The Experts below are selected from a list of 38838 Experts worldwide ranked by ideXlab platform
Dimitrios Marmanis - One of the best experts on this subject based on the ideXlab platform.
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classification with an edge improving semantic image segmentation with Boundary Detection
Isprs Journal of Photogrammetry and Remote Sensing, 2018Co-Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, U StillaAbstract:We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge Detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding Boundary Detection to the segnet encoder-decoder architecture. Second, we also include Boundary Detection in fcn-type models and set up a high-end classifier ensemble. We show that Boundary Detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.
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classification with an edge improving semantic image segmentation with Boundary Detection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, U StillaAbstract:We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large windows (receptive fields). However, this success comes at a cost, since the associated loss of effecive spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge Detection, thus making class-boundaries explicit in the model, First, we construct a comparatively simple, memory-efficient model by adding Boundary Detection to the Segnet encoder-decoder architecture. Second, we also include Boundary Detection in FCN-type models and set up a high-end classifier ensemble. We show that Boundary Detection significantly improves semantic segmentation with CNNs. Our high-end ensemble achieves > 90% overall accuracy on the ISPRS Vaihingen benchmark.
U Stilla - One of the best experts on this subject based on the ideXlab platform.
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classification with an edge improving semantic image segmentation with Boundary Detection
Isprs Journal of Photogrammetry and Remote Sensing, 2018Co-Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, U StillaAbstract:We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge Detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding Boundary Detection to the segnet encoder-decoder architecture. Second, we also include Boundary Detection in fcn-type models and set up a high-end classifier ensemble. We show that Boundary Detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.
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classification with an edge improving semantic image segmentation with Boundary Detection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, U StillaAbstract:We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large windows (receptive fields). However, this success comes at a cost, since the associated loss of effecive spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge Detection, thus making class-boundaries explicit in the model, First, we construct a comparatively simple, memory-efficient model by adding Boundary Detection to the Segnet encoder-decoder architecture. Second, we also include Boundary Detection in FCN-type models and set up a high-end classifier ensemble. We show that Boundary Detection significantly improves semantic segmentation with CNNs. Our high-end ensemble achieves > 90% overall accuracy on the ISPRS Vaihingen benchmark.
Ridha Ilahi - One of the best experts on this subject based on the ideXlab platform.
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PERKIRAAN JARAK OBJEK BERDASARKAN PROYEKSI CITRA PADA SENSOR KAMERA SMARTPHONE MENGGUNAKAN TRIGONOMETRI DAN Boundary Detection PERKIRAAN JARAK OBJEK BERDASARKAN PROYEKSI CITRA PADA SENSOR KAMERA SMARTPHONE MENGGUNAKAN TRIGONOMETRI DAN Boundary Detection
Fakultas Pascasarjana, 2021Co-Authors: Ridha IlahiAbstract:Navigasi mandiri pada robot dan pengukuran jarak objek yang tidak dapat dijangkau oleh manusia atau oleh alat ukur konvensional membutuhkan cara pengukuran yang lebih tepat. Penelitian yang terdahulu sulit diterapkan oleh masyarakat karena membutuhkan alat yang tidak biasa dimiliki, menggunakan lebih dari satu alat, atau menggunakan metode yang rumit. Penelitian ini bertujuan untuk memperkiraan jarak objek berdasarkan proyeksi citra pada sensor kamera smartphone menggunakan trigonometri dan Boundary Detection. Kalibrasi lensa dilakukan untuk menormalisasi peregangan citra. Citra diambil menggunakan smartphone yang dihadapkan tegak lurus terhadap objek. Tinggi antara titik tengah citra dan titik terbawah objek dibantu dengan Boundary Detection dan dinormalisasi berdasarkan kalibrasi lensa kemudian dikonversi ke ukuran sensor. Prediksi jarak dilakukan menggunakan trigonometri berdasarkan tinggi kamera dan sudut yang terbentuk. Tingkat akurasi rata-rata sebelum normalisasi sebesar 98.78% dan setelah normalisasi sebesar 99.16% meningkat sebesar 0.37%. Akurasi maksimal sebelum normalisasi sebesar 99.73% dan setelah normalisasi sebesar 99.95% meningkat sebesar 0.22%. Penelitian ini juga mengungkapkan faktor-faktor yang mempengaruhi akurasi prediksi jarak objek.Banda Ace
Zijing Chen - One of the best experts on this subject based on the ideXlab platform.
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rbnet a deep neural network for unified road and road Boundary Detection
International Conference on Neural Information Processing, 2017Co-Authors: Zhe Chen, Zijing ChenAbstract:Accurately detecting road and its Boundary on the images is an essential task for vision-based autonomous driving systems. However, prevailing methods either only detect road or add an extra processing stage to detect road Boundary. In this work, we introduce a deep neural network, called Road and road Boundary Detection Network (RBNet), that can detect both road and road Boundary in a single process. In specific, we first investigate the contextual relationship between the road structure and its Boundary arrangement and then model them with a Bayesian network. By implementing the Bayesian model, the RBNet can learn to simultaneously estimate the probabilities of a pixel on the image belonging to the road and road Boundary. Comprehensive evaluations are carried out based on the well-known road benchmark, which can demonstrate the compelling performance of the proposed method.
Konrad Schindler - One of the best experts on this subject based on the ideXlab platform.
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classification with an edge improving semantic image segmentation with Boundary Detection
Isprs Journal of Photogrammetry and Remote Sensing, 2018Co-Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, U StillaAbstract:We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge Detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding Boundary Detection to the segnet encoder-decoder architecture. Second, we also include Boundary Detection in fcn-type models and set up a high-end classifier ensemble. We show that Boundary Detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.
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classification with an edge improving semantic image segmentation with Boundary Detection
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, U StillaAbstract:We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large windows (receptive fields). However, this success comes at a cost, since the associated loss of effecive spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge Detection, thus making class-boundaries explicit in the model, First, we construct a comparatively simple, memory-efficient model by adding Boundary Detection to the Segnet encoder-decoder architecture. Second, we also include Boundary Detection in FCN-type models and set up a high-end classifier ensemble. We show that Boundary Detection significantly improves semantic segmentation with CNNs. Our high-end ensemble achieves > 90% overall accuracy on the ISPRS Vaihingen benchmark.