Majlesi Journal of Multimedia Processing
Volume:1 Issue: 3, Sep 2012

As the popularity of digital media is growing, and world is becoming smaller, all due to the internet connectivity and WWW phenomena, the copyright protection of intellectual properties have become a necessity for prevention of illegal copying and content integrity verification. Thus newer data hiding techniques that satisfy the requirements of imperceptibility, robustness, capacity, or data hiding rate and security of the hidden data etc. Are being developed. So we go for digital image watermarking which is a method of authentication data (which is the presence of logo here) embedding in image characteristics with expectation to show resiliency against different type of unintentional or deliberate attacks. Here wavelet transform plays the role of an efficient tool due to its multi-resolution capability. Along with this wavelet transform we mix up another very strong mathematical tool called the singular value decomposition (SVD). Though till date both of them have individually been used as a tool for watermarking of digital media, very few works have utilized their skills in tandem, especially in this area. Our work here by focuses on using both of them together to provide a hybrid technique developed for protection of the intellectual property with better robustness against the popular malicious attacks. This we have seen practically by attacking the watermarked image against simulated attacks and recovering the logo from it.

Common vehicle traffic surveillance and controlling systems are based on electromagnetic spectrum. GPS, RFID and radar are some of these technologies, which are used in ITS (Intelligent Transportation System). In other hand, the humidity of the air may distort the electromagnetic waves. So, we are unable to control the traffic in foggy weather. Also there is a similar occurrence in Image Processing, because of electromagnetic identity of the light. Fog affects the Brightness and Contrast of the image. We observed worse performance in LPR (License Plate Recognition) methods in foggy weather in comprehension with normal weather.In this paper, we present an image processing technique based on histogram. In this approach, fog detects by initial histogram analysis. Next we perform histogram equalizing and contrast enhancement. We selected the parameter of edge detection which is an important feature in LPR methods.We performed this algorithm on two morphological methods. They show around 60% better result in success rates. In this paper we present 100 images of cars in fog, by simulating them with image processing softwares.

This paper presents a segmentation algorithm for detecting and counting greenhouse cucumbers in dynamic light condition in a real environment and position acquisition for a harvesting robot. This algorithm uses a combination of methods including filtering, morphology, and shape descriptor. Since there is no standard light condition in a greenhouse environment and since this condition differs for each image, first data set images are preprocessed, then appropriate filters are applied to separate color channel, third morphology operations are used to omit undesirable objects. Finally, shape descriptors are used for acquiring possible areas and data mining techniques help to detect the exact position of cucumbers. Final results show an 88.15% success rate, great improvement and high accuracy compared with the previous works.

One of the most useful compression standards is JPEG. Before sending or saving an image, it should be compacted. The image will be dispersed in destination. During the image transmission, because of the channel noise, some of its information will be lost; therefore, the quality of the reconstructed image will be decreased. There are a lot of methods to increase the quality of compacting JPEG for transmitting an image in a noisy channel. In this article, the compensation of the lost blocks will be considered, and it is assumed that the noisy blocks location is clear. In this method, the whole image will be send through one channel, and the noisy block will be reconstructed by the adjacent blocks. Also, in this article, the intelligent averaging method, in frequency domain, will be suggested and simulated to reconstruct color information. In addition, its result is compared to current averaging method. Simulation and quality measurement of different compacted images show that using intelligent averaging method in reconstruct images is better than using current method.

Applying image processing techniques to identify rice kernels based on their varieties is an objective method which can increase the performance of this process in real applications. In this paper, rice varieties were identified using an ensemble classifier. Images from five different classes of rice varieties were acquired using a flatbed scanner. After segmentation process, 41 morphological features were extracted. To classify rice kernels, an ensemble classifier were investigated. The overall classification F-measure was achieved as 99.86. Result of this research, can be used for developing an efficient rice kernel sorting system.

Nowadays one of the most common devices for computer data entry is the keyboard. Optimization of keyboard arrangement is of great importance, since it can help us to have access to information in less time. A combined evolutionary algorithm can search on the keyboard and reach the optimized arrangement with regard to an evaluation factor (the level of typing comfort for a special letter arrangement) in the space of Persian letters arrangement on a keyboard. In this paper, the genetic and simulated annealing algorithms are searching for the best permutation among the 33 Persian letters on the keyboard. The evaluation criteria include three factors: intermittent use of hands in typing the texts, not using a hand for typing two adjacent letters and the level of hardness of typing a letter in the related arrangement. In the studies conducted by the large and various data sets (Persian texts), it was determined that the optimized arrangement resulted from this hybrid algorithm performs better than the present algorithm.

Early detection of breast cancer can significantly prevent the mortality from this disease. This article, in contrast to similar methods, which suffer from such flaws as accuracy and low speed and loss of visual data in detection of cancerous tumors, proposes an accurate and intelligent system for segmentation and precise positioning of tumors, even the ones with extremely low size. It instantly detect tumor from the other parts of breast. In the first step, after designing a filter, the input image is enhanced to reveal the detail of breast tissues. Then, the image is segmented using topography contour method and finally the cancerous tumors are separated from other parts of mammographic image by utilizing image processing techniques such as edge detection, thresholding and image integration. To evaluate the performance of the system, 240 mammographic images were taken from mini-MIAS and DDSM database. It detected tumors at an acceptable level with an average 94.58% accuracy and 96.24% sensitivity. The exact positioning of cancerous tumors enables the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth.