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An Improved Convexity Based Segmentation Algorithm for Heavily Camouflaged Images  [cached]
Amarjot Singh,N Sumanth Kumar
International Journal of Image, Graphics and Signal Processing , 2013,
Abstract: The paper proposes an advanced convexity based segmentation algorithm for heavily camouflaged images. The convexity of the intensity function is used to detect camouflaged objects from complex environments. We take advantage of operator for the detection of 3D concave or convex graylevels to exhibit the effectiveness of camouflage breaking based on convexity. The biological motivation behind operator and its high robustness make it suitable for camouflage breaking. The traditional convexity based algorithm identifies the desired targets but in addition also identifies sub-targets due to their three dimensional behavior. The problem is overcome by combining the conventional algorithm with thresholding. The proposed method is able to eliminate the sub-targets leaving behind only the target of interest in the input image. The proposed method is compared with the conventional operator. It is also compared with some conventional edge based operator for performance evaluation.
Target Detection in Three-Dimension Sensor Networks Based on Clifford Algebra  [PDF]
Tiancheng HE, Weixin XIE, Wenming CAO
Wireless Sensor Network (WSN) , 2009, DOI: 10.4236/wsn.2009.12013
Abstract: The three-dimensional sensor networks are supposed to be deployed for many applications. So it is signifi-cant to do research on the problems of coverage and target detection in three-dimensional sensor networks. In this paper, we introduced Clifford algebra in 3D Euclidean space, developed the coverage model of 3D sensor networks based on Clifford algebra, and proposed a method for detecting target moving. With Clif-ford Spinor, calculating the target moving formulation is easier than traditional methods in sensor node’s coverage area.
Coverage Assessment and Target Tracking in 3D Domains  [PDF]
Noureddine Boudriga,Mohamed Hamdi,Sitharama Iyengar
Sensors , 2011, DOI: 10.3390/s111009904
Abstract: Recent advances in integrated electronic devices motivated the use of Wireless Sensor Networks (WSNs) in many applications including domain surveillance and mobile target tracking, where a number of sensors are scattered within a sensitive region to detect the presence of intruders and forward related events to some analysis center(s). Obviously, sensor deployment should guarantee an optimal event detection rate and should reduce coverage holes. Most of the coverage control approaches proposed in the literature deal with two-dimensional zones and do not develop strategies to handle coverage in three-dimensional domains, which is becoming a requirement for many applications including water monitoring, indoor surveillance, and projectile tracking. This paper proposes efficient techniques to detect coverage holes in a 3D domain using a finite set of sensors, repair the holes, and track hostile targets. To this end, we use the concepts of Voronoi tessellation, Vietoris complex, and retract by deformation. We show in particular that, through a set of iterative transformations of the Vietoris complex corresponding to the deployed sensors, the number of coverage holes can be computed with a low complexity. Mobility strategies are also proposed to repair holes by moving appropriately sensors towards the uncovered zones. The tracking objective is to set a non-uniform WSN coverage within the monitored domain to allow detecting the target(s) by the set of sensors. We show, in particular, how the proposed algorithms adapt to cope with obstacles. Simulation experiments are carried out to analyze the efficiency of the proposed models. To our knowledge, repairing and tracking is addressed for the first time in 3D spaces with different sensor coverage schemes.
Are camouflaged seeds less attacked by wild birds?
Almeida, Alexandre de;Couto, Hilton Thadeu Zarate do;Almeida, álvaro Fernando de;
Scientia Agricola , 2010, DOI: 10.1590/S0103-90162010000200007
Abstract: wheat, corn and rice crops in brazil use seeds treated with systemic insecticide/nematicide carbofuran, mixed to rhodamine b red dye. carbofuran is toxic and rhodamine b is attractive to wild birds that eat up these seeds, resulting in notable mortality during planting. a field experiment was performed in southeast brazil to evaluate if camouflaged seeds would be less consumed by wild birds in comparison to commercial seeds with red-colored rhodamine b and aposematic blue seeds. camouflaged seeds were less removed than seeds with rhodamine b and natural colors. the camouflaging was more effective in the presence of irregularities and litter. there was no removal of blue-colored seeds. as legislation requires treated seeds to receive a different color to avoid accidents with humans, camouflaging may be used as replacement of rhodamine b to reduce mortality rates of wild birds.
Target Detection Algorithm Based on the Movement of Codebook Model  [cached]
Kaige Chen,Xiaojun Han,Tenghao Huang
Computer and Information Science , 2012, DOI: 10.5539/cis.v5n2p49
Abstract: Movement target detection is the researchful emphasis and aporia in the fields of computer vision, model recognition and video coding. In order to extract moving targets from the complex background scenes, this paper puts forward to calculate the color distortion degree by means of getting every pixel or a group of pixels of time series model, and converting pixels from RGB space to HSV space. Based on the background Codebook model of the target motion detection algorithm, the experiment shows that the method can achieve better target detection quality.
Multiple Target Detection for HRR Signal Design
Mohd. Moazzam Moinuddin,Mallikarjuna Reddy. Y.,Pasha. I. A,Lal Kishore. K
International Journal of Engineering and Technology , 2010,
Abstract: This paper addresses the problem of Signal design for multiple target detection. The notion of poly-semantic radar, which gave improved performance through coincidence detection, is analyzed for high resolution radar system in presence of high density additive noise and Doppler shift. These sequences are optimized by employing Hamming backtrack algorithm (HBT). he detection capability of poly-semantic sequences is further improved through coincidence detection of the return signal. The simulation results show that the proposed sequences give improved robustness of noise and Doppler shift for HRR target detection compared to conventional pulse compression sequences.
Multi-target tracking algorithms in 3D  [PDF]
Rastislav Telgarsky
Computer Science , 2012,
Abstract: Ladars provide a unique capability for identification of objects and motions in scenes with fixed 3D field of view (FOV). This paper describes algorithms for multi-target tracking in 3D scenes including the preprocessing (mathematical morphology and Parzen windows), labeling of connected components, sorting of targets by selectable attributes (size, length of track, velocity), and handling of target states (acquired, coasting, re-acquired and tracked) in order to assemble the target trajectories. This paper is derived from working algorithms coded in Matlab, which were tested and reviewed by others, and does not speculate about usage of general formulas or frameworks.
Radar Target Detection Using Hidden Markov Models
Serdar Tugac;Murat Efe
PIER B , 2012, DOI: 10.2528/PIERB12081603
Abstract: Standard radar detection process requires that the sensor output is compared to a predetermined threshold. The threshold is selected based on a-priori knowledge available and/or certain assumptions. However, any knowledge and/or assumptions become inadequate due to the presence of multiple targets with varying signal return and usually non stationary background. Thus, any fixed predefined threshold may result in either increased false alarm rate or increased track loss. Even approaches where the threshold is adaptively varied will not perform well in situations when the signal return from the target of interest is too low compared to the average level of the background. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. However, although TBD techniques eliminate the need for detection threshold at sensor's signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Hidden Markov Model (HMM) based target detection method that avoids any thresholding at any stage of the detection process. Moreover, since the proposed HMM method is based on the target motion models, the output of the detection process can easily be employed for manoeuvring target tracking.
Face Detection with a 3D Model  [PDF]
Adrian Barbu,Nathan Lay,Gary Gramajo
Computer Science , 2014,
Abstract: This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six dimensional pose space is addressed by proposing meaningful 3D pose candidates by image-based regression from detected face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive classifier based on difference features relative to the 3D pose. A compatible subset of candidates is then obtained by non-maximal suppression. Experiments on two standard face detection datasets show that the proposed 3D model based approach obtains results comparable to or better than state of the art.
Evaluating Subpixel Target Detection Algorithms in Hyperspectral Imagery  [PDF]
Yuval Cohen,Yitzhak August,Dan G. Blumberg,Stanley R. Rotman
Journal of Electrical and Computer Engineering , 2012, DOI: 10.1155/2012/103286
Abstract: Our goal in this work is to demonstrate that detectors behave differently for different images and targets and to propose a novel approach to proper detector selection. To choose the algorithm, we analyze image statistics, the target signature, and the target's physical size, but we do not need any type of ground truth. We demonstrate our ability to evaluate detectors and find the best settings for their free parameters by comparing our results using the following stochastic algorithms for target detection: the constrained energy minimization (CEM), generalized likelihood ratio test (GLRT), and adaptive coherence estimator (ACE) algorithms. We test our concepts by using the dataset and scoring methodology of the Rochester Institute of Technology (RIT) Target Detection Blind Test project. The results show that our concept correctly ranks algorithms for the particular images and targets including in the RIT dataset.
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