Toward Real Time Fractal Image Compression Using Graphics Hardware

Erra, Ugo

doi:10.1007/11595755_92

Cited by 19 publications

(11 citation statements)

References 4 publications

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“…This is due to the fact that, in the case of dividing the image into sub-images and then compressing them, the list of the domain blocks on which we perform the search process is smaller than in the classical case. We can obtain a further speed improvement in the compression using graphics hardware (Erra, 2005).…”

Section: Discussionmentioning

confidence: 99%

Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Gdawiec

Domańska

2011

International Journal of Applied Mathematics and Computer Science

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One of the approaches in pattern recognition is the use of fractal geometry. The property of self-similarity of fractals has been used as a feature in several pattern recognition methods. All fractal recognition methods use global analysis of the shape. In this paper we present some drawbacks of these methods and propose fractal local analysis using partitioned iterated function systems with division. Moreover, we introduce a new fractal recognition method based on a dependence graph obtained from the partitioned iterated function system. The proposed method uses local analysis of the shape, which improves the recognition rate. The effectiveness of our method is shown on two test databases. The first one was created by the authors and the second one is the MPEG7 CE-Shape-1PartB database. The obtained results show that the proposed methodology has led to a significant improvement in the recognition rate.

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Section: Discussionmentioning

confidence: 99%

Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Gdawiec

Domańska

2011

International Journal of Applied Mathematics and Computer Science

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“…To counter this side effect, parallel implementations that exploit SIMD architecture on current commodity graphics hardware can achieve real-time performances [7].…”

Section: State Of the Artmentioning

confidence: 99%

Volume Content Indexing Using a Fractal Coding Algorithm

Popescu

Tudorache

Tanasie

2010

2010 Second International Conferences on Advances in Multimedia

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Current technology has made possible to scan volumes with ease and thus huge amount of data is generated. Devising algorithms to efficiently store and index this data is an open challenge. In this article we propose a theoretical approach to volume indexing by extending the idea of using fractal compression to index images, to the third dimension. The described technique uses a classification method that greatly improve the exponential complexity of the volume fractal coding. The compressed volume can then be used to be indexed in a digital library in a more efficient way. Then, content-based similarity queries can take full advantage of the self-similarity property of fractals. All these features will be present in an application that is currently under development.

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“…By using a GPU they achieved factor of 3.5 (or 3.5×) speedup over the CPU implementation. In [28], Erra implemented a fractal image compression on an NVidia GeForces FX 6800 GPU and achieved a 280× speedup over the CPU implementation. Many other applications and algorithms implemented on the GPU, such as geometric computations, collision detection, and particle tracking, are discussed in [29].…”

Section: Computational Loadmentioning

confidence: 99%

Human and computer recognition of regional accents and ethnic groups from British English speech

Hanani

Russell

Carey

2013

Computer Speech & Language

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Language Recognition Evaluation (LRE) tasks. This system is applied to the problems of regional accent recognition for British English, and ethnic group recognition within a particular accent. We compare the results with human performance and, for accent recognition, the 'text dependent' ACCDIST accent recognition measure. For the fourteen regional accents of British English in the ABI-1 corpus (good quality read speech), our language ID system achieves a recognition accuracy of 86.4%, compared with 95.18% for our best ACCDIST-based system and 58.24% for human listeners. The "Voices across Birmingham" corpus contains significant amounts of telephone conversational speech for the two largest ethnic groups in the city of Birmingham (UK), namely the 'Asian' and 'White' communities. Our language ID system distinguishes between these two groups with an accuracy of 94.3% compared with 90.24% for human listeners. Although direct comparison is difficult, it seems that our language ID system performs much better on the standard twelve class NIST 2003 Language Recognition Evaluation task or the two class ethnic group recognition task than on the fourteen class regional accent recognition task. We conclude that automatic accent recognition is a challenging task for speech technology, and that the use of natural conversational speech may be advantageous for these types of paralinguistic task.One issue with conventional approaches to language ID that use high-order Gaussian Mixture Models (GMMs) and high-dimensional feature vectors is the amount of computing power that they require. Currently, multi-core Graphics Processing Units (GPUs) provide a possible solution at very little cost. In this thesis we also explore the application of GPUs to speech signal and pattern processing, using language ID as a vehicle to demonstrate their benefits. Realisation of the full potential of GPUs requires both effective coding of predetermined algorithms, and, in cases where there is a choice, selection of the algorithm or technique for a specific function that is most able to exploit the properties of the GPU. We demonstrate these principles using the NIST LRE 2003 task, which involves processing over 600 hours of speech. We focus on two parts of the system, namely the acoustic classifier, which is based on a 2048 component GMM, and the acoustic feature extraction process. In the case of the latter we compare a conventional FFT-based analysis with an FIR filter bank, both in terms of their ability to exploit the GPU architecture and language ID performance. With no increase in error rate our GPU based system, with an FIR-based front-end, completes the full NIST LRE 2003 task in 16 hours, compared with 180 hours for the more conventional FFT-based system on a standard CPU (a speed up factor of more than 11).

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Toward Real Time Fractal Image Compression Using Graphics Hardware

Cited by 19 publications

References 4 publications

Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Volume Content Indexing Using a Fractal Coding Algorithm

Human and computer recognition of regional accents and ethnic groups from British English speech

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