But for Remote Sensing (RS) images, **image** signature plays an important role in classifying the data. Hence in this study, the **image** signatures are compared **using** **spectral** signature graph. RS data generally collected from the sensors on satellite platform are used for mapping land features. While **using** imagery mode, **spectral** signature curve is used extensively to understand and classify the feature **of** the land. Three **band** compressed color images are useful for visual quality but the RS data is used for feature extraction and classification **of** earth features **using** mathematical techniques. The digital numbers are rigorously processed **using** statistical technique. In this study, DWT (Buccigrossi and Simoncelli, 1999) is used for compressing the **multi** **spectral** Landsat5 **image**. Wavelets are advantageous over Fourier methods in analyzing the discontinuities and sharp spikes in **image**. **Wavelet** **Transform** (WT) is used to reduce the inter pixel redundancy in an **image** and the **performance** is analyzed **using** CR, MSE and PSNR. Based on this, some **of** the wavelets are selected and its DN values are calculated. **Using** these DN values the **spectral** signature graph is drawn. From the graph, a particular **wavelet** is selected based on the DN values. After **image** **compression**, the **image** classification is used to identify the liability between the original and compressed **image**. In this study, Maximum Likelihood classification is used to identify the information classes **of** interest and a statistical characterization **of** the reflectance for each class is developed and accuracy is assessed.

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The methods proposed for implementing fusion **of** **multi** focused images **using** HDWT take the following form in general. A pair **of** source images namely Pepsi is taken for study and are assumed to be registered spatially. The images are transformed into **wavelet** domain by taking HDWT **transform**. MATLAB codes are written to take the HDWT **of** the two images. In each sub-**band**, activity measure **of** the two images is compared based on the above proposed fusion methods at that particular scale and space. A fused **wavelet** **transform** is created by taking pixels from that **wavelet** **transform** that shows greater activity at the pixel locations. The inverse HDWT is the fused **image** with clear focus on the whole **image**. The **performance** **of** HDWT is measured in terms **of** RMSE, PSNR & QI and the results are tabulated in table I. The results **of** **image** fusion **using** HDWT are shown in figure 3. From the table, it is inferred that the proposed methods for HDWT based **image** fusion out performs the existing methods due to its directionality and approximate shift invariant.

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The above algorithms are based on the classification **of** hyperspectral data **using** the characteristic **of** Earth object. The classification can be viewed as an approach to remove the spatial correlation **of** hyperspectral data, and MLR is applied to each class. The residual **image** obtained by MLR is considered to be the noise. All **of** these methods firstly remove the spatial correlation by classification, and then remove the **spectral** correlation by MLR. Therefore, the accuracy **of** the estimation by the classification algorithms depends on the Earth objects. In recent years, **wavelet** proposed by Mallat (1989) has been widely used for **image** processing. **Wavelet** has a good time-frequency-localization property. As a result **of** **multi**-resolution approach, the signal power is concentrated in the low frequency subband by **wavelet** **transform**, and the high frequency subband describes non- stationary characteristics **of** the signal well. Thus **wavelet** **transform** can accurately capture the significant information about an object **of** interest **using** a sparse description, and remove the spatial correlation **of** the **image**. Therefore, this paper presents a noise estimation method **of** hyperspectral remote sensing **image**, which is based on MLR and **wavelet** **transform**. The method removes the spatial correlation **of** the residual **image** obtained by MLR via **wavelet** **transform**. At last the standard deviation is estimated from the median absolute value **of** the **wavelet** coefficients **of** the noisy signals in the high-frequency subbands. The experiments are tested on both simulated and real data. The simulated experiment is tested on AVIRIS data added with Gaussian white noise and real data experiment is tested on Hyperion data. Results show that the method can estimate the standard deviation **of** the noise in each **band** accurately.

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The opposite reasoning is also valid, i.e. when one approximates f **of** the lower resolution level, the signal information is lost (Mallat, 1999). Considering a signal represented with a given resolution (j-1), the scaling functions Φ(x) form a basis for a signal set. In this context, the signal and its detail are combined with a fi ner resolution level j. In such way, all approximation and details contents are obtained **using** a successive process **of** decimation, called convolutions. The **multi**-resolution **analysis** is consequently responsible for the division **of** the original signal in different scales **of** resolution, named sub-**band** codifi cation (Kaiser, 1994). For this procedure, in the **discrete** **wavelet** **transform**, each signal x(t) belonging to the space V j |V j = V j+1 + W j-1 can be expressed by the basis **of** each space, where W j is the orthogonal complement **of** the space V j . Therefore, **using** the coeffi cients generated in the fi rst approximation A 0 (k) belonging to the scale J (Eq. (6)), the coeffi cients A 1 (k) and D 1 (k), scale j-1, can be determined (Eq. (7)).

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ICA raised the fabric defect detection rate measured up to the **wavelet** **transform** and ICA alone. Latif- Amet et al. [20] used co-occurrence matrices and MRF (Markov Random Fields) by obtaining features from **wavelet** decomposed **image**. Jasper et al. [21, 22] applied **wavelet** **analysis**. Yang et al. [23] detailed adaptive **wavelet**- from single adaptive **wavelet** to the multiple adaptive wavelets. The results showed 98.2% success rate in the detection **of** 56 faulty fabric images including eight types **of** defects and 64 defect free ones. In reference [24], the study which was performed by **using** **wavelet** **transform** for the inspection **of** 45 defective fabric samples showed 100% accuracy rate. Liu and Qu [25] performed **wavelet** **transform** (db5 **wavelet**) on 30 images and succeeded classification at 95% rate **using** back- propagation NN. Guan and Shi [26] got a 92.5% success rate analyzing fabric images with db1, db2, db3 db4 **wavelet** at first level. Han and Shi [27] proposed a novel approach based on co-occurrence matrices extracted from approximation sub-**band** images. Jiang et al. [28] proposed optimal **wavelet** **transform** tree. The study was done with entropy and energy values **of** the sub-**band** images. Liu et al. [29] performed a classification on nonwoven fabrics **using** **wavelet** texture **analysis** and LVM. Su et al. [30] obtained a 96.5% success rate **using** **wavelet** energies and backpropagation NN. Guan [31] performed a one level **transform** for normal faults and multiple levels transforms for small defects on 160 fabric images. He classified them over 95% by **using** features as standard deviation, entropy, energy etc. Liu et al. [32] studied on nonwoven fabrics **using** **wavelet** energy and Bayesian NN. The 18 characteristic features **of** images obtained from third level decomposition provided a 98% success rate, additionally 24 features obtained from fourth level decomposition raised success up to 99%. Liang et al. [33] achieved 90% classification success rate by **using** **discrete** **wavelet**, statistical measurement and Fuzzy NN.

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The texture features are supposed to contain more information and hence they have been successfully analysed **using** Gabor filter earlier. There exist verification/identification systems based on extracted features **using** various transforms such as Fourier **Transform** [13] (where Fourier range and angle features have been extracted to identify the palm-print **image**) and [14] (where cepstum coefficients are matched), Karhunen-Loeve **transform** [5], **Discrete** Cosine **Transform**, Walsh **Transform** [18] etc. It has been observed that different transforms analyze the palmprint in different ways. Attempt has been done by Kekre et. al. [17] to combine more than one transforms to generate hybrid **transform** and use it for palmprint identification. Properties **of** many transforms can be combined in hybrid **transform** and hence the results are better than those obtained **using** single **transform**. This was further extended to bi-resolution hybrid wavelets [18] which analyze palmprints at only global and local levels **of** resolution and were successfully used for identification purpose. In this paper we present **multi**- resolution hybrid wavelets, which provide with palmprint **image** **analysis** at local, semi-global (at various levels) and global levels **of** resolution. Since palmprints have principle lines (less texture) which can be viewed at low resolution, minor lines (medium texture) which can be viewed at various resolutions at medium level and ridges and valleys (high texture) which can only be viewed at high resolution, it becomes essential to analyze the palmprint at various resolutions.

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The opposite reasoning is also valid, i.e. when one approximates f **of** the lower resolution level, the signal information is lost (Mallat, 1999). Considering a signal represented with a given resolution (j-1), the scaling functions Φ(x) form a basis for a signal set. In this context, the signal and its detail are combined with a fi ner resolution level j. In such way, all approximation and details contents are obtained **using** a successive process **of** decimation, called convolutions. The **multi**-resolution **analysis** is consequently responsible for the division **of** the original signal in different scales **of** resolution, named sub-**band** codifi cation (Kaiser, 1994). For this procedure, in the **discrete** **wavelet** **transform**, each signal x(t) belonging to the space V j |V j = V j+1 + W j-1 can be expressed by the basis **of** each space, where W j is the orthogonal complement **of** the space V j . Therefore, **using** the coeffi cients generated in the fi rst approximation A 0 (k) belonging to the scale J (Eq. (6)), the coeffi cients A 1 (k) and D 1 (k), scale j-1, can be determined (Eq. (7)).

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amp litudes[1]. The advantage **of** **wavelet** transforms is enable high **compression** ratios with good quality **of** reconstruction. The **Discrete** **Wavelet** **Transform** (DWT), The **Discrete** **Wavelet** **Transform** (DWT), which is based on sub-**band** coding, is found to yield a fast computation **of** **Wavelet** **Transform**. The one dimensional **wavelet** decomposition is first applied along the rows **of** the images, and then the results are decomposed along the columns. This results in four decomposed sub imagesL1, H1, V1, and D1 for each **image**. These sub images represent different frequency localizat ions **of** the original **image**.

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advantage **of** **using** PCA **transform** is to choose the suitable significant components into which to embed the watermark. In Yavuz [19], a reference **image** is generated from the cover **image** **using** PCA and the watermark is embedded according to the difference between the **image** and its reference. Kang [20] has proposed a new algorithm where advantage **of** the strength **of** both **multi**-**band** **wavelet** **transform** (MWT) and PCA is used. The watermark energy is distributed to **wavelet** coefficients **of** every detail sub-**band** efficiently to achieve better robustness and perceptual transparency. A hybrid scheme combining both DWT and PCA has been proposed by Mostafa et al. in [21]. The watermark was embedded into the first principle components and the mid-**band** coefficient **of** the PCA **wavelet** frame. In Sinha [22] a binary watermark is embedded into each **of** the video frames by the decomposition **of** the frames into DWT sub bands followed by block based PCA on the sub-blocks **of** the low frequency sub-**band**. The watermark is embedded into the principal components **of** the sub-blocks.

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Compressing an **image** is significantly different than compressing raw binary data. **Of** course, general purpose **compression** programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some **of** the finer details in the **image** can be sacrificed for the sake **of** saving a little more bandwidth or storage space. This also means that lossy **compression** techniques can be used in this area. The **discrete** **wavelet** is essentially sub **band**–coding system and sub **band** coders have been quit successful in speech and **image** **compression**. In this paper the standard **image** MASK is analyzed **using** **wavelet** techniques for **image** **compression**. The transformations used for **wavelet** transformations are Haar, Symlet, Coiflet and db4. The results in terms **of** **compression** ratio and MSE show that the Haar transformation has the best **performance**. The Haar **transform** is then used to compress the **image** up to four levels. The reconstructed **image** after every level **of** **compression** is also shown. It is clear that DWT has potential application in **compression** problem and the use **of** Haar transformation for **Wavelet** Transformation provides the best results.

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The effectiveness **of** the proposed scheme is explained **using** the following experimental results. Figure 6a and b are the watermarked and tampered watermarked **image**. Figure 6c is the generated watermark for the Barbara **image** (130×130). At the receiving side, once the watermark is retrieved, it is compared with the original watermark. Since the retrieved watermark is in permuted form, the receiver must generate the watermark as per the size **of** the received **image** and permute it **using** the same key. Now the two watermarks are compared for equality. If they differ at some position, then the respective 2×2 sub- block is marked as black as shown in Fig. 6e. Figure 6d shows the retrieved watermark after applying the inverse permutation. It reveals that some tamperings have been occurred in the watermarked **image**. After applying inverse permutation to Fig. 6e, the tampering positions are clearly located as shown in Fig. 6f. The tampering positions are marked in white blocks in the watermarked **image** as shown in Fig. 6g. The proposed scheme is able to identify even a single pixel modification. Table 1 shows the PSNR and MSE **of** the watermarked images.

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Kumar and Zhang (2006) proposed a new multimodal system for personal authentication by combining hand biometric features. The proposed method attempts to improve fingerprint verification by incorporating palm print and hand-shape features. The number **of** matched minutiae on the overlapping areas **of** the fingerprint images is used for computing matching scores. Distances **of** feature vectors are computed for palm print and hand-shape matching. During registration the fingerprint, palm print and hand- shape scores **of** the person are computed and stored. The combined scores is used to authenticate a user. Experiments were conducted **using** 100 users, the results showed that the proposed multimodal system achieved better accuracy.

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complex **wavelet** **transform** (CWT) has two real-valued DWTs. The DWT is shift variant because **of** the decimation operation exploited in the **transform** .because **of** this, a small shift in the input signal can cause a very different set **of** coefficients produced at the output. For that, Kingsbury [1, 7, 8 ] introduced a new kind **of** **wavelet** **transform**, called the DT-CWT or CWT, for short, which exhibits approximate shift invariant property and improves directional resolution when compared with that **of** the DWT. At each scale, the DT-CWT produces six directional sub-bands, oriented at ±15 o , ±45 0 , and ±75 0 while the DWT produces only three directional sub-**band** oriented at 0 o , 45 0 and 90 0 **transform**

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In this technique the **image** is divided into two equal sections. Then the mean **of** each local is calculated. The local with higher mean will be divided into another two sections. This step will be repeated until the mean **of** one **of** the locals is greater than a given high boundary threshold value, e.g. 200 for a gray scale 8-bit **image** representation. Then this local is referred as high intensity local. Similar steps will be done for the locals with the lower mean till the local with mean lower than a given lower boundary threshold is achieved. This local is referred as low intensity local. This division **of** the **image** into three locals (high intensity local, low intensity local, and the remaining part) is actually inspired from the HVS, as it was mentioned in the introduction. Now each local is equalized by **using** SVE+DWT technique [5] which can be explained as follows: As it was mentioned, the singular value matrix obtained by SVD contains the illumination information. The biggest singular value has the highest impact on the illumination **of** the **image**. Therefore, changing singular values will directly affect the illumination **of** the **image** hence the other information in the **image** will not be changed. Also it was mentioned that the illumination information is embedded in LL subband. The edges are concentrated in other subbands (i.e. LH, HL, and HH). Hence, separating the high frequency subbands and applying the illumination enhancement in LL subband only, will protect the edge information from possible degradation. After reconstructing the final **image** by **using** IDWT, the resultant **image** will not only be enhanced with respect to illumination, but also it will be sharper.

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Now-a-days X-rays are playing very important role in medicine. One **of** the most important applications **of** X- ray is detecting fractures in bones. X-ray provides important information about the type and location **of** the fracture. Sometimes it is not possible to detect the fractures in X-rays with naked eye. So it needs further processing to detect the fractures even at minute levels. To detect minute fractures, in this paper various edge feature extraction methods are analyzed which helps medical practitioners to study the bone structure, detects the bone fracture, measurement **of** fracture treatment, and treatment planning prior to surgery. The classical derivative edge detection operators such as Roberts, Prewitt, sobel, Laplacian **of** Gaussian can be used as edge detectors, but a lot **of** false edge information will be extracted. Therefore a technique based on orthogonal **wavelet** transforms like Haar, daubechies, coiflet, symlets are applied to detect the edges and are compared. Among all the methods, Haar **wavelet** **transform** method performs well in detecting the edges with better quality. The various **performance** metrics like Ratio **of** Edge pixels to size **of** **image** (REPS), peak signal to noise ratio (PSNR) and computation time are compared for various wavelets.

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Increasing growth **of** functionality in current IT trends proved the decision making operations through mass data mining techniques. There is still a requirement for further efficiency and optimization. The problem **of** constructing the optimization decision tree is now an active research area. Generating an efficient and optimized decision tree with **multi**-attribute data source is considered as one **of** the shortcomings. This paper emphasizes to propose a multivariate statistical method **Discrete** **Wavelet** **Transform** on **multi**-attribute data for reducing dimensionality and to **transform** traditional decision tree algorithm to form a new algorithmic model. The experimental results described that this method can not only optimizes the structure **of** the decision tree, but also improves the problems existing in pruning and to mine the better rule set without effecting the purpose **of** prediction accuracy altogether.

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Look up Table (LUT) based architecture have been designed for fast 2-D DCT computation that can be used in the DXT-ME scheme. For computation **of** 2-D DCT, we have used row-column decomposition approach that makes use **of** one dimensional DCT (1- D DCT) operations twice and each 1-D DCT computation involves the use **of** Chen’s algorithm. The Transformation coefficients **of** Chen’s matrices, C(i) (i:0,1,…,7)are multiplied with all possible combinations **of** the inputs that range from 0-255 (e.g.: C(i)*0, C(i)*1, C(i)*2,…, C(i)*255) and these combinations are stored in LUTs. To obtain the result **of** multiplication **of** the input and the coefficient, the input is used as pointer to index the LUTs. This results in a purely combinational logic to compute 1- D DCT. Thus, for computation **of** 1-D DCT, it requires a latency **of** just 1 clock cycle. This makes the proposed architecture very time efficient.

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Figure 1 presents the waves **of** a length **of** speech signal and the waves after three kinds **of** transforms, for convenience **of** comparison only the absolute parts are drawn for DFT. We can see from the figure that DCT is the best in binding energy. The transformed speech energy mainly concentrates on the low frequency area IJCSI International Journal **of** Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011

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Based on the proposed algorithm and chosen threshold values water extraction for the Landsat 5 TM and Spot 5 HRG had been carried out. Figure 7 shows result **of** water body extraction **of** Landsat 5 TM **image** and Figure 8 displays Spot 5 HRG water body extraction. Water body filled by solid red colour over 4, 3, 2 colour composites is showed on right sites in both figures. The study area on Land sat 5 TM **image** is larger than on the Spot 5 HRG. The study area includes Hanoi City and surrounding area. Main water bodies are lakes, ponds, rivers and canals. In colour composite SWIR=red, NIR=green and Green=blue vegetation appears as green, urban including housing, construction appear in purple colour. Clear water is showed in dark blue and turbid water in cyan colour with different level **of** brightness depending on sedimentation contamination grades.

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