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of 21
pro vyhledávání: '"Kindiroglu, Ahmet Alp"'
Sign language recognition (SLR) has recently achieved a breakthrough in performance thanks to deep neural networks trained on large annotated sign datasets. Of the many different sign languages, these annotated datasets are only available for a selec
Externí odkaz:
http://arxiv.org/abs/2403.14534
Autor:
Bayar, Alperen Enes, Uyan, Ufuk, Toprak, Elif, Yuheng, Cao, Juncheng, Tang, Kindiroglu, Ahmet Alp
Urban environments are characterized by complex structures and diverse features, making accurate segmentation of point cloud data a challenging task. This paper presents a comprehensive study on the application of RandLA-Net, a state-of-the-art neura
Externí odkaz:
http://arxiv.org/abs/2312.11880
Autor:
Yalçın, Metehan, Kindiroglu, Ahmet Alp, Bağcı, Furkan Burak, Uyan, Ufuk, Öztürk, Mahiye Uluyağmur
Researchers are doing intensive work on satellite images due to the information it contains with the development of computer vision algorithms and the ease of accessibility to satellite images. Building segmentation of satellite images can be used fo
Externí odkaz:
http://arxiv.org/abs/2212.13712
This paper presents the preliminary findings of a semi-supervised segmentation method for extracting roads from sattelite images. Artificial Neural Networks and image segmentation methods are among the most successful methods for extracting road data
Externí odkaz:
http://arxiv.org/abs/2212.13079
Over the past decade, there has been a significant increase in the use of Unmanned Aerial Vehicles (UAVs) to support a wide variety of missions, such as remote surveillance, vehicle tracking, and object detection. For problems involving processing of
Externí odkaz:
http://arxiv.org/abs/2212.02302
Autor:
Yalçın, Metehan, Kındıroğlu, Ahmet Alp, Bağcı, Furkan Burak, Uyan, Ufuk, Öztürk, Mahiye Uluyağmur
Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by
Externí odkaz:
http://arxiv.org/abs/2212.02130
Sign Languages are expressed through hand and upper body gestures as well as facial expressions. Therefore, Sign Language Recognition (SLR) needs to focus on all such cues. Previous work uses hand-crafted mechanisms or network aggregation to extract
Externí odkaz:
http://arxiv.org/abs/2009.14139
Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset ai
Externí odkaz:
http://arxiv.org/abs/2004.01283
In this paper, we propose a set of features called temporal accumulative features (TAF) for representing and recognizing isolated sign language gestures. By incorporating sign language specific constructs to better represent the unique linguistic cha
Externí odkaz:
http://arxiv.org/abs/2004.01225
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