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This paper proposes an algorithm that improves ship detection accuracy using preprocessing and post-processing. To achieve this, high-resolution electro-optical satellite images with a wide range of shape and texture information were considered. The developed algorithms display the problem of unreliable detection of ships owing to clouds, large waves, weather influences, and shadows from large terrains. False detections in land areas with image information similar to that of ships are observed frequently. Therefore, this study involves three algorithms: global feature enhancement pre-processing (GFEP), multiclass ship detector (MSD), and false detected ship exclusion by sea land segmentation image (FDSESI). First, GFEP enhances the image contrast of high-resolution electro-optical satellite images. Second, the MSD extracts many primary ship candidates. Third, falsely detected ships in the land region are excluded using the mask image that divides the sea and land. A series of experiments was performed using the proposed method on a database of 1984 images. The database includes five ship classes. Therefore, a method focused on improving the accuracy of various ships is proposed. The results show a mean average precision (mAP) improvement from 50.55% to 63.39% compared with other deep learning-based detection algorithms.

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Automatic segmentation of intracellular compartments is a powerful technique, which provides quantitative data about presence, spatial distribution, structure and consequently the function of cells. With the recent development of high throughput volumetric data acquisition techniques in electron microscopy (EM), manual segmentation is becoming a major bottleneck of the process. To aid the cell research, we propose a technique for automatic segmentation of mitochondria and endolysosomes obtained from urinary bladder urothelial cells by the dual beam EM technique. We present a novel publicly available volumetric EM dataset - the first of urothelial cells, evaluate several state-of-the-art segmentation methods on the new dataset and present a novel segmentation pipeline, which is based on supervised deep learning and includes mechanisms that reduce the impact of dependencies in the input data, artefacts and annotation errors. We show that our approach outperforms the compared methods on the proposed dataset.

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Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.

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A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.

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Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

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In order to determine the effects on foraminifera due to spilled crude oil in the "Herbei Spirit" incident, a study of benthic foraminiferal assemblages was carried out on sediment samples collected from the Sogeunri tidal flat, Taean Peninsula, Korea. Breakages of the chambers in the Ammonia beccarii and Elphidium subincertum species of the Sogeunri tidal flat with a low pH (6.98 on average) were marked. These chamber breakages occurred in 71.6% of A. beccarii and are thought to be caused by decalcification due to the fall in pH resulting from the "Hebei Spirit" oil spill. The factors that affect breakage of the chamber in benthic foraminifera under low pH condition may be not only deto decalcification but also to exposure duration of substrata in the tidal flat spilled crude oil.

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This study describes the ultrastructural characteristics of external epidermis of mantle of Sepia esculenta using light and electron microscopy. The epidermis was thicker on the ventral surface than on the dorsal surface, with a higher secretory cell distribution on the ventral surface than on the dorsal surface. The epidermis was a single layer composed of epithelial cells, secretory cells, ciliated cells and neuroglial cells. Epithelial cells were columnar with well-developed microvilli on the free surface, and the microvilli were covered with glycocalyx. The epithelial cells were connected to the neighboring cells by tight junctions and membrane interdigitations of the apico-frontal surface. Well-developed microfilaments were arranged in a vertical direction in the cortical cytoplasm. The secretory cells were categorized into three types (A, B and C) in accordance with the light microscopical characteristics and ultrastructures of the secretory granules. The distribution of these cells was in the following order: Type A>Type B>Type C. SEM observation revealed that the secretory pore size of the Type A secretory cells was approximately 8.6 µm×12.2 µm. Cytoplasm displayed a red color as the result of Masson's trichrome stain and H-E stain, and contained polygonal granules of approximately 1.2 µm2 with a high electron density. The secretory pore size of the Type B secretory cells was approximately 10.1 µm×12.1 µm. As the results of AB-PAS (pH 2.5) and AF-AB (pH 2.5) reactions, the cytoplasm displayed a red color. The cells contained membrane bounded secretory granules with very low electron density. The secretory pore of the Type C secretory cells was circular shape, and approximately 5.5 µm×5.5 µm. Cytoplasm was found to be homogeneous under H-E stain and Masson's trichrome stain, and displayed a red color. As the result of AB-PAS (pH 2.5) reaction, the cytoplasm displayed a red color. The electron density of the secretory substance was the highest among the three types of secretory cells. The ciliated cells had a ciliary tuft on the free surface and were distributed throughout the mantle with the exception of the adhesive organs. Neuroglial cells were connected to the basal membrane, epithelial cells, secretory cells and nerve fibers through cytoplasmic process, and contained neurosecretory granules with high electron density within the cytoplasm.

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Pen shell (Atrina pectinata) is a popular food source with a high commercial value in a number of Asian Pacific areas. The natural A. pectinata population has been declining continuously over the past several decades. Microsatellite DNA markers are a useful DNA-based tool for monitoring the genetic variation of pen shell populations. In this study, 20 polymorphic microsatellite (MS) DNA markers were identified from a partial genomic pen shell DNA library enriched in CA repeats, and used to compare allelic variation between wild and hatchery pen shell populations in Korea. A total of 438 alleles were detected at the 20 MS loci in the two populations. All loci were easily amplified and demonstrated allelic variability, with the number of alleles ranging from 5 to 35 in the wild population and from 5 to 22 in the farmed population. The average observed and expected heterozygosities were 0.69 and 0.82, respectively, in the hatchery samples and 0.69 and 0.83, respectively, in the wild samples. Statistical analysis of fixation index (F(ST)) and analysis of molecular variance (AMOVA) showed minor, but significant, genetic differences between the wild and hatchery populations (F(ST) = 0.0106, CI(95%) = 0.003-0.017). These microsatellite loci may be valuable for future aquaculture and population genetic studies for developing conservation and management plans. Further studies with additional pen shell samples are needed to conclusively determine the genetic diversity between the wild and hatchery populations.

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