1,721,384 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Extracting camera-based fingerprints for video forensics
No full textVideo source attribution is an important operation in forensics applications. Identifying which specific device or camera model took a video can help in authorship verification, but can be also a precious source of information for detecting a possible manipulation. The key observation is that any physical device leaves peculiar traces in the acquired content, a sort of fingerprint that can be exploited to establish data provenance. Moreover, absence or modification of such traces may reveal a possible manipulation. In this paper, inspired by recent work on images, we train a neural network that enhances the model-related traces hidden in a video, extracting a sort of camera fingerprint, called video noiseprint. The net is trained on pristine videos with a Siamese strategy, minimizing distances between same-model patches, and maximizing distances between unrelated patches. Experiments show that methods based on video noiseprints perform well in major forensic tasks, such as camera model identification and video forgery localization, with no need of prior knowledge on the specific manipulation or any form of fine-tuning
A comparison of flat and object-based transform coding techniques for the compression of multispectral images
No full textIn this work we implement and compare several state-of-the-art transform coding schemes for the compression of multispectral images, in order to better understand which elements have a deeper impact on the overall performance, and which tools guarantee the best results. All schemes are based on Karhunen-Löeve transform and/or Wavelet Transform, in various combinations, and use SPIHT as the coding engine. Moreover, besides the ordinary techniques, their object-based counterparts are also examined, so as to study the viability of such approach [1] for these images. Whenever possible, an optimal rate allocation strategy is applied. The experiments, performed on images acquired by two different sensors, highlight the superiority of KLT as spectral transform; the rough equivalence between object-based and ordinary techniques in terms of rate-distortion performance; and the importance of the optimal allocation. © 2005 IEEE
Riconoscimento analitico di sostanze responsabili di intossicazione acuta: studio statistico su 106 casi.
No full textRegion-based transform coding of multispectral images
No full textWe propose a new efficient region-based scheme for the compression of multispectral remote-sensing images. The region-based description of an image comprises a segmentation map, which singles out the relevant regions and provides their main features, followed by the detailed (possibly lossless) description of each region. The map conveys information on the image structure and could even be the only item of interest for the user; moreover, it enables the user to perform a selective download of the regions of interest, or can be used for high-level data mining and retrieval applications. This approach, with the multiple pieces of information required, may seem inherently inefficient. The goal of this research is to show that, by carefully selecting the appropriate segmentation and coding tools, region-based compression of multispectral images can be also effective in a rate-distortion sense, thus providing an image description that is both insightful and efficient. To this end, we define a generic coding scheme, based on Bayesian image segmentation and on transform coding, where several key design choices, however, are left open for optimization, from the type of transform, to the rate allocation procedure, and so on. Then, through an extensive experimental phase on real-world multispectral images, we gain insight on such key choices, and finally single out an efficient and robust coding scheme, with Bayesian segmentation, class-adaptive Karhunen-Loève spectral transform, and shape-adaptive wavelet spatial transform, which outperforms state-of-the-art and carefully tuned conventional techniques, such as JPEG-2000 multicomponent or SPIHT-based coders. © 2007 IEEE
Costs and advantages of shape-adaptive wavelet transform for region-based image coding
No full textRegion-based encoding techniques have been long investigated for the compression of still images and video sequences and have recently gained much popularity, as testified by the object-based nature of the MPEG-4 video coding standard. This work aims at analyzing costs and advantages of implementing such an approach by shape-adaptive wavelet transform and shape-adaptive SP1HT. The analysis of several performance measures in a number of experiments confirm the potential of wavelet-based region-based approach, and provide insight about what performance gains and losses can be expected in various operative conditions. © 2005 IEEE
Low-complexity scalable video coding through table lookup VQ and index coding
No full textThe Internet community is very heterogeneous in terms of access bandwidth and terminal capabilities, hence, there is much interest for low-computation, software-only, scalable video coders that guarantee universal access to video communication. Scalability allows users to achieve a fair quality of service in relation to their resources. Low complexity, on the other hand, is necessary in order to ensure that also users with low computing power can be served. In this work, we propose a multiplication-free video codec, whose complexity is much reduced with respect to standard coders at the price of a limited increase in memory requirements. To this end we resort to very simple coding tools such as table lookup vector quantization (VQ) and conditional replenishment.We start from the simple coder proposed in [1], which already guarantees high scalability and limited computational burden, and improve upon it by further reducing complexity, as well as the encoding rate, with no effect on the encoding quality. The main innovation is the use of ordered VQ codebooks, which allows the encoder to generate correlated indexes, unlike in conventional VQ. Index correlation, in turn, allows us to carry out conditional replenishment (the most time-consuming operation in the original coder) by working on indexes rather than on block of pixels, and to reduce drastically its complexity. In addition, we also take advantage of the correlation among indexes to compress them by means of a predictive scheme, which leads to a 15-20% rate reduction in the base layer, without significant increase in complexity. Thanks to these and other minor optimizations we have obtained improved performance and, more important, a 60-70% reduction of the encoding time (on a general purpose machine) with respect to [1]
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