Geliştirilmiş bulanık ARTMAP ile radar darbelerinin uyarlanabilir sınıflandırılma

Radar kimliklendirme, elektronik istihbarat sistemlerinin esas parçalarından bir tanesidir. Bu zamana kadarki çalışmalarda eğitim sürecinde öğretildiği radar türlerini tanıyabilmesinin yanısıra test aşamasında öğrenmeye devam edip yeni bir radar türüyle karşılaştığında ise yeni bir sınıf açma kabiliyetine sahip olması sebebiyle bulanık ARTMAP tercih edilen yöntemlerden birisi olmuştur. Bu çalışmada radar Darbe Tanımlayıcı Kelimelerin (DTK) doğrudan radar vericilerini kimliklendirme amacı ile kullanılması probleminin Bulanık ARTMAP ile çözümüne yenilikler eklenmiştir. Bunlardan ilki, sistemin karmaşıklığını azaltmak adına geleneksel bulanık ARTMAP algoritmasındaki iki katmanlı benzerlik kontrolünün farklı bir benzerlik ölçütü ile tek seferde gerçekleştirilmesidir. İkinci olarak, yeterli benzerliğe karar vermek için kullanılan eşik (uyanıklık) değeri, eğitim sürecine eklenen bir geçerlilik testi ile ortama göre ayarlanmaktadır. Elde edilen sonuçlar bu iki yeniliğin geleneksel bulanık ARTMAP sınıflandırıcısını sınıflandırma doğruluğu ve karmaşıklık açısından geliştirdiğini onaylamaktadır.Radar emitter identification is an indispensable part of electronic intelligence (ELINT). Due to its ability to assign a new class label to unfamiliar classes and continue learning during testing while at the same time holding the information obtained during training, fuzzy ARTMAP is one of the methods that has been considered for this problem up to now. In this paper, fuzzy ARTMAP is improved in order to identify radar emitters directly from radar Pulse Description Words (PDWs). The first improvement is the use of a one-step similarity check mechanism instead of the two-layer similarity check mechanism of conventional fuzzy ARTMAP in order to decrease the complexity. The second one is that vigilance parameter is set according to the current environment during an extra vigilance-validation stage within training. The results prove that fuzzy ARTMAP is improved with the addition of these two improvements in terms of complexity and classification accuracy

The Visual Object Tracking VOT2017 Challenge Results

The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website

Automatic multi-modal dialogue scene indexing

An automatic algorithm for indexing dialogue scenes in multimedia content is proposed The content is segmented into dialogue scenes using the state transitions of a hidden Markov model (HMM) Each shot is classified using both audio and visual information to determine the state/scene transitions for this model Face detection and silence/speech/music classification are the basic tools which are utilized to index the scenes While face information is extracted after applying some heuristics to skin-colored regions, audio analysis is achieved by examining signal energy, periodicity and zero crossing rate (ZCR) of the audio waveform The simulation results show the possibility of automatically indexing the dialogues using the proposed algorithm

Integrated three-dimensional television - capture, transmission and display (3DTV)

A NoE addressing the 3DTV issues will be formed and kept operational. Creating exact 3D moving images as ghost-like replicas of 3D objects has been an ultimate goal in video science. Capturing 3D scenery, processing the captured data for transmission, and displaying the result for 3D viewing are the main functional components. These components encompass a wide range of disciplines: imaging and computer graphics, signal processing, telecommunications, electronics, optics and physics are needed. The primary objective is to align European researchers with diverse experience and activity in distinct, yet complementary, areas so that an effective network for achieving full scale 3D video capabilities integrated seemlessly to a more general information technology base (like internet), is established and kept functional for a long time. About 150 researchers from well known 20 institutions are committed. The NoE topic and the planed activities well coincide with the objectives of the IST Priority. The NoE will create a highly needed synergy among the European partners, at a critical time since 3DTV related research has been significantly accelerating throughout the world, and therefore will boost the European competitiveness. The activities are grouped under "integration activities", "jointly conducted research activities", "activities to spread excellence" and "management activities. Integration activities are diverse and include general research meetings, setting up internal communications/exchange infrastructure, developing the basis for common software libraries. There is a well- composed organizational structure and a strong management plan. Potential application areas will also be investigated. Social impact of 3DTV and gender related issues in technical research fields will be discussed. The technology level reached after the jointly conducted research activities will be presented as a set of deliverables for exploitation of results.NoE - Network of Excellenc

Sea detection on high-resolution panchromatic satellite images using texture and intensity

In this paper, a two-stage sea-land mask detection algorithm on high resolution panchromatic images is proposed. An initial mask is generated using texture features in the first stage and this mask is refined by using intensity values in the second stage. Image is divided into windows and the Local Binary Patterns (LBP) histograms, evaluated at each window, are modelled using the sea and land sample spaces obtained by the altitude information which has very low resolution compared to the image. These models are utilized for graph cut segmentation algorithm to generate the initial mask. Output mask is generated by thresholding the geodesic distance to the eroded initial mask, calculated on the enhanced and filtered image. Test results obtained on satellite images showed that the proposed algorithm is capable of detection of sea with a high accuracy rate

Block Based Video Data Hiding Using Repeat Accumulate Codes and Forbidden Zone Data Hiding

Video data hiding is still an important research topic due to the design complexities in We propose a new video data hi cling method that makes use of erasure correction capability of Repeat Accumulate codes and superiority of a novel scheme, namely Forbidden Zone Data Hiding Selective embedding is utilized in the proposed method to determine host signal samples suitable for data hiding This method also contains a temporal synchronization scheme in order to withstand frame drop and insert attacks The proposed framework is tested by typical broadcast material against MPEG-2. H 264 compression and some frame-rate conversion attacks The decoding en or values are reponed On typical system parameters The simulation results Indicate that the framework can be successfully utilized in video data hiding application

An Improved Stereo Matching Algorithm with Ground Plane and Temporal Smoothness Constraints

In this study, novel techniques are presented addressing the challenges of stereo matching algorithms for surveillance and vehicle control. For this purpose, one of the most efficient local stereo matching techniques, namely permeability filter, is modified in terms of road plane geometry and temporal consistency in order to take the major challenges of such a scenario into account. Relaxing smoothness assumption of the permeability filter along vertical axis enables extraction of road geometry with high accuracy, even for the cases where ground plane does not contain sufficient textural information. On the other hand, temporal smoothness is enforced by transferring reliable depth assignments against illumination changes, reflections and instant occlusions. According to the extensive experiments on a recent challenging stereo video dataset, the proposed modifications provide reliable disparity maps under severe challenges and low texture distribution, improving scene analyses for surveillance related applications. Although improvements are illustrated for a specific local stereo matching algorithm, the presented specifications and modifications can be applied for the other similar stereo algorithms as well

TEMPORALLY CONSISTENT DENSE DEPTH MAP ESTIMATION VIA BELIEF PROPAGATION

A method for estimating temporally and spatially consistent dense depth maps in multiple camera setups is presented which is important for reduction of perception artifacts in 3D displays. For this purpose, initially, depth estimation is performed for each camera with the piece-wise planarity assumption and Markov Random Field (MRF) based relaxation at each time instant independently. During the relaxation step, the consistency of depth maps for different cameras is also considered for the reliability of the models. Next, temporal consistency of the depth maps is achieved in two steps. In the first step, median filtering is applied for the static or background pixels, whose intensity levels are constant in time. Such an approach decreases the number of inconsistent depth values significantly. The second step considers the moving pixels and MRF formulation is updated by the additional information from the depth maps of the consequent frames through motion compensation. For the solution of the MRF formulation for both spatial and temporal consistency, Belief Propagation approach is utilized. The experiments indicate that the proposed method provide reliable dense depth map estimates both in spatial and temporal domains

Segmentation Driven Semantic Information Inference from 2.5D Data

In this work, we propose a multi-relational concept discovery method for business intelligence applications. Multi-relational data mining finds interesting patterns that span over multiple tables. The obtained patterns reveal useful information for decision making in business environments. However as the patterns include multiple relations, the search space gets intractably complex. In order to cope with this problem, various search strategies, heuristics and language pattern limitations are employed in multi-relational learning systems. In this work, we develop an ILP-based concept discovery method that uses inverse resolution for generalization of concept instances in the presence of background knowledge and refines these patterns into concept definitions by applying specialization operator There are two main benefits in this appoach. The first one is to relax the strong declarative biases and user-defined specifications. The second one is to integrate the method on relational databases so that usage of the system is facilitated in business intelligence applications.Semantic information retrieval from unorganized point clouds becomes necessity for incoming technology such as 3DTV. Besides we surrounded with planar, nearly planar and partially planar things. With this motivation we aim to find planar structures in 2.5D point clouds. With the Hough Transform found in literature, Recursive Hough Transform and Hough Trasform with segmentation algorithms, which are variations of the original algorithm obtained by us, are implemented. K-Means and Mean-shift algorithms, which are popular segmentation methods in 2D, are adapted to 3D with/without color information and their performance analysis are presented

GIBBS RANDOM FIELD MODEL BASED 3-D MOTION ESTIMATION BY WEAKENED RIGIDITY

3-D motion estimation from a video sequence remains a challenging problem. Modelling the local interactions between the 3-D motion parameters is possible by using Gibbs random fields. An energy function which gives the joint probability distribution of the motion vectors, is constructed. The most probable motion vector set is found by maximizing the probability, represented by this distribution. Since the 3-D motion estimation problem is ill-posed, the regularization is achieved by an initial rigidity assumption. Afterwards, the rigidity is weakened hierarchically, until the finest level is reached. At the finest level, each point has its own motion vector and the "weak-connection" between these vectors are described by the energy function. The high computational cost Q decreased considerably by the multiprecision approach. The simulation results support all our discussions