Earticle

Multimedia security is extremely significant concern for the internet technology because of the ease of the duplication, distribution and manipulation of the multimedia data. The digital watermarking is a field of information hiding which hide the crucial information in the original data for protection illegal duplication and distribution of multimedia data. This paper presents a survey on the existing digital image watermarking techniques. The results of various digital image watermarking techniques have been compared on the basis of outputs. In the digital watermarking the secret information are implanted into the original data for protecting the ownership rights of the multimedia data. The image watermarking techniques may divide on the basis of domain like spatial domain or transform domain or on the basis of wavelets. The spatial domain techniques directly work on the pixels and the frequency domain works on the transform coefficients of the image. This survey elaborates the most important methods of spatial domain and transform domain and focuses the merits and demerits of these techniques.

Great effort has been put into the development of diagnosis methods for the most dangerous type of skin diseases - Melanoma. This paper aims to develop a prototype capable of segment and classify skin lesions in dermoscopy images based on ABCD rule. The proposed work is divided into four distinct stages: 1) Pre-processing, consists of filtering and contrast enhancing techniques. 2) Segmentation, thresholding and statistical properties are computed to localize the lesion. 3) Features extraction, Asymmetry is calculated by averaging the calculated results of the two methods: Entropy and Bi-fold. Border irregularity is calculated by accumulate the statistical scores of the eight segments of the segmented lesion. Color feature is calculated among the existence of six candidate colors: white, black, red, light-brown, dark-brown, and blue-gray. Diameter is measured by the conversion operation from the total number of pixels in the greatest diameter into millimeter (mm). 4) Classification, the summation of the four extracted feature scores multiplied by their weights to yield a total dermoscopy score (TDS); hence, the lesion is classified into benign, suspicious, or malignant. The prototype is implemented in MATLAB and the dataset used consists of 200 dermoscopic images from Hospital Pedro Hispano, Matosinhos. The achieved results shows an acceptable performance rates, an accuracy 90%, sensitivity 85%, and specificity 92.22%.

In this work, we have presented a hierarchical scheme for classifying music data. Instead of dealing with large variety of features, proposed scheme relies on MFCC and its variants which are introduced at the different stages to satisfy the need. At the top level music is classified as song (music with voice) and instrumental (music without voice) based on MFCC. Subsequently, instrumental signals and songs are classified based on instrument type and genres respectively. Hierarchical approach has been followed for such detailed categorization. Using two-stage process, instrumental signals are identified as one of the four types namely, string, woodwind, percussion or keyboard. Wavelet and MFCC based features are used for this purpose. For song classification, at first level signals are categorized as classical or non-classical(popular) ones by capturing the MFCC pattern present in the high sub-band of wavelet decomposed signal. At second level, we consider the task of further classification of popular songs into various genres like Pop, Jazz, Bhangra (an Indian genre) based on amplitude variation pattern. RANSAC has been utilized as the classifier at all stages. Experimental result indicates the effectiveness of the proposed schemes.

A novel local region-based active contour model is proposed to segment medical images with intensity inhomogeneities and various noises. The contribution of the proposed work is twofold. First, the anisotropy of evolution contours is exploited to characterize the local classification information around each pixel. Integrating it with local gray intensity information, the new model stabilizes the active contours in all evolving processes. Second, under the constraint of maximum absolute error of parameter estimation, the optimal spatial scales are automatically selected for the local segmentation models. It is demonstrated from the experiments that our algorithm achieves faster and more robust results than several same-type methods.

Design of fractional order analog filter, using a single Operational Transresistance Amplifier (OTRA) as an active current mode building block, is presented. The order of the proposed filter is 1.5. The theoretical results have been verified with PSPICE simulation. Obtained filter was realized using OTRA using the RC-RC decomposition technique. Frequency response for the presented filter is shown in the paper. The proposed filter offers some important features: employing single operational transresistance amplifier as an active element, lower sensitivity, insensitive to stray capacitances and parasitic resistances due to internally grounded input terminals of OTRA. Simulation results agree well with the theoretical values.

Vehicle detection and tracking applications play an important role for military and civilian applications such as in highway traffic surveillance control management and traffic planning.This paper presents a review on the various techniques of On-Road Vehicle detection systems that are based on motion model. In this paper a literature Survey of previous and recent works is presented on vision-based vehicle detection using sensors. Detecting the objects in the video and tracking their motion to identify their characteristics has been emerging as a demanding research area in the domain of Image Processing and Computer Vision. The traffic image analysis comprises of three parts: (1) Traffic Analysis (2) Motion Vehicle Detection and Segmentation Approaches and (3) Vehicle Tracking Approaches. In this survey, we have classified these methods into various groups, and these groups are providing a detailed description of various representation methods and find out their positive and negative aspects.

Color image processing is widely used in Intelligent Transport System, but seldom used in recognition of roads and slopes collapse. The application can reduce time and efforts. And the roads and slopes segmentation is the first and key step of the recognition system, which is a challenging and difficult problem. One of the problems is the presence of different types of roads and slopes. In this paper, we propose a novel framework for segmenting road images in a hierarchical manner that can separate the following objects: road and slopes with or without collapse, sky, road signs, cars, buildings and vegetation from the images. Then the Region of Interests (ROIs), i.e. the roads and slopes, are obtained with the geometrical, location of the objects and statistical color features which are extracted based on L*a*b color space and Gabor filter. According to combination K-means clustering with region merging, connected-component algorithm and morphological operation, the roads and slopes are segmented. The hierarchical approach does not assume the roads are present in the same type and assume the road images can be captured from arbitrary angles. The experiments show that the approach in this paper can achieve a satisfied result on various road images.

It is difficult to track, count and separate the moving bars at a high speed on production line for their overlap and accumulation. Therefore, it is necessary to establish a reliable, practical recognition and segmentation mechanism for the adhered bars. A new solution to the problem of bars adhesion is proposed: a support vector machine is constructed to recognize the adhesion type of bars by the feature vectors of training samples. The geometric feature values and moment feature values based on Blob regions in images are extracted, which is the input feature vector of support vector machine. The trained classifier is used for identifying the adhesion type of bars in images. Finally, classification and recognition is carried by support vector machine. The experimental results show that the recognition accuracy based RBF kernel achieves 100%. The method is feasible and effective for the recognition and segmentation of the adhered bars.

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM and PCM), the membership function is an interval distribution, the determined fuzzy values which are the outputs of the FCM and PCM. Secondly, the initialization of cluster center and the process of type-reduction are optimized in this algorithm, which can greatly reduce the calculation of IT2PFCM and accelerate the convergence of the algorithm. Finally, experimental results are given to show the effectives of proposed method in contrast to conventional FCM, PFCM and type 2 fuzzy c-means.

Most of the sub-pixel edge detection methods proposed in literature are based on Ghosal and Mehrotra’s method which uses Zernike moments. Some research has been reported using Fourier-Mellin moments for sub-pixel edge detection. Pseudo Zernike moments have been proved to be superior to Zernike moments in terms of their feature representation capabilities and sensitivity to image noise. This paper proposes the use of Pseudo Zernike moments for sub-pixel edge detection. Testing of the proposed algorithm shows significantly better results than Zernike and Fourier-Mellin moment based methods in case of images corrupted with additative or multiplicative noise .