Search

In this study, the issue of intent classification in frequently asked questions for chatbots is discussed. There are many studies on this subject. However, most of the studies were conducted for the English language, and the issue of improving the intent classification results for small data sets has not been adequately examined. For this purpose, frequently asked questions data were used with subword semantic hashing method and its effects in Turkish and English languages were examined. According to the results, we observe an increase in classification performance of the subword semantic hashing method in Turkish language and the improvement is significantly higher compared to the English language. In addition, the classification performance results of pretrained the subword semantic hashing is better than the results of neural network models such as CLIP and FastText.

LiDAR technology is advancing. As a result, researchers can benefit from high-resolution height data from Earth's surface. Digital terrain model (DTM) generation and point classification (filtering) are two important problems for LiDAR data. These are connected problems since solving one helps solving the other. Manual classification of LiDAR point data could be time consuming and prone to errors. Hence, it would not be feasible. Therefore, researchers proposed several methods to solve DTM generation and point classification problems. Although these methods work fairly well in most cases, they may not be effective for all scenarios. To contribute in this research topic, a novel method based on two-dimensional (2-D) empirical mode decomposition (EMD) is proposed in this study. Local, nonlinear, and nonstationary characteristics of EMD allow better DTM generation. The proposed method is tested on two publicly available LiDAR dataset, and promising results are obtained. Besides, the proposed method is compared with other methods in the literature. Comparison results indicate that the proposed method has certain advantages in terms of performance.

Automated digital terrain model (DTM) generation from remotely sensed data has gained wide application areas due to increased sensor resolution. In this study, a novel ground filtering and segmentation method is proposed for digital surface model (DSM) data. The proposed method starts with extracting DSM feature points. These are used in a probabilistic framework to generate a non-ground object probability map in spatial domain. Modes of this map are used as seed points in a novel segmentation method based on morphological operations. This leads to ground filtering and DTM generation. The method is tested on three different data sets. Two of these originate from light detection and ranging (lidar) sensors, where resulting kappa coefficient (?) range mostly higher than 95% for differently structured urban areas. Also, the visual appearance of the generated DTM exhibits obvious improvements over all other investigated methods. The third data set is a DSM obtained from WorldView-2 stereo image pairs. Also here, we compare our results with three different methods in the literature. Although the DSM quality is much lower, more than 85% of ? can be reached by the proposed method, showing its superiority over other methods. Overall experimental results show that the proposed method can be used reliably for DTM generation. The results also indicate that the method has prominent advantages in comparison to established methodologies in terms of robustness in handling urban areas of different properties. Moreover, there are only few parameters to adjust in the proposed method, and these are independent of the object size in DSM data. © 2018 Informa UK Limited, trading as Taylor & Francis Group. 1,14e+201 This work is supported by TUBITAK through project no 114E199.

Automated object detection in remotely sensed data has gained wide application areas due to increased sensor resolution. In this study, we propose a novel building detection method using high resolution DSM data and true orthophoto image. In the proposed method, DSM feature points and NDVI are obtained. Then, they are used for spatial voting to generate a building probability map. Local maxima of this map are used as seed points for segmentation. For this purpose, a morphology based segmentation method is proposed. This way, buildings are detected from DSM data. We tested our method on ISPRS semantic labeling dataset and obtained promising results. © 2016 IEEE. 24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- -- 122605

Automatic extraction of bare-Earth LiDAR points to generate Digital Terrain Model (DTM) is still an ongoing problem. Even though there are several methods for ground filtering, automatic and adaptive methods are still a need due to the complexity of the environment. In this study, we address the ground filtering problem by applying Empirical Mode Decomposition (EMD) to the airborne LiDAR data. EMD is a data-driven method that adapts to the local characteristics of the signal. We benefit from EMD to extract the local trend of the LiDAR height data. This way, can extract a local adaptive threshold to filter ground and non-ground objects. We tested our method using the ISPRS LiDAR reference dataset and obtained promising results. We also compared the filtering results with the ones in the literature to show the improvements obtained. © 2015 IEEE. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052

Crowd monitoring is an important task of security forces. If an emergency occurs during large events, authorities should take urgent measures to prevent causalities. Also understanding crowd dynamics such as tracking crowds or sparse people goups before an emergency occurs is a need. Therefore, crowd detection and analysis is a critical research area. There are several studies for crowd monitoring that use street or indoor cameras which may not be directly used for analyzing large crowds. In this study, we approach the problem using aerial images. We propose two novel methods. In the first method, we use first-order spatial point statistics. It uses the nearest neighbor relations for each person in the image to detect crowd regions. Our second method also uses the first order statistics with an additional sparse people group detection flexibility. We test the proposed methods on two aerial images and provide quantitative test results. © 2015 IEEE. Aselsan;e al.;HAVELSAN;Roketsan;TAI;TURKSAT 7th International Conference on Recent Advances in Space Technologies, RAST 2015 -- 16 June 2015 through 19 June 2015 -- -- 116912

The crowd density in public places increases in social events. If an emergency occurs during such events, authorities should take urgent measures to prevent causalities. Therefore, crowd detection and analysis is a critical research area. Even though there are several studies on person detection from street or indoor cameras, these may not be directly used to detect or analyze the crowd formed from people. In this study, we approach the problem using aerial images. We propose two novel methods to detect the crowd using spatial statistics. The first novel method is based on the first-order statistics. It uses the nearest neighbor relations for each person in the image. The second novel method is based on the second-order statistics. Here, the spatial position of persons are checked whether they are clustered or randomly distributed. We test these two methods on a sample test image and provide performance measures. © 2015 IEEE. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052

LiDAR data provides valuable information for various remote sensing applications. For these, one important and challenging problem is ground filtering. This operation separates the bare earth and object data. Researchers proposed several methods to solve this problem. However, the complexity of the data limit the usability of these methods for all terrain types. Besides, the performance obtained in ground filtering should be improved further. In this study, we focus on this problem and propose a novel ground filtering method using Empirical Mode Decomposition (EMD). We tested the proposed method on the standard ISPRS data set and evaluate its strengths and weaknesses. We also compared the proposed method with the ones in the literature to show the improvements obtained.

In this paper, sea clutter radar plots are modeled by spatial point processes. A test procedure is proposed to analyze "Complete Spatial Randomness (CSR)" characteristics of radar plot locations. Plot intensity map is also constructed. This map is separated into two sub-regions; cutter region and moving target region. This map can be used as a reliability metric for target detection algorithms.