Zernike moments

Most of Content Based Image Retrieval (CBIR) system use global texture features for representing and retrieving images. If local texture features are ignored during the initial stage of image processing, the performance will be affected. Meanwhile the features extraction, if it is based on Color co-occurrence Matrix (CCM) will provide the opportunities effective CBIR. Therefore, the main objective of this paper avoids the performance ineffectiveness and the same time opting for much effective CBIR. The problems that were highlighted will be tacked by considering the approach that is based on the local Haralick’s texture features, specifically using Average Analysis (AA) and Principal Component Analysis (PCA) methods. The extraction by Haralick’s texture feature was based on the predetermined CCM. The experimentation was done on the suggested ten categories of 1000 selected images from the Coral image database. The results portray, it is interesting to note that for certain image cat...

Today, the number of registered trademarks is huge and is increasing rapidly. Thus, the job of identifying infringement of trademarks by solely using manual inspection is tiring, laborious and time consuming. To cope with the tremendous amount of available registered trademarks and to protect the infringement of trademarks, a new automatic and efficient trademark retrieval system is necessary and urgent. This paper presents an efficient content-based trademark retrieval based on the synergy between the colorspatial technique and zernike moment method. Zernike moments could be used as an effective descriptor of global shape of a trademark while the color-spatial feature could be used to obtain color spatial distribution in the trademark image. To retrieve visually similarly look trademark, we use a weight Euclidean distance measure. Experimental evaluations conducted on a database containing 1000 trademark images shows that the proposed methodology is very effective in retrieving visually similar look trademarks.

The matching and retrieval of the 2D shapes are challenging issues in object recognition and computer vision. The principal idea in this paper focus to propose an robust descriptor for indexing and retrieval Arabic hand print character. If we make an observation, we can show that some Arabic letters are completely similar with small deferent is represented by a dots. In this paper, we propose new hybrid descriptor based on Fourier coefficients. The proposed vector descriptor is constructed with Fourier coefficients and tow scalars parameters, the first parameter given by Euler number and the second represent the number of objects in the shape on the binary image. The choice of Fourier descriptor as part in the combination of proposed descriptor is based on studies of four well known descriptors based on, Zernike moments, Invariants Hu moments, Elliptic Fourier coefficients and Fourier coefficients. The comparative studies show that the hybrid descriptor gives good results using a test database constructed by Arabic Hand print letters. The results given by the proposed descriptor show the perfection and robustness of our method.

This paper represents a new approach for face recognition that incorporates Prewitt edge detection, Gabor filter and Zernike moments to transform the image into a unified domain. On this joint domain, five distance metrics are constructed using Schoenberg transform for the purpose of defining efficient similarity measures for holistic face recognition. The proposed Schoenberg similarity applies Schoenberg transform to the logarithm of five existing distance metrics: Minkowski, City-Block, Euclidean, Soergel and Lorentzian metrics. The constructed Schoenberg logarithmic metrics are called SL-Minkowski, SL-City-Block, SL-Euclidean, SL-Soergel and SL-Lorentzian. These distance metrics are utilized as similarity measures after being normalized over the range [0,1] for fair comparison with existing measures. The proposed Schoenberg system can resist three problems: Change in illumination, pose and facial expression. Simulation results show that the proposed distance measures have superio...

This paper reports the results of experiments in improving performance of leaf identification system using Principal Component Analysis (PCA). The system involved combination of features derived from shape, vein, color, and texture of leaf. PCA was incorporated to the identification system to convert the features into orthogonal features and then the results were inputted to the classifier that used Probabilistic Neural Network (PNN). This approach has been tested on two datasets, Foliage and Flavia, that contain various color leaves (foliage plants) and green leaves respectively. The results showed that PCA can increase the accuracy of the leaf identification system on both datasets.

There are situations where it is not possible to capture a large document with a given imaging media such as scanner or copying machine in a single stretch because of their inherent limitations. This results in capturing a large document in terms of split components of a ...

The study presents a plant recognition system that uses image and data processing techniques for recognition. A lot of research has been going on to identify plants by their leaves and one of the features that is used is the shape of the leaf but the accuracy is not high and therefore other features should also be considered to increase the accuracy. This system designed has three main steps which are image pre-processing, feature extraction and matching. Image pre-processing performs basic operations on the leaf image for segmentation which helps in making feature extraction easy. Seven (7) leaf features derived from geometric parameters of leaf shape were extracted from the pre-processed image and the simple principle of minimum Euclidean distance was used for finding the closest match to the input leaf image. The system used 10 species of leaves with a total of 50 leaf images from the flavia dataset for testing and obtained an accuracy above 90%. The algorithm is accurate and is easy to implement. However, it is slow and not tested on a large dataset. It is hoped that this proposed system will be exploited further and the speed will be improved and will also be able to give more information on the plant.

The study presents a plant recognition system that uses image and data processing techniques for recognition. A lot of research has been going on to identify plants by their leaves and one of the features that is used is the shape of the leaf but the accuracy is not high and therefore other features should also be considered to increase the accuracy. This system designed has three main steps which are image pre-processing, feature extraction and matching. Image pre-processing performs basic operations on the leaf image for segmentation which helps in making feature extraction easy. Seven (7) leaf features derived from geometric parameters of leaf shape were extracted from the pre-processed image and the simple principle of minimum Euclidean distance was used for finding the closest match to the input leaf image. The system used 10 species of leaves with a total of 50 leaf images from the flavia dataset for testing and obtained an accuracy above 90%. The algorithm is accurate and is easy to implement. However, it is slow and not tested on a large dataset. It is hoped that this proposed system will be exploited further and the speed will be improved and will also be able to give more information on the plant.

This paper represents a new approach for face recognition that incorporates Prewitt edge detection, Gabor filter and Zernike moments to transform the image into a unified domain. On this joint domain, five distance metrics are constructed using Schoenberg transform for the purpose of defining efficient similarity measures for holistic face recognition. The proposed Schoenberg similarity applies Schoenberg transform to the logarithm of five existing distance metrics: Minkowski, City-Block, Euclidean, Soergel and Lorentzian metrics. The constructed Schoenberg logarithmic metrics are called SL-Minkowski, SL-City-Block, SL-Euclidean, SL-Soergel and SL-Lorentzian. These distance metrics are utilized as similarity measures after being normalized over the range [0,1] for fair comparison with existing measures. The proposed Schoenberg system can resist three problems: Change in illumination, pose and facial expression. Simulation results show that the proposed distance measures have superio...

Handwritten signature is broadly utilized as personal verification in financial institutions ensures the necessity for a robust automatic signature verification tool. This tool aims to reduce fraud in all related financial transactions' sectors. This paper proposes an online, robust, and automatic signature verification technique using the recent advances in image processing and machine learning. Once the image of a handwritten signature for a customer is captured, several pre-processing steps are performed on it including filtration and detection of the signature edges. Afterwards, a feature extraction process is applied on the image to extract Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) features. Finally, a verification process is developed and applied to compare the extracted image features with those stored in the database for the specified customer. Results indicate high accuracy, simplicity, and rapidity of the developed technique, which are the main criteria to judge a signature verification tool in banking and other financial institutions.

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance,  2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.

For finger-vein recognition, many successful methods, such as Line Tracking (LT), Maximum Curvature (MC) and Wide Line Detector (WL), have been proposed. Among these, LT has a very slow matching and featureextraction phase, and LT, MC and WL are translation and rotation dependent. Moreover, we show in the paper, they are affected by noise. To overcome these drawbacks, we propose using popular feature descriptors widely used for several Computer Vision or Pattern Recognition (CVPR) problems in the literature. The CVPR descriptors we test include Histogram of Oriented Gradients (HOG), Fourier Descriptors (FD), Zernike Moments (ZM), Local Binary Patterns (LBP) and Global Binary Patterns (GBP), which have not been applied to the finger-vein recognition problem before. We compare these descriptors against LT, MC, and WL and evaluate their running times, performance and resilience against noise, rotation and translation. We report that the LT and WL methods accuracy are comparable to each other and WL gives the best accuracy, LT method's speed is the slowest. Our results indicate that WL can be used together with ZM and GBP in case of rotation and noise, respectively.

For finger-vein recognition, many successful methods, such as Line Tracking (LT), Maximum Curvature (MC) and Wide Line Detector (WL), have been proposed. Among these, LT has a very slow matching and featureextraction phase, and LT, MC and WL are translation and rotation dependent. Moreover, we show in the paper, they are affected by noise. To overcome these drawbacks, we propose using popular feature descriptors widely used for several Computer Vision or Pattern Recognition (CVPR) problems in the literature. The CVPR descriptors we test include Histogram of Oriented Gradients (HOG), Fourier Descriptors (FD), Zernike Moments (ZM), Local Binary Patterns (LBP) and Global Binary Patterns (GBP), which have not been applied to the finger-vein recognition problem before. We compare these descriptors against LT, MC, and WL and evaluate their running times, performance and resilience against noise, rotation and translation. We report that the LT and WL methods accuracy are comparable to each other and WL gives the best accuracy, LT method's speed is the slowest. Our results indicate that WL can be used together with ZM and GBP in case of rotation and noise, respectively.

Floating-point arithmetics may lead to numerical errors when numbers involved in an algorithm vary strongly in their orders of magnitude. In the paper we study numerical stability of Zernike invariants computed via complex-valued integral images according to a constant-time technique from [2], suitable for object detection procedures. We indicate numerically fragile places in these computations and identify their cause, namely-binomial expansions. To reduce numerical errors we propose piecewise integral images and derive a numerically safer formula for Zernike moments. Apart from algorithmic details, we provide two object detection experiments. They confirm that the proposed approach improves accuracy of detectors based on Zernike invariants.

This paper describes an approach based on Zernike moments and Delaunay triangulation for localization of handwritten text in machine printed text documents. The Zernike moments of the image are first evaluated and we classify the text as handwritten using the nearest neighbor classifier. These features are independent of size, slant, orientation, translation and other variations in handwritten text. We then use Delaunay triangulation to reclassify the misclassified text regions. When imposing Delaunay triangulation on the centroid points of the connected components, we extract features based on the triangles and reclassify the text. We remove the noise components in the document as part of the preprocessing step so this method works well on noisy documents. The success rate of the method is found to be 86%. Also for specific handwritten elements such as signatures or similar text the accuracy is found to be even higher at 93%.

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance,  2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance,  2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.

The paper proposes a new approach as a combination of the multiscale analysis and the Zernike moment based for detecting tampered image with the formation of copy – move forgeries. Although the traditional Zernike moment based technique has proved its ability to detect image forgeries in block based method, it causes large computational cost. In order to overcome the weakness of the Zernike moment, a combination of multiscale and Zernike moments is applied. In this paper, the wavelets and curvelets are candidates for multiscale role in the proposed method. The Wavelet transform has successful in balancing the running time and precision while the precision of the algorithm applied the Curvelets does not meet the expectation. The comparison and evaluation of the performance between the Curvelets analysis and the Wavelets analysis combining with the Zernike moments in a block based forgery detection technique not only prove the effective of the combination of feature extraction and mul...

This paper presents a new method to classify facial expressions from frontal pose images. In our method, first Pseudo Zernike Moment Invariant (PZMI) was used to extract features from the global information of the images and then Radial Basis Function (RBF) Network was employed to classify the facial expressions, based on the features which had been extracted by PZMI. Also, the images were preprocessed to enhance their gray-level, which helps to increase the accuracy of classification. For JAFFE facial expression database, the achieved rate of classification in our experiment is 98.33%. This result leads to a conclusion that the proposed method can ensure a high accuracy rate of classification. Index Terms-Facial expression classification, pseudo Zernike moment invariant, RBF neural network.

Center Symmetric Local Binary Pattern (CSLBP) is widely used in texture and object detection, but its utilization in image hashing is still limited. Image hashing is a powerful technique to identify whether image content is changed or preserved. Two main issues in image hashing are its compact length and its robust discrimination power. To deal with these issues, a robust image hashing method is proposed by combining features of CSLBP and Discrete Wavelet Transform(DWT). CSLBP is applied on LL band of DWT for compact length hash. Discrimination power of hashing is enhanced by weigh factor which comprises information from all sub bands of 1-Level DWT. All sub-band images of 1-Level DWT is combined to form secondary image. Local statistical features like Standard Deviation and Magnitude Average are calculated to obtain local weight factor MASD (Magnitude Average and Standard Deviation). Weighted LL-CSLBP histogram is constructed using this weight factor. Experimental results are demonstrated with Receiver Operating Characteristics (ROC) and Normalized Hamming Distance (NHD). The results shows that, the proposed scheme is robust against content preserving operations and sensitive to malicious operations.

The rapid growth of digital imaging techniques and versatility among the images has motivated the development of Content Based Image Retrieval (CBIR). CBIR is the application of computer vision techniques which uses the visual contents to retrieve relevant images from large databases according to user's interests. The visual contents (color, texture, shape etc) serve as the features for the images. Features are measurements of extreme interest analysed from an image. This paper proposes a novel combined Color-Size and texture features for CBIR system. A new type of visual feature named Color-Size feature which integrates the information of both color and size of the image in terms of number of segments is proposed. To implement the system, initially the images are segmented using Watershed segmentation approach. Different images would yield different number of segments that has to be taken into account for the extraction of features. From the segmented image the Color-Size featu...

The purpose of this project was to do a comparative study for content based image retrieval using DCT, DWT, LBP and curvelet transform texture descriptor. The aim is to investigate an efficient and effective technique for content based image retrieval from DCT, DWT, LBP and discrete curvelet transform. Wavelet transform has performed better for one dimensional signal as it is good in representing point discontinuities. The discrete cosine transform (DCT) helps separate the image into parts (or spectral subbands) of differing importance (with respect to the image's visual quality).A local binary pattern (LBP) texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. In this work, different type of hundred images were collected and stored as the database. Then Color images were converted into gray color by using gray level cooccurrence matrix. After that it performs decomposition up to three levels by using discrete wavelet transform and For discrete cosine transform it takes cosine transform (returns real values of images) so we get frequency features. For local binary pattern which checks the difference in pixel values within a block. Here we got local features. For discrete curvelet transform wrapping based method was there and features are extracted. Then indexing, similarity matching was done by using Euclidian distance method. Hence finally project has been demonstrated by using the plotted graphs one for the efficiency and another graph with respect to time of the system. The results obtained demonstrate that the curvelet transform based retrieval are more efficient then DCT, DWT, LBP based retrieval. The results obtained are in accordance with the expected predictions of the existing theory of curvelet transform.

So far, plant identification has challenges for several researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several experiments in using Zernike moments to build foliage plant identification systems. In this case, Zernike moments were combined with other features: geometric features, color moments and gray-level co-occurrence matrix (GLCM). To implement the identifications systems, two approaches has been investigated. First approach used a distance measure and the second used Probabilistic Neural Networks (PNN). The results show that Zernike Moments have a prospect as features in leaf identification systems when they are combined with other features.

Weapon detection is a vital need in dual-energy X-ray luggage inspection systems at security of airport and strategic places. In this paper, a novel weapon detection framework in high- energy images of X-ray dual-energy images based on shape and edge features is proposed. In this framework, image enhancement is carried out by two noise removal and histogram stretching operations. Also, Connected Component Analysis (CCA) is applied to detect weapon candidate regions. An optimum feature set such as Fourier descriptors and invariant moments features are selected by feature forward selection algorithm and used to classify the detected objects into weapon (illicit) and non-weapon (lawful) objects using Probabilistic Neural Network (PNN) classifier. The proposed framework is evaluated on a perfect database consisting of various weapons in different size, type and view and accuracy rate of 96.48% has obtained.

So far, plant identification has challenges for several researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several experiments in using Zernike moments to build foliage plant identification systems. In this case, Zernike moments were combined with other features: geometric features, color moments and gray-level co-occurrence matrix (GLCM). To implement the identifications systems, two approaches has been investigated. First approach used a distance measure and the second used Probabilistic Neural Networks (PNN). The results show that Zernike Moments have a prospect as features in leaf identification systems when they are combined with other features.

Several researches in leaf identification did not include color information as features. The main reason is caused by a fact that they used green colored leaves as samples. However, for foliage plants-plants with colorful leaves, fancy patterns in their leaves, and interesting plants with unique shape-color and also texture could not be neglected. For example, Epipremnum pinnatum 'Aureum' and Epipremnum pinnatum 'Marble Queen' have similar patterns, same shape, but different colors. Combination of shape, color, texture features, and other attribute contained on the leaf is very useful in leaf identification. In this research, Polar Fourier Transform and three kinds of geometric features were used to represent shape features, color moments that consist of mean, standard deviation, skewness were used to represent color features, texture features are extracted from GLCMs, and vein features were added to improve performance of the identification system. The identification system uses Probabilistic Neural Network (PNN) as a classifier. The result shows that the system gives average accuracy of 93.0833% for 60 kinds of foliage plants.

Problem statement : One challenging research area nowadays is pattern recognition. Many applications lay under the field of pattern recognition such as face and iris recognition, speech recognition, texture discrimination and optical character recognition. A system that recognizes isolated pattern of interest is called pattern recognition. The pattern under consideration could be an image. During the process of image recognition, many problems could occur such as noise, distortion, overlap, errors in the segmentation results and obstruction of objects in the image. Several approaches to handle and solve pattern recognition problems have been developed. Examples of such approaches are neural networks (NN), Contour matching, texture and color signature. Approach : The aim of this study is to develop a system to recognize isolated fish object in the image based on a combination between significant extracted features using anchor points, texture and statistical measurements. A generic f...

The retrieval and classification of shape-based objects employing three descriptors-generic Fourier descriptor (GFD), Legendre moment descriptor (LMD), and wavelet Zernike moment descriptor (WZMD) are described. All three descriptors have been applied for shape retrieval to a database of 1000 shapes from 20 different classes, where each class consists of 50 shapes. The Euclidean distance has been calculated as a similarity measure parameter for shape classification. To study the effect of noise on the retrieval and classification, additive and multiplicative noise of various variances were applied to the database. The classification results have been compared and it is inferred that WZMD performs better than GFD and LMD techniques. Precision and recall were measured as parameters of performance metric. For retrieved shape recognition, an optical experiment employing joint transform correlator architecture has been carried out.

This paper represents a new approach for face recognition that incorporates Prewitt edge detection, Gabor filter and Zernike moments to transform the image into a unified domain. On this joint domain, five distance metrics are constructed using Schoenberg transform for the purpose of defining efficient similarity measures for holistic face recognition. The proposed Schoenberg similarity applies Schoenberg transform to the logarithm of five existing distance metrics: Minkowski, City-Block, Euclidean, Soergel and Lorentzian metrics. The constructed Schoenberg logarithmic metrics are called SL-Minkowski, SL-City-Block, SL-Euclidean, SL-Soergel and SL-Lorentzian. These distance metrics are utilized as similarity measures after being normalized over the range [0,1] for fair comparison with existing measures. The proposed Schoenberg system can resist three problems: Change in illumination, pose and facial expression. Simulation results show that the proposed distance measures have superio...

Face localization using neural network is presented in this communication. Neural network was trained with two different kinds of feature parameters vectors; Zernike moments and Eigenfaces. In each case, coordinate vectors of pixels surrounding faces in the images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinates vector (R,θ) representing pixels surrounding the face contained in treated image. This way to proceed gives accurate faces contours which are well adapted to the faces shapes. Performances obtained for the two kinds of training feature parameters were recorded using a quantitative measurement criterion according to experiences carried out on the XM2VTS database.

Automated characterization of human actions plays an important role in video indexing and retrieval for many applications. Action change detection is considered among the most necessary element to ensure a good video description. However, it is quite challenging to achieve detection without prior knowledge or training. Usually humans are practicing different actions in the same video and their silhouettes give significant information for characterizing human poses in each video frame. We have developed an approach based on pose descriptors of these silhouettes, cross correlations matrices and Kullback-Leibler distance to detect action changes. In this paper, we will focus firstly on the specific problem of change detection in videos. After that, the proposed approach for action change detection will be detailed and tested on Weizman dataset. Finally, experimental results has been analyzed and showed the good performance of our approach.

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.

Research on Content Based Image Retrieval (CBIR) has become popular as it offers solutions to overcome or complement the drawbacks of Text Based Image Retrieval (TBIR). In CBIR, feature extraction and feature matching are two critical processes, which are of high importance to the retrieval performance of the system. This paper introduces a new approach to shape-based image retrieval by combining global and local shape features using Zernike moments (ZM) and edge-gradient co-occurrence matrix (£GCM) respectively. Two-stage matching strategy is then used to measure similarity between images. Our proposed method achieves higher precision rate compared to other commonly used shape feature.

Scanning baggage by x-ray and analysing such images have become important technique for detecting illicit materials in the baggage at Airports. In order to provide adequate security, a reliable and fast screening technique is needed for baggage examination.This paper aims at providing an automatic method for detecting concealed weapons, typically a gun in the baggage by employing image segmentation method to extract the objects of interest from the image followed by applying feature extraction methods namely Shape context descriptor and Zernike moments. Finally the objects are classified using fuzzy KNN as illicit or non-illicit object.

In most of classic plant identification methods a dichotomous or multi-access key is used to compare characteristics of leaves. Some questions about if the analyzed leaves are lobed, unlobed, simple or compound need to be answered to identify plants successfully. However, very little attention has been paid to make an automatic distinction of leaves using such features. In this paper we first explore if incorporating prior knowledge about leaves (categorizing between lobed simple leaves, and the unlobed simple ones) has an effect on the performance of six classification methods. According to the results of experiments with more than 1,900 images of leaves from Flavia data set, we found that it is statically significant the relationship between such categorization and the improvement of the performances of the classifiers tested. Therefore, we propose two novel methods to automatically differentiate between lobed simple leaves, and the unlobed simple ones. The proposals are invariant to rotation, and achieve correct prediction rates greater than 98%.

With the large uses of the intelligent systems in different domains, and in order to increase the drivers and pedestrians' safety, the road and traffic sign recognition system has been a challenging issue and an important task for many years. But studies, done in this field of detection and recognition of traffic signs in an image, which are interested in the Arab context, are still insufficient. Detection of the road signs present in the scene is the one of the main stages of the traffic sign detection and recognition. In this paper, an efficient solution to enhance road signs detection, including Arabic context, performance based on color segmentation, Randomized Hough Transform and the combination of Zernike moments and Haralick features has been made. Segmentation stage is useful to determine the Region of Interest (ROI) in the image. The Randomized Hough Transform (RHT) is used to detect the circular and octagonal shapes. This stage is improved by the extraction of the Haralick features and Zernike moments. Furthermore, we use it as input of a classifier based on SVM. Experimental results show that the proposed approach allows us to perform the measurement's precision.

Looney’s fuzzy reasoning algorithm supported by Fuzzy Petri Net(FPN)is further extended for representing the fuzzy production rules in a knowledge based system which is provided with fuzzy reasoning ability of fuzzy systems. In intuitionistic fuzzy sets(IFS)there is an extension of Zadeh Fuzzy sets that possess a new additional attribute function i,e non membership and intutionistic index take into account with stronger expression ability to deal with uncertain information. In this paper a rule based reasoning algorithm is presented for intuitionistic fuzzy petrinets. intuitionistic fuzzy members are used to represent the confidence degree and threshold of the transition along with token values of each place. A generalized fuzzy petri net to intuitionistic fuzzy petri net and vice versa is presented in an effective way. Some new features are added to take care of the generalization of the intuitionistic nature of the fuzzy petri nets.

In this paper, we propose various types of moments like Legendre moments, complex moments and Zernike Moments invariants. In the past decades has been extensively investigated about moment invariants. In Zernike moments we construct to derive set of invariants which is simultaneously invariant to similarity transformation and to convolution with spread spectrum(PSF).In this mainly two contributions are provided, The frame work for theoretical is derived from Zernike moments of a blurred image and the way to construct the one of invariant moment combined geometric blur invariants. To evaluate the performance of the invariants with various PSFs and similarity Transformations. Experimental results show that the propose dinvarients perform better to compared with other invariants like Legendre moments and complex Moments.

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.

Potholes on road causes majority of traffic incidents. It happens with driver’s unawareness to potholes on the road, while potholes location data mining is still performed manually by technician nowadays. This issues drives these following research and prototyping to be performed. This paper presents a prototype of a pothole detection system using image processing. The system will detect the potholes by assuming potholes as a blob, using blob detection. Live feed from a camera module will be processed by the algorithm that is embedded to a microcontroller simultaneously to determine the potholes. Every detected potholes and its location will be recorded and displayed on a webpage. The prototype will be an embedded system Linuxbased running on a Raspberry Pi, featured with camera and a computer program utilizing OpenCV. Several blob parameter such as shape, including circularity, convexity, and inertia value is utilized to optimize the pothole detection. The prototype is tested on ac...

Nowadays it is immediate need for best pre-screening tool to identify the abnormality of the mammogram images in the earlier stage itself. In this paper it is discussed about a tumor segmentation and classification algorithm from mammogram. The proposed approach concentrates on the result of two issues. One is the way to recognize tumors as suspicious regions may be very weak contrast to the background and the next is the way to concentrate properties which classify tumors. The proposed technique follows step by step procedures such as (a) Image Enhancement (b) Tumor Segmentation. (c) The extraction of properties from the segmented tumor region. (d) The utilization of SVM classifier. The improvement could be characterized as change of the image originality to a superior and more reasonable level. The mammogram enhancement can be obtained by removing the noise and improve the quality of the image using speckle noise removal and EM algorithm respectively. The most well-known division technique utilized is Modified Watershed Segmentation method. The features are extracted from the segmented tumor region and classify the regions utilizing the SVM classifier. The technique was tried on 100 mammographic images using MIAS and Apollo hospital based images. The system attained an Accuracy of 98%.

Robust hashing method is developed for detecting image forgery including removal, insertion, and replacement of objects, and abnormal color modification, and also copy move and spliced forgery, and for locating the forged area. Both global and local features are used in forming the hash sequence. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image as a whole. The local features include position and texture information of salient regions in the image. Secret keys are introduced in feature extraction and hash construction. While being robust against content preserving image processing, the hash is sensitive to malicious tampering and, therefore, applicable to image authentication. The hash of a test image is compared with that of a reference image. When the hash distance is greater than a threshold T1 and less than T2, the received image is judged as a fake. By decomposing the hashes, the type of image forgery and locati...

In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising resul...

The face image comprises different global and local details related to the whole and different local areas of the face image, therefore extract global features from the local areas leads to provide distinct distinguishing of the face images. In this, work a face recognition approach based on Jacobi-Fourier moments has been presented. Jacobi-Fourier moments used to provide global and distinct features about local areas in the face image. These features are used to construct histogram bins. Extensive experiments are carried out using different standard face database such as ORL, JAFEE and UMIST which are contain large variability in facial expression, pose and illumination. The experimental analysis refers that the proposed approach outperforms the traditional method under pose, facial expression, and different variations as well as it rotation invariant.

Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.

The explosive growth of image data leads to the research and development of image content searching and indexing systems. Image annotation systems aim at annotating automatically animage with some controlled keywords that can be used for indexing and retrieval of images. This paper presents a comparative evaluation of the image content annotation system by using the multilayer neural networks and the nearest neighbour classifier. The region growing segmentation is used to separate objects, the Hu moments, Legendre moments and Zernike moments which are used in as feature descriptors for the image content characterization and annotation.The ETH-80 database image is used in the experiments here. The best annotation rate is achieved by using Legendre moments as feature extraction method and the multilayer neural network as a classifier.

Image enhancement is becoming increasingly important with the advancement of space exploration techniques and the technological development of more durable and scientifically sound observatories equipped with more powerful telescopes. The enhancement of images helps astronomers analyze the results and act toward determining the dates of religious festivals. This work describes a technique known as contrast-limited adaptive histogram equalization (CLAHE) with grayscale contrast enhancement and bilateral filtering. We apply CLAHE on the L component of the CIE-Lab color space to adjust lightness contrast. Subsequently, grayscale contrast enhancement is performed to increase the visibility of the moon crescent. Noise caused by grayscale contrast enhancement is reduced using bilateral filtering. Two quantitative measures are selected (PSNR and MSE) to show the visual improvement achieved by the proposed algorithm.

Five power transform (a.k.a snowflake) distance metrics are constructed for the purpose of defining similarity measures for holistic face recognition. The power transform metrics are based on the logarithm of five distance metrics: Euclidean, City-Block, Soergel, Lorentzian and Minkowski metrics. The constructed power logarithmic metrics have been called PL-Euclidean, PL-City-Block, PL-Soergel, PL-Lorentzian and PL-Minkowski. Distance is considered in the Zernike domain of face images, a domain that contains a vector of Zernike moments for each face image. These distance metrics are further modified as similarity measures then normalized to be restricted over the range (0,1) so that a fair comparison could be made with the well-known image structural similarity measure (SSIM) and Feature Similarity Index for Image Quality Assignment (FSIM). The purpose is discovering the similarity of a given face image with a face-database to check for best match. A measure for quality of recogniti...