The paper presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q 2003 Elsevier B.V. All rights reserved.

Many different low-level feature detectors exist and it is widely agreed that the evaluation of detectors is important. In this paper we introduce two evaluation criteria for interest points: repeatability rate and information content. Repeatability rate evaluates the geometric stability under different transformations. Information content measures the distinctiveness of features. Different interest point detectors are compared using these two criteria. We determine which detector gives the best results and show that it satisfies the criteria well.

Abstract. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms and, second, to propose a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image. The mathematical analysis is based on the analysis of the “method noise, ” defined as the difference between a digital image and its denoised version. The NL-means algorithm is proven to be asymptotically optimal under a generic statistical image model. The denoising performance of all considered methods are compared in four ways; mathematical: asymptotic order of magnitude of the method noise under regularity assumptions; perceptual-mathematical: the algorithms artifacts and their explanation as a violation of the image model; quantitative experimental: by tables of L 2 distances of the denoised version to the original image. The most powerful evaluation method seems, however, to be the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.

The increasing performance and decreasing cost of processors and memory are causing system intelligence to move into peripherals from the CPU. Storage system designers are using this trend toward "excess" compute power to perform more complex processing and optimizations inside storage devices. To date, such optimizations have been at relatively low levels of the storage protocol. At the same time, trends in storage density, mechanics, and electronics are eliminating the bottleneck in moving data off the media and putting pressure on interconnects and host processors to move data more efficiently. We propose a system called Active Disks that takes advantage of processing power on individual disk drives to run application-level code. Moving portions of an application's processing to execute directly at disk drives can dramatically reduce data traffic and take advantage of the storage parallelism already present in large systems today. We discuss several types of appl...

We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the non local means (NL-means), based on a non local averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters. 1.

The principal trend in the design of computer systems is the expectation of much greater computational power in future generations of microprocessors. This trend applies to embedded systems as well as host processors. As a result, devices such as storage controllers have excess capacity and growing computational capabilities. Storage system designers are exploiting this trend with higher-level interfaces to storage and increased intelligence inside storage devices. One development in this direction is Network-Attached Secure Disks (NASD) which attaches storage devices directly to the network and raises the storage interface above the simple (fixed-size block) memory abstraction of SCSI. This allows devices more freedom to provide efficient operations; promises more scalable subsystems by offloading file system and storage management functionality from dedicated servers; and reduces latency by executing common case requests directly at storage devices. In this paper, we push this increa...

Abstract—A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods. I.

Abstract—We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks. Index Terms—Stereo, 3D/stereo scene analysis.

We propose a biologically motivated computational step, called nonclassical receptive field (non-CRF) inhibition, more generally surround inhibition or suppression, to improve contour detection in machine vision. Non-CRF inhibition is exhibited by 80 % of the orientation-selective neurons in the primary visual cortex of monkeys and has been demonstrated to influence the visual perception of man as well. The essence of this mechanism is that the response of an edge detector in a certain point is suppressed by the responses of the operator in the region outside the area of operator support. We combine classical edge detection with two types of inhibitory mechanism, isotropic and anisotropic inhibition, both of which have counterparts in biology. For edge detection, we also use a biologically motivated method (the Gabor energy operator). The resulting operator responds strongly to isolated lines, edges, and contours, but exhibits a weaker or no response to edges that make part of texture. We use natural images with associated ground truth contour maps to assess the performance of the proposed operator regarding the detection of contours while suppressing texture edges. The results show that our method enhances contour detection in cluttered visual scenes more effectively than classical edge detectors used in machine vision (Canny edge detector). Therefore, the proposed operator is more useful for contour-based object recognition tasks, such as shape comparison, than traditional edge detectors, which do not distinguish between contour and texture edges. Traditional edge detection algorithms can, however, also be extended with surround suppression. Next to the advancement of contour detection in machine vision, this study contributes to the understanding of inhibitory mechanisms in biology.