International Journal of Image and Graphics

Compression of logo as an image is addressed in logo watermarking, in which a framework of joint Human Vision System (HVS) model and rate allocation theory in the wavelet domain is applied. Under this framework, a novel logo watermarking algorithm using reversible discrete wavelet transform is proposed. Based on multi-level wavelet decomposition of both host and logo images, a well-known HVS model is applied to locate the visually insensitive area in the host for embedding, while the rate allocation theory determines how the logo is compressed and embedded by using the statistical characteristics of the logo. For a given overall embedding distortion, quantization step-sizes of different logo subbands are analytically determined to maximize the fidelity of the extracted logo under the imperceptibility constraint. The adaptive system is thus applicable to different hosts and logos without tuning the parameters manually. It is proved to be robust against various types of attacks and is quite suitable for hardware implementations. Since logo is considered as an image in this new algorithm, the watermarking approach developed can be extended in general to image-in-image embedding.

In this paper we consider the problem of effective browsing of collections of images. In particular, we address those situations where it is necessary to find a limited number of images satisfying certain criteria, as well as the very common case in which the user, not having a clear idea about what to search, needs to explore the entire picture database to identify what he or she simply likes. The approaches we propose try to find good trade-offs between browsing time and quality of the exploration. The article has a twofold purpose: on the one hand, it presents a selection of the new image display methods we have developed in the last years, which differentiate from those considered to date by the Information Visualization community; on the other hand, it illustrates the results obtained through comparative tests performed on a subset of the developed display modes, with the main aim to prove the feasibility of their use as substitutes for the usual "grid" arrangement.

Digital inpainting provides a means for reconstruction of damaged portions of an image. Although the inpainting basics are straightforward, most inpainting techniques published in the literature are only suitable for remarkable small portion or smooth color image. In order to avoid such shortcomings, we present a new algorithm for digital reconstruction based on combination of wavelet decomposition, surface-based/PDE-based inpainting and texture synthesis. In this algorithm, wavelet transform firstly decomposes the image into high frequency and low frequency level parts. Subsequently, CSRBF which is generally used for surface interpolation or PDE-based inpainting is employed for low frequency level and texture synthesis is used for high frequency level. It results in that not only slight portion but also the common blotched image can be reconstructed with high quality. Especially, our algorithm makes large-size blotched image possible and becomes more efficient as compared to individual PDE-based and CSRBF approaches.

In this paper a novel color image segmentation algorithm based on homogeneity histogram is proposed. The proposed approach uses intermediate features of maximum overlap wavelet transform (IMOWT). The IMOWT, which is the efficient transform, has been applied to color image segmentation for its time effectiveness, flexibility and translation invariance which are required for good segmentation results. The set of transform coefficients derived from wavelet domain are subjected to an efficient peak finding algorithm (PFA). PFA is employed to identify the most significant peaks of the homogeneity histogram. While we process the homogeneity histogram, both local and global information are taken into account. This is particularly helpful in taking care of small objects and local variations of the image. This method provides better segmentation results when compared to the direct application of PFA and Mean shift algorithm.

Texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. The development in multi-resolution analysis such as Gabor and wavelet transform help to overcome this difficulty. This paper describes the texture characterization system using (i) Wavelet Statistical Features (WSF) obtained from 3-level Discrete Wavelet Transformed (DWT) images, (ii) Wavelet Co-occurrence Features (WCF) of one level wavelet transformed images in detail.

In the last decade, many function-approximation methods for illustration images have been proposed. However, these methods cannot process more complex full color images effectively, even when some simple regularity of color transitions such as gradations appear in images. In this paper, we propose a rigorous function-approximation method that is applicable for gradation images. This method performs image segmentation that recognizes a gradation pattern as one region. This segmentation is performed by recursively approximating the tones of colors using multiple regression analysis of 2-variable functions. We compare the proposed method and the conventional methods with regard to the quality of enlarged images and the efficiency of image description and reproduction. The experimental results show that our approach is higher in quality, smaller in data size, and faster in computational costs than the conventional methods.

A novel optical flow estimation is proposed in this paper, which addresses some issues including the credible estimation of optical flow and the prevention of over-smoothing across motion boundaries. Our main contribution is that we estimated the optical flow by a nonlinear filtering process instead of an energy minimizing process, the latter often needs the corresponding smoothing constraint to be restricted to some form of convex and differentiable entity. In this way, it avoids some restrictions needed by the regularization. So we can choose a nonlinear filter from more flexible forms, which can help to deal with flow discontinuities more efficiently in some sense. We modified and extended a scalar 2D bilateral filter to optical flow field as the wanted nonlinear filter. Qualitative and quantitative results show that the new method can produce a reliable result.

Advances in electronic technology have made integrated circuits (ICs) the fundamental components in all electronic devices. To increase their production yield by catching defects as early as possible, we need to perform quality assurance on the semiconductor dies during the assembly and packaging processes. A common approach is to employ machine vision to compare a test die with a "known good die". However, difficulties in ensuring identical imaging conditions (such as illumination) are limitations to this die-to-die comparison approach. Instead, in this work we develop a novel reference-free defect detection algorithm for an IC die by analyzing its image. By identifying intrinsic and extrinsic features of various segments in the image, we implement a classification scheme to identify whether the die is defective or not. We rely on the fact that normal ICs contain regular patterns, and the abnormal and irregular regions are classified as potential areas of defects. Experimental results show that the proposed reference-free defect detection algorithm can detect most of the defects from different types of IC dies, and can also correctly classify normal IC dies as non-defective. These results demonstrate the feasibility of the reference-free defect detection approach.