A robust nonlinear scale space change detection approach for SAR images

In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance

Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

A taxonomic study of the genus Fibigia Medik. (Brassicaceae)

In this study, Purpureae A.Duran and Ö.Çetin sect. nov. is described under the genus Fibigia Medik. The taxa of the genus were revised under the sections Fibigia and Purpureae. Fibigia clypeata (L.) Medik was classified as F. clypeata (L.) Medik subsp. clypeata and subsp. anatolica A.Duran & Tustas subsp. nov., and Fibigia eriocarpa (DC.) Boiss. was rearranged as F. clypeata (L.) Medik subsp. clypeata var. eriocarpa in Turkish Flora. The infrageneric and subgeneric keys were revised. Seed and pollen surface ornamentations were studied by scanning electron microscopy (SEM). The pollens examined have tricolpate aperture type and reticulate surface ornamentation. Seed surface ornamentation was reticulate. The taxa of the genus Fibigia have a diploid chromosome number of 2n=16. Karyotype analyses of the taxa were carried out for the first time. It was found that each taxon differed in chromosome morphology.Key words: Fibigia, morphology, pollen, scanning electron microscopy (SEM), new taxa, Turkey

Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video

Real-time flame detection is crucial in video based surveillance systems. We propose a vision-based method to detect flames using Deep Convolutional Generative Adversarial Neural Networks (DCGANs). Many existing supervised learning approaches using convolutional neural networks do not take temporal information into account and require substantial amount of labeled data. In order to have a robust representation of sequences with and without flame, we propose a two-stage training of a DCGAN exploiting spatio-temporal flame evolution. Our training framework includes the regular training of a DCGAN with real spatio-temporal images, namely, temporal slice images, and noise vectors, and training the discriminator separately using the temporal flame images without the generator. Experimental results show that the proposed method effectively detects flame in video with negligible false positive rates in real-time

Interactive ship segmentation in SAR images (SAR görüntülerinde etkileşimli gemi bölütleme)

Ship detection from synthetic aperture radar (SAR) images is important for various automatic target recognition (ATR) tasks. Although the ships in offshore areas can be easily detected, the ones near the shores or close to each other are difficult to detect. Furthermore, segmentation and classification of such ships is extremely difficult. In this study, a novel approach is presented for the fast and accurate segmentation of ship boundaries with minimal user interaction. In this approach, the rough location and orientation of a ship is determined by the user. Then, a ship model, which is constructed from synthetic ship images, is fitted on to the ship selected by the user and accurate ship boundaries are extracted. The effectiveness of the proposed algorithm is demonstrated by experimental results

Sparsity Based Image Retrieval using relevance feedback

In this paper, a Content Based Image Retrieval (CBIR) algorithm employing relevance feedback is developed. After each round of user feedback Biased Discriminant Analysis (BDA) is utilized to find a transformation that best separates the positive samples from negative samples. The algorithm determines a sparse set of eigenvectors by L1 based optimization of the generalized eigenvalue problem arising in BDA for each feedback round. In this way, a transformation matrix is constructed using the sparse set of eigenvectors and a new feature space is formed by projecting the current features using the transformation matrix. Transformations developed using the sparse signal processing method provide better CBIR results and computational efficiency. Experimental results are presented. © 2012 IEEE

Iterated Prisoners Dilemma with limited attention

How attention scarcity effects the outcomes of a game? We present our findings on a version of the Iterated Prisoners Dilemma (IPD) game in which players can accept or refuse to play with their partner. We study the memory size effect on determining the right partner to interact with. We investigate the conditions under which the cooperators are more likely to be advantageous than the defectors. This work demonstrates that, in order to beat defection, players do not need a full memorization of each action of all opponents. There exists a critical attention capacity threshold to beat defectors. This threshold depends not only on the ratio of the defectors in the population but also on the attention allocation strategy of the players.Comment: 8 pages, 3 figure

Assessment of cardiac ultrasonography in predicting outcome in adult cardiac arrest

Objective: A prospective follow-up study to evaluate the ability of cardiac ultrasonography performed by emergency physicians to predict resuscitation outcome in adult cardiac arrest patients. METHODS: Ultrasonographic examination of the subxiphoid cardiac area was made immediately on presentation to the emergency department with pulseless cardiac arrest. Sonographic cardiac activity was defined as any detected motion within the heart including the atria, ventricles or valves. Successful resuscitation was defined as any of: return of spontaneous circulation for ≥ 20 min; return of breathing; palpable pulse; measurable blood pressure. RESULTS: The study enrolled 149 patients over an 18-month period. The presence of sonographic cardiac activity at the beginning of resuscitation was significantly associated with a successful outcome (19/27 [70.4%] versus 55/122 [45.1%] patients without cardiac activity at the beginning of resuscitation). CONCLUSIONS: Ultrasono -graphic detection of cardiac activity may be useful in determining prognosis during cardiac arrest. Further studies are needed to elucidate the predictive value of ultrasonography in cardiac arrest patients. © 2012 Field House Publishing LLP

Cepstrum based feature extraction method for fungus detection

In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)

Framework for online superimposed event detection by sequential Monte Carlo methods

In this paper, we consider online seperation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a 1D-signal, is superimposed by an Auto-Regressive (AR) 'event signal', but the proposed approach is applicable in a more general setting. The activation and deactivation times of the event-signal are assumed to be unknown. We solve the online detection problem of this superpositional event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach. ©2008 IEEE