Sharif Digital Repository

By watermarking an image, we hide a pattern in it in such a way that the pattern is not visible but can be extracted using a decomposition algorithm and a key in the receiver side. The watermark pattern can be a character string or any small image (pattern). One of the main applications of watermarking is its application in proving digital image ownership in widely used Internet. In this paper, we present a watermarking method for color images which is robust with respect to usual attacks such as noise addition, smoothing, compression and also rotation. The validity of correctness of retrieved watermark is based on the result of a Naive-Bayes classifier. This classier was trained on a set of... 

A significant step in color image compression is to apply a color space transformation to the original RGB image to concentrate most of its energy in one plane. Standard JPEG-based compression methods utilize predefined transformations like RGB to YCbCr. In this paper, a devised algorithm is proposed to optimize the transformation for each image. The algorithm iteratively increases the energy concentration by optimizing a novel cost function while limiting the matrix determinant to a bounded region  

Color information of natural images can be considered as a highly correlated vector space. Many different color spaces have been proposed in the literature with different motivations toward modeling and analysis of this stochastic field. Recently, color transfer among different images has been under investigation. Color transferring consists of two major categories: colorizing grayscale images and recoloring colored images. The literature contains a few color transfer methods that rely on some standard color spaces. In this paper, taking advantages of the principal component analysis (PCA), we propose a unifying framework for both mentioned problems. The experimental results show the... 

In this paper we propose a method for CBIR based on the combination of texture, edge map and color. As texture of edges yields important information about the images, we utilized an adaptive edge detector that produces a binary edge image. Also, using the statistics of color in two different color spaces provides complementary information to retrieve images. Our method is time efficient since we have applied texture calculations on the binary edge image. Our experimental results showed both the higher accuracy and lower time complexity of our method with similar related works using SIMPLIcity database  

Segmentation plays an important role in the machine vision field. Extraction of dominant segments with large number of pixels is essential for some applications such as object detection. In this paper, a new approach is proposed for color image segmentation which uses ideas behind the social science and complex networks to find dominant segments. At first, we extract the color and texture information for each pixel of input image. A network that consists of some nodes and edges is constructed based on the extracted information. The idea of community detection in social networks is used to partition a color image into disjoint segments. Community detection means partitioning vertices of a... 

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of... 

Color image segmentation is a primitive operation in many image processing and computer vision applications. Accordingly, there exist numerous segmentation approaches in the literature, which might be misleading for a researcher who is looking for a practical algorithm. While many researchers are still using the tools which belong to the old color space paradigm, there is evidence in the research established in the eighties that a proper descriptor of color vectors should act locally in the color domain. In this paper, these results are used to propose a new color image segmentation method. The proposed method searches for the principal colors, defined as the intersections of the cylindrical... 

From the birth of multi-spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray-scale images, instead of an ensemble of an n-D realization. However, it has been proved that using vector-based tools leads to a more appropriate understanding of color images and thus more efficient algorithms for processing them. Such tools are able to take into consideration the high correlation of the color components and thus to successfully carry out energy compaction. In this paper, a novel method is proposed to utilize the principal component analysis in the neighborhoods of an image in order to extract the corresponding eigenimages.... 

One of the most famous approaches for the segmentation of color images is finding clusters in the color space. Shapes of these clusters are often complex and the time complexity of the existing algorithms for finding clusters of different shapes is usually high. In this paper, a novel clustering algorithm is proposed and used for the image segmentation purpose. This algorithm distinguishes clusters of different shapes using a two-stage clustering approach in a reasonable time. In the first stage, the mean-shift clustering algorithm is used and the data points are grouped into some sub-clusters. In the second stage, connections between sub-clusters are established according to a dissimilarity... 

Transmission of video over error prone and still bandwidth limited wireless channels demand high compression efficiency and resilience to packet losses and errors. Scalable or layered video coding applied to highly compression efficient codecs is an ideal solution to the problem. However, scalability reduces compression efficiency of the coders. In this paper we show how compression efficiency of two-layer SNR scalable video coders can be retained via joint base-enhancement layer optimization. Simulation results show that joint base-enhancement layer optimization significantly outperforms separate optimization of the layers, and it closely follows the compression performance of the... 

In content based image retrieval similarity measurement is one of the most important aspects in a large image database for efficient search and retrieval to find the best answer for a user query. Color and texture are among the more expressive of the visual features. Considerable work has been done in designing efficient descriptors for these features for applications such as similarity retrieval. The MPEG-7 specifies a standard set of descriptors for color, texture and shape. In the Human Vision System (HVS), visual information is not perceived equally; some information may be more important than other information. The purpose of this paper is to show how the MPEG-7 descriptor based on... 

The ubiquity of broadband digital communications and mass storage in modern society has stimulated the widespread acceptance of digital media. However, easy access to royalty-free digital media has also resulted in a reduced perception in society of the intellectual value of digital media and has promoted unauthorised duplication practices. To detect and discourage the unauthorised duplication of media, researchers have investigated watermarking methods to embed ownership data into media. However, some authorities have expressed doubt over the efficacy of watermarking methods to protect digital media. The paper introduces a novel method to discourage unauthorised duplication of digital... 

The goal of image segmentation is to find regions that represent objects or meaningful parts of objects. In this paper a new method is presented for color image segmentation which involves the ideas used for community detection in social networks. In the proposed method an initial segmentation is applied to partition input image into small homogeneous regions. Then a weighted network is constructed from the regions, and a community detection algorithm is applied to it. The detected communities represent segments of the image. A remarkable feature of the method is the ability to segments the image automatically by optimizing the modularity value in the constructed network. The performance of... 

A novel method is proposed in the scope of image segmentation that solves this problem by breaking it into two main blocks. The first block's functionality is a method to anticipate the color basis of each segment in segmented images. One of the challenges of image segmentation is the inappropriate distribution of colors in the RGB color space. To determine the color of each segment, after mapping the input image onto the HSI color space, the image colors are classified into some clusters by exploiting the K-Means. Then, the list of cluster centers is winnowed down to a short list of colors based on a set of criteria. The second block of the proposed method defines how each pixel of the... 

Converting gray-scale images to colorful ones is one of the challenging tasks in the Computer Vision area, and various approaches based on neural network architectures have been proposed to generate colorful images. However, most of the suggested colorization frameworks use a single model for colorization regardless of the diversity of colors in images of various datasets. We claim that since such a structure is responsible for generating all types of images, it results in producing either faint-colored images or some artifacts in the images. We addressed this issue by proposing parallel colorization models, each of which is customized for generating images with similar contexts or color... 

The fish freshness was evaluated using machine vision technique through color changes of eyes and gills of farmed and wild gilthead sea bream (Sparus aurata), being employed lightness (L*), redness (a *), yellowness (b*), chroma (c *), and total color difference (ΔE) parameters during fish ice storage. A digital color imaging system, calibrated to provide accurate CIELAB color measurements, was employed to record the visual characteristics of eyes and gills. The region of interest was automatically selected using a computer program developed in MATLAB software. L*, b *, and ΔE of eyes increased with storage time, while c* decreased. The a* parameter of fish eyes did not show clear a trend... 

The first step of any face processing system is detecting the location in images where faces are present. In this paper we present an upright frontal face detection system based on the multi-resolution analysis of the face. In this method firstly, skin-color information is used to detect skin pixels in color images; then, the skin-region blocks are decomposed into frequency sub-bands using contourlet transform. Features extracted from sub-bands are used to detect face in each block. A multi-layer perceptrone (MLP) neural network was trained to do this classification. To decrease false positive detection we use eyes and lips template matching. These templates achieved by averaging... 

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin... 

In this paper, we have proposed a new face detection method which combines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in images. Due to noise and illumination changes some nonfaces might be detected too, therefore we have used a skin color model in the YCbCr color space to remove some of the detected nonfaces. Finally, we have utilized SVM to detect faces more accurately. Experimental results show that the performance of the proposed method is higher than the basic AdaBoost in the sense of detecting fewer nonfaces  

This paper proposes a new approach to watermarking multimedia products based on the combination of redundant discrete wavelet transform and independent component analysis. The original image is decomposed by RDWT, and watermark is embedded in to LL sub-band frequency according mixing model of the ICA, after that for enhancing the robustness of the watermark, the perceptual model is applied via stochastic approach for watermark adapting. This is based on computation of a noise visibility function (NVF) which has local image properties so the strength of watermarking is controllable. Principal component analysis (PCA) whitening process and FastICA techniques are introduced to ensure a blind...