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This study presents a novel landmine identification method that estimates intrinsic parameters of buried objects from their primary and secondary GPR reflections to reduce false alarm rates of GPR-based landmine detection algorithms. To achieve this, two different features are extracted from A-scan GPR data of buried objects. The first feature identifies significant GPR signal length. The second feature estimates intrinsic impedance of the object. These two features are classified with support vector machine (SVM) classifier. The experimental results show that the proposed features have very high discrimination power which reduces false alarm rates to a great extent.

A vehicle classification system, which uses features based on radiometry, is developed for single band infrared (IR) image sequences. In this context, the process is divided into three components. These are moving vehicle detection, radiance estimation, and classification. The major contribution of this paper lies in the usage of the radiance values as features, other than the raw output of IR camera output, to improve the classification performance of the detected objects. The motivation behind this is that each vehicle class has a discriminating radiance value that originates from the source temperature of the object modified by the intrinsic characteristics of the radiating surface and the environment. As a consequence, significant performance gains are achieved due to the use of radiance values in classification for the utilized measurement system.

Infrared imagery is widely used in many applications in both civilian and military areas. In landmine detection, the goal is to detect the anomalies between mine surface and soil from variation of reflected/emitted thermal radiation. In this study, various types of anomaly detection techniques of IR are investigated and the feasibility of these techniques for use in landmine detection is analyzed. Additionally, effects of parameters for algorithms are compared and the parameters are optimized for increasing detection accuracy. Furthermore, fusion of the algorithms is performed to reduce False Alarm Rate (FAR). We also prepare an experimental setup to reflect the effects of environmental changes on FLIR imagery recording. Soil and various types of landmine mock-ups are also examined in this setup. Finally, all anomalies are mapped into local coordinate space for indicating possible landmines locations.

In this paper, a decision level fusion using multiple pre-screener algorithms is proposed for the detection of buried landmines from Ground Penetrating Radar (GPR) data. The Kernel Least Mean Square (KLMS) and the Blob Filter pre-screeners are fused together to work in real time with less false alarms and higher true detection rates. The effect of the kernel variance is investigated for the KLMS algorithm. Also, the results of the KLMS and KLMS+Blob filter algorithms are compared to the LMS method in terms of processing time and false alarm rates. Proposed algorithm is tested on both simulated data and real data collected at the field of IPA Defence at METU, Ankara, Turkey.

In this paper, we propose a system to detect buried disk-shaped landmines from ground penetrating radar (GPR) and forward-looking long wave infrared (FL-LWIR) data. The data is collected from a test area of 500m 2 , which was prepared at the IPA Defence, Ankara, Turkey. This test area was divided into four lanes, each of size 25m length by 4m width and 1m depth. Each lane was first carefully cleaned of stones and clutter and then filled with different soil types, namely fine-medium sand, course sand, sandy silt loam and loam mix. In all lanes, various clutter objects and landmines were buried at different depths and at 1meter intervals. In the proposed approach, IR data is used as a pre-screener. Then possible target regions are further analyzed using the GPR data. IR data processing is done in three steps such as preprocessing, target detection, and postprocessing. In the pre-processing stage, bilateral noise reduction filtering is performed. The target detection stage finds circular targets by a radial transformation algorithm. The proposed approach is compared with the RX algorithm used widely for anomaly detection. The suspicious regions are further analyzed using Histogram of Oriented Gradient (HOG) features that are extracted from GPR images and classified by SVM. The same approach can also be applied in a parallel way where the results are combined using decision level fusion. The results of the proposed approach are given on different scenarios including different weather temperature and depth of buried targets.

In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh frames into two sets by temporal subsampling and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or so-called descriptions are transmitted independently. The 3D dynamic mesh coder is based on predictive coding with spatial and temporal layered decomposition. In addition, the proposed method allows for different redundancy allocations by including a number of encoded spatial layers of the frames in the other set. The algorithm is evaluated with redundancy-rate-distortion curves and it is shown that, when one of the descriptions is lost, acceptable quality can be achieved with around 50% redundancy.

A European consortium of six partners has been developing core technological components of a mobile 3D television system over DVB-H channel. In this overview paper, we present our current results on developing optimal methods for stereo-video content creation, coding and transmission and emphasize their significance for the power-constrained mobile platform, equipped with auto-stereoscopic display. We address the user requirements by applying modern usercentered approaches taking into account different user groups and usage contexts in contrast to the laboratory assessment methods which, though standardized, offer limited applicability to real applications. To this end, we have been aiming at developing a methodological framework for the whole system development process. One of our goals has been to further develop the user-centered approach towards experienced quality of critical system components. In this paper, we classify different research methods and technological solutions analyzing their pros and constraints. Based on this analysis we present the user-centered methodological framework being used throughout the whole development process of the system and aimed at achieving the best performance and quality appealing to the end user.

In this paper, we propose a blind watermarking method where watermark is chosen as the hologram of the signal to be embedded. In the proposed approach the quantized phase of the hologram is embedded into an image using quantization index modulation (QIM). The robustness of the proposed technique is tested against several attacks such as filtering, compression, occlusion and cropping. Also the effects of quantization of the hologram on the reconstruction quality are demonstrated.