laborious process. Hence there is a need for automatic detection of artifact zones. This project discusses a method to automatically identify slow varying OA zones and applying wavelet based adaptive thresholding algorithm only to the identified OA zones, which avoids the removal of background EEG information. Adaptive thresholding applied only to the OA zone does not affect the low frequency components in the non-OA zones and also preserves the shape (waveform) of the EEG signal in non-artifact zones which is of very much importance in clinical diagnosis.
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