Thionucleotides in the transfer ribonucleic acid of Image Image

tRNA isolated from Image Image , grown in the presence of radioactive sulfur was analyzed for the occurrence of thionucleotides. The analysis revealed the presence of at least five thionucleotides, of which three were identified as 4-thiouridylic acid, 5-methylaminomethyl-2-thiouridylic acid and 2-thiocytidylic acid. Iodine-oxidation affected the acceptor ability of several amino acid specific tRNAs, those for lysine and serine being affected most. The tRNA of Image Image differs from that of Image . Image both in the number and the relative proportion of thionucleotides.

From cages to wedges and clefts : Design of some novel hosts based on Image , Image , Image -triquinane framework

Annulation of aromatic rings on the folded Image ,Image ,Image -triquinane backbone has led to the design of potential host systems Image and Image whose crystal structures have been determined.

Image Heritage - The Temporal Dimension in Consumers' Corporate Image Constructions

Images and brands have been topics of great interest in both academia and practice for a long time. The company’s image, which in this study is considered equivalent to the actual corporate brand, has become a strategic issue and one of the company’s most valuable assets. In contrast to mainstream corporate branding research focusing on consumerimages as steered and managed by the company, in the present study a genuine consumer-focus is taken. The question is asked: how do consumers perceive the company, and especially, how are their experiences of the company over time reflected in the corporate image? The findings indicate that consumers’ corporate images can be seen as being constructed through dynamic relational processes based on a multifaceted network of earlier images from multiple sources over time. The essential finding is that corporate images have a heritage. In the thesis, the concept of image heritage is introduced, which stands for the consumer’s earlier company-related experiences from multiple sources over time. In other words, consumers construct their images of the company based on earlier recalled images, perhaps dating back many years in time. Therefore, corporate images have roots - an image heritage – on which the images are constructed in the present. For companies, image heritage is a key for understanding consumers, and thereby also a key for consumer-focused branding strategies and activities. As image heritage is the consumer’s interpretation base and context for image constructions here and now, branding strategies and activities that meet this consumer-reality has a potential to become more effective. This thesis is positioned in the tradition of The Nordic School of Marketing Thought and introduces a relational dynamic perspective into branding through consumers’ image heritage. Anne Rindell is associated to CERS, the Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration.

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

Image reconstruction by inhomogeneous diffusion for positron emission tomography

In positron emission tomography (PET), image reconstruction is a demanding problem. Since, PET image reconstruction is an ill-posed inverse problem, new methodologies need to be developed. Although previous studies show that incorporation of spatial and median priors improves the image quality, the image artifacts such as over-smoothing and streaking are evident in the reconstructed image. In this work, we use a simple, yet powerful technique to tackle the PET image reconstruction problem. Proposed technique is based on the integration of Bayesian approach with that of finite impulse response (FIR) filter. A FIR filter is designed whose coefficients are determined based on the surface diffusion model. The resulting reconstructed image is iteratively filtered and fed back to obtain the new estimate. Experiments are performed on a simulated PET system. The results show that the proposed approach is better than recently proposed MRP algorithm in terms of image quality and normalized mean square error.

On resampling detection and its application to detect image tampering

Usually digital image forgeries are created by copy-pasting a portion of an image onto some other image. While doing so, it is often necessary to resize the pasted portion of the image to suit the sampling grid of the host image. The resampling operation changes certain characteristics of the pasted portion, which when detected serves as a clue of tampering. In this paper, we present deterministic techniques to detect resampling, and localize the portion of the image that has been tampered with. Two of the techniques are in pixel domain and two others in frequency domain. We study the efficacy of our techniques against JPEG compression and subsequent resampling of the entire tampered image.

GAP-RBF based NR image quality measurement for JPEG coded images

In this paper, we present a growing and pruning radial basis function based no-reference (NR) image quality model for JPEG-coded images. The quality of the images are estimated without referring to their original images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the problem of quality estimation is transformed to a function approximation problem and solved using GAP-RBF network. GAP-RBF network uses sequential learning algorithm to approximate the functional relationship. The computational complexity and memory requirement are less in GAP-RBF algorithm compared to other batch learning algorithms. Also, the GAP-RBF algorithm finds a compact image quality model and does not require retraining when the new image samples are presented. Experimental results prove that the GAP-RBF image quality model does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

Real-time image denoising algorithm in teleradiology systems - art.no.606304

Denoising of medical images in wavelet domain has potential application in transmission technologies such as teleradiology. This technique becomes all the more attractive when we consider the progressive transmission in a teleradiology system. The transmitted images are corrupted mainly due to noisy channels. In this paper, we present a new real time image denoising scheme based on limited restoration of bit-planes of wavelet coefficients. The proposed scheme exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each sub-band. The desired bit-rate control is achieved by applying the restoration on a limited number of bit-planes subject to the optimal smoothing. The proposed method adapts itself to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with unrestored case, in context of error reduction. It also has capability to adapt to situations where noise level in the image varies and with the changing requirements of medical-experts. The applicability of the proposed approach has implications in restoration of medical images in teleradiology systems. The proposed scheme is computationally efficient.

A new FIR filter for image restoration

Image filtering techniques have potential applications in biomedical image processing such as image restoration and image enhancement. The potential of traditional filters largely depends on the apriori knowledge about the type of noise corrupting the image. This makes the standard filters to be application specific. For example, the well-known median filter and its variants can remove the salt-and-pepper (or impulse) noise at low noise levels. Each of these methods has its own advantages and disadvantages. In this paper, we have introduced a new finite impulse response (FIR) filter for image restoration where, the filter undergoes a learning procedure. The filter coefficients are adaptively updated based on correlated Hebbian learning. This algorithm exploits the inter pixel correlation in the form of Hebbian learning and hence performs optimal smoothening of the noisy images. The application of the proposed filter on images corrupted with Gaussian noise, results in restorations which are better in quality compared to those restored by average and Wiener filters. The restored image is found to be visually appealing and artifact-free

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

Time Dimension in Consumers’ Image Construction Processes: Introducing Image Heritage and Image-in-use

This paper focuses on the time dimension in consumers’ image construction processes. Two new concepts are introduced to cover past consumer experiences about the company – image heritage, and the present image construction process - image-in-use. Image heritage and image-in-use captures the dynamic, relational, social, and contextual features of corporate image construction processes. Qualitative data from a retailing context were collected and analysed following a grounded theory approach. The study demonstrates that consumers’ corporate images have long roots in past experiences. Understanding consumers’ image heritage provides opportunities for understanding how consumers might interpret management initiatives and branding activities in the present.

Value of Image in Service

Purpose This paper takes a customer view on corporate image and value, and discusses the value of image in service. We propose a model depicting how the customer’s corporate brand image affects the customer’s value-in-use. Methodology/approach The paper represents conceptual development on customers’ value and image construction processes. By integrating ideas and elements from the current service and branding literature a model is proposed that extends current views on how value-in-use emerges. Findings From a current service perspective it is the customer who makes value assessments when experiencing service. Similarly, if branding is a concept used to denote the service provider’s intentions and attempts to create a corporate brand, image construction is the corresponding process where the customer constructs the corporate image. This image construction process is always present both in service interactions and in communication and has an effect on the customer’s value-in-use. We argue that two interrelated concepts are needed to capture corporate image construction and dynamics and value-in-use – the image-in-use and image heritage. Research implications The model integrates two different streams of research pointing to the need to consider traditional marketing communication and service interactions as inherently related to each other from the customer’s point of view. Additionally the model gives a platform for understanding how value-in-use emerges over time. New methodological approaches and techniques to capture image-in-use and image heritage and their interplay with value-in-use are needed. Practical implications The company may not be able to control the emergence of value-in-use but may influence it, not only in interactions with the customer but also with pure communication. Branding activities should therefore be considered related to service operations and service development. Additionally, practitioners would need to apply qualitative methods to understand the customer’s view on image and value-in-use. Originality/value The paper presents a novel approach for understanding and studying that the customer’s image of a company influences emergence of value-in-use. The model implies that the customer’s corporate image has a crucial role for experienced value-in-use.