EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors, and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so-called EndNet, that is based on a two-staged autoencoder network. This well-known structure is completely enhanced and restructured by introducing additional layers and a projection metric [i.e., spectral angle distance (SAD) instead of inner product] to achieve an optimum solution. Moreover, we present a novel loss function that is composed of a Kullback-Leibler divergence term with SAD similarity and additional penalty terms to improve the sparsity of the estimates. These modifications enable us to set the common properties of endmembers, such as nonlinearity and sparsity for autoencoder networks. Finally, due to the stochastic-gradient-based approach, the method is scalable for large-scale data and it can be accelerated on graphical processing units. To demonstrate the superiority of our proposed method, we conduct extensive experiments on several well-known data sets. The results confirm that the proposed method considerably improves the performance compared to the state-of-the-art techniques in the literature

Intelligent distributed cognitive-based open learning system for schools (ICLASS)

The iClass project will develop an intelligent cognitive-based open learning system and environment, adapted to individual learners' needs and ensure their take-up in the education sector at a European level. This will be achieved by creating:- an advanced learning system, founded on an ontology-based architecture for sequencing of knowledge and adaptive to learner's level of understanding and learning style by dynamically creating individualized learning objects;- a distributed, collaborative environment with ubiquitous access for all stakeholders (parents, teachers, students, Ministries, publishers) to rich multimedia content and services, empowering direct communication. iClass will have an open architecture and be fully compatible with legacy learning systems and tools. iClass will advance European research by investigating and validating the use of ontological maps for the dynamic creation of learning objects and their transferability between the various European curricula. This advance in research will be disseminated and transferred to the wider RandD community. To reach its objectives, iClass unites 22 partners from 11 different countries made up of: leading European research partners in cognitive science, pedagogy and artificial intelligence; 4 of the world's leading IT companies and 3 SMEs with state of the art learning and new media technologies; 2 multinational school networks who will pilot and evaluate the iClass results during the project. iClass will improve the quality and efficiency of learning, an important intangible asset, through the development of e-learning solutions and standards, taking into account not only advanced technology but also educational, psychological and cognitive aspects to ensure full integration of current research and best practices. The iClass project will provide Europe with the future framework and infrastructure for exploiting and delivering national curricula and educational resources in an advanced learning environment.IP - Integrated Projec

Architectures for multi-threaded MVC-compliant multi-view video decoding and benchmark tests

3D video based on stereo/multi-view representations is becoming widely popular. Real-time encoding/decoding of such video is an important concern as the number and spatial/temporal resolution of views increase. We present a systematic method for design and optimization of multi-threaded multi-view video encoding/decoding algorithms using multi-core processors and provide benchmark results for real-time decoding. The proposed multi-core decoding architectures are compliant with the current MVC extension of H.264/AVC international standard, and enable multi-threaded processing with negligible loss of encoding efficiency and minimum processing overhead. Benchmark results show that multi-core processors and multi-threading decoding are necessary for real-time high-definition multi-view video decoding and display

Görüntü ve video veritabanlarına etkili erişim sistemi

TÜBİTAK EEEAG Proje01.12.2003Bilgisayar yazılım ve donanımındaki son gelişmeler elektronik bilginin daha kolay bir şekilde üretilmesi, işlenmesi ve saklanmasını sağlamıştır. Elektronik bilgi başta sadece yazılı metinden ibaretken, giderek artan bir oranda grafik, imge, animasyon, video, ses ve diğer çoğul ortam verileri de bu kapsama dahil olmaktadır. Bu verilerin çoğuna , hızlı ağlar, arama makineleri ve gözatma araçları vasıtasıyla WEB üzerinden erişilebilmektedir. Fakat verilere ulaşma amaçlı yapılan sorguların çoğu istenilen verilerden daha çok ilgisiz verileri listelemektedir. Çoğul ortam verilerinde durum daha kötüdür. Geleneksel çoğul ortam erişim yöntemleri arama yapan kişinin verdiği anahtar sözcüklere dayanması nedeniyle, verimlilikten uzaktır. Bu sebebten dolayı sayısal görüntülerin içerik tabanlı erişimi veritabanı yönetiminde aktif bir araştırma konusudur. Büyük çoğulortam veritabanlarında veri arama konusundaki diğer önemli bir nokta da kullanıcı arayüzünün kolay kullanılabilir ve yüksek etkileşimli olmasıdır. Bunun yanısıra sistemin geniş ulaşılabilirlik amacıyla çok platformlu olması da gerekir. Literatürde bilinen içerik tabanlı indeksleme ve erişim araçlarının çoğu JAVA APPLET ve CGI-BIN temellidir. Fakat bu tekniklerin, yavaş çalışması ve/veya sınırlı sayıda kullanıcıya ulaşabilmesi gibi dezavantajları vardır. Bu sorunları çözmek için son yıllarda önerilen iki teknoloji ASP (Active Server Pages) ve JAVA Servlet'dir. Bu tekniklerin kullanıcı makineye sadece basit HTML kodlanmış bir sayfa göndermesi ve erişim sürecinin sonuçlarının herhangi bir gözatma aracı kullanılarak izlenebilmesini olanaklı kılması düşünülmektedir. Bu nedenlerden dolayı bu projede JAVA Servlet ve ASP'ye dayanan içerik tabanlı bir veritabanı erişim sistemi geliştirilmiş, performans değerlendirmeleri yapılmıştır

COST 276 ''Tümleşik çoklu ortam iletişimi için bilgi idaresi'' (information and knowledge management for integrated media communication systems)

TÜBİTAK EEEAG AY Proje01.08.200

Optimal packet scheduling and rate control for video streaming

In this paper, we propose a new low-complexity retransmission based optimal video streaming and rate adaptation algorithm. The proposed OSRC (Optimal packet Scheduling and Rate Control) algorithm provides average reward optimal solution to the joint scheduling and rate control problem. The efficacy of the OSRC algorithm is demonstrated against optimal FEC based schemes and results are verified over TFRC (TCP Friendly Rate Control) transport with ns-2 simulations

CONTEXT BASED SUPER RESOLUTION IMAGE RECONSTRUCTION

In this paper a context based super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator identifies local gradients and textures for selecting the optimal SR method for the region of interest. Texture segmentation and gradient map estimation are done prior to the reconstruction stage. Gradient direction is used for optimal noise reduction along the edges for non-textured regions. On the other hand, regularization term is cancelled for textured regions so that the resultant method reduces to maximum likelihood (ML) solution. It is demonstrated on Brodatz Texture Database that ML solution gives the best PSNR values on textures compared to the regularized SR methods in the literature. Experimental results show that the proposed hybrid method has superior performance in terms of Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index Measure (SSIM) compared the SR methods in the literature

Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search

The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware. This paper introduces a fast and robust method that ensures both of these goals by obtaining state-of-the-art performance for an image-to-video search scenario. Hence, we present critical enhancements to well-known indexing and visual representation techniques by promoting faster, better and moderate retrieval performance. We also boost the superiority of our method for some visual challenges by exploiting individual decisions of local and global descriptors at query time. For instance, local content descriptors represent copied/duplicated scenes with large geometric deformations such as scale, orientation and affine transformation. In contrast, the use of global content descriptors is more practical for near-duplicate and semantic searches. Experiments are conducted on a large-scale Stanford I2V dataset. The experimental results show that our method is useful in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. The proposed method is superior and achieves state-of-the-art performance based on the mean average precision (MAP) score of this dataset. Lastly, we report additional MAP scores after updating the ground annotations unveiled by retrieval results of the proposed method, and it shows that the actual performance

Texture and edge preserving multiframe super-resolution

Super-resolution (SR) image reconstruction refers to methods where a higher resolution image is reconstructed using a set of overlapping aliased low-resolution observations of the same scene. Although edge preservation has been a widely explored topic in SR literature, texture-specific regularisation has recently gained interest. In this study, texture-specific regularisation is handled as a post-processing step. A two stage method is proposed, comprising multiple SR reconstructions with different regularisation parameters followed by a restoration step for preserving edges and textures. In the first stage, two maximum-aposteriori estimators with two different amounts of regularisation are employed. In the second stage, pixel-to-pixel difference between these two estimates is post-processed to restore edges and textures. Frequency selective characteristics of discrete cosine transform and Gabor filters are utilised in the post-processing step. Experiments on synthetically generated images and real experiments demonstrate that the proposed methods give better results compared with the state-of-the-art SR methods especially on textures and edges