標(biāo)題: Titlebook: Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik; In Gemeinschaft mit F. Neureiter (o. Professor Dr. Med [打印本頁(yè)] 作者: Cleveland 時(shí)間: 2025-3-21 16:28
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik影響因子(影響力)
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik影響因子(影響力)學(xué)科排名
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik網(wǎng)絡(luò)公開(kāi)度
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik被引頻次
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik被引頻次學(xué)科排名
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik年度引用
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik年度引用學(xué)科排名
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik讀者反饋
書目名稱Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik讀者反饋學(xué)科排名
作者: circumvent 時(shí)間: 2025-3-21 22:31 作者: 有組織 時(shí)間: 2025-3-22 03:39
F. v. Neureiter,F. Pietrusky,E. Schütt address the above challenges. GCEENet features a combination of global context encoders and local distribution modules, working in conjunction to preserve the global image context. Our experiments on several medical image segmentation datasets show that GCEENet outperforms current state-of-the-art models in all measured metrics.作者: follicle 時(shí)間: 2025-3-22 08:18 作者: 極小量 時(shí)間: 2025-3-22 12:14 作者: fabricate 時(shí)間: 2025-3-22 14:57 作者: PLE 時(shí)間: 2025-3-22 20:46
ta augmentation and balancing. We show that a very small convolutional neural network (SAT-CNN) with approximately three million parameters can outperform a deep pre-trained classifier, VGG16 - which is used for many state-of-the-art tasks - with over 138 million parameters.作者: Foment 時(shí)間: 2025-3-22 21:19
F. v. Neureiter,F. Pietrusky,E. Schüttrk architecture called NeoUNet, along with a hybrid loss function to solve this problem. Experiments show highly competitive results for NeoUNet on our benchmark dataset compared to existing polyp segmentation models.作者: 失望昨天 時(shí)間: 2025-3-23 01:45 作者: 抱怨 時(shí)間: 2025-3-23 08:16 作者: 寄生蟲(chóng) 時(shí)間: 2025-3-23 10:10
F. v. Neureiter,F. Pietrusky,E. Schütt4) comprises topics such as visualization; visual computing with multimodal data streams; visual computing in digital cultural heritage; intelligent environments: algorithms and applications; applications;?virtual reality.作者: Breach 時(shí)間: 2025-3-23 17:49 作者: Admire 時(shí)間: 2025-3-23 20:53 作者: 黃油沒(méi)有 時(shí)間: 2025-3-24 01:39
low object resolution. In this work we focus on recognizing objects taken from the xView Satellite Imagery dataset. The xView dataset introduces its own set of challenges, the most prominent being the imbalance between the 60 classes present. xView also contains considerable label noise as well as b作者: reperfusion 時(shí)間: 2025-3-24 03:10
F. v. Neureiter,F. Pietrusky,E. Schüttd subtle inter-class differences. In this paper, we tackle this problem in a weakly supervised manner, where neural network models are getting fed with additional data using a data augmentation technique through a visual attention mechanism. We perform domain adaptive knowledge transfer via fine-tun作者: 不來(lái) 時(shí)間: 2025-3-24 07:01 作者: 辭職 時(shí)間: 2025-3-24 11:05
F. v. Neureiter,F. Pietrusky,E. Schüttization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection,作者: FECT 時(shí)間: 2025-3-24 15:37
F. v. Neureiter,F. Pietrusky,E. Schüttce the benefits of such analysis. This paper presents a novel technique to automatically recover both intrinsic and extrinsic parameters for each surveillance camera within a camera network by only using a walking human. The same feature points of a pedestrian are taken to calculate each camera’s in作者: CULP 時(shí)間: 2025-3-24 22:22
F. v. Neureiter,F. Pietrusky,E. Schütties a series of operators in a pipeline to transform a video stream into shots. These operators are replicated to work in parallel on Flink-managed computing nodes. The V2F deployment of the standard twin-comparison video segmentation method is more than 7 times faster than its non-parallel (i.e., s作者: biosphere 時(shí)間: 2025-3-25 00:39 作者: 精密 時(shí)間: 2025-3-25 06:50
F. v. Neureiter,F. Pietrusky,E. Schüttntation models is the inherent complexity and inter-connectivity of pixels in medical images. These characteristics require modeling not only local features but also a global understanding of image semantics. In this paper, we propose a deep convolutional neural network called GCEENet to effectively作者: OATH 時(shí)間: 2025-3-25 08:56
F. v. Neureiter,F. Pietrusky,E. Schüttntation models is the inherent complexity and inter-connectivity of pixels in medical images. These characteristics require modeling not only local features but also a global understanding of image semantics. In this paper, we propose a deep convolutional neural network called GCEENet to effectively作者: stroke 時(shí)間: 2025-3-25 13:42 作者: Minatory 時(shí)間: 2025-3-25 16:41
F. v. Neureiter,F. Pietrusky,E. Schütth as joint angles of a 3D object. The probability of the object’s states, including correlations between the state parameters, is learned a priori from training samples. We introduce a framework that integrates this knowledge into the family of particle filters and particularly into the annealed par作者: 反感 時(shí)間: 2025-3-25 20:23 作者: 無(wú)能力之人 時(shí)間: 2025-3-26 00:24
F. v. Neureiter,F. Pietrusky,E. Schütt Rethymnon, Crete, Greece, in July 2013. The 63 revised full papers and 35 poster papers presented together with 32 special track papers were carefully reviewed and selected from more than 220 submissions. The papers are organized in topical sections: Part I (LNCS 8033) comprises computational bioim作者: Leisureliness 時(shí)間: 2025-3-26 07:48
F. v. Neureiter,F. Pietrusky,E. Schütthieved by a new frame structure allowing to remove redundancies and to reconstruct the original stream without loss. The introduced advancement is able to increase the compression efficiency and keeps other beneficial property of the traditional codec, as, high-quality frame-wise access and scalabil作者: 合法 時(shí)間: 2025-3-26 10:43
F. v. Neureiter,F. Pietrusky,E. Schüttos, where the data comes from specific application requirements. The specific real-world application that we are focusing on in this paper is cognitive assessment in children using cognitively demanding physical tasks. We created a system called Cross-Your-Body and recorded data, which is unique in 作者: 交響樂(lè) 時(shí)間: 2025-3-26 16:08
F. v. Neureiter,F. Pietrusky,E. Schütt agent through the neurovasculature is imaged in a series of CT scans. Relevant perfusion parameters, such as cerebral blood volume (CBV), flow (CBF) and delay (Tmax), can be computed by deconvolution of the contrast-time curves with the bolus shape measured at one of the feeding arteries. These par作者: NOMAD 時(shí)間: 2025-3-26 19:22
F. v. Neureiter,F. Pietrusky,E. Schüttsts while increasing efficiency. However, classifying a polyp as being neoplasm or not and segmenting it at the pixel level is still a challenging task for doctors to perform in a limited time. In this work, we propose a fine-grained formulation for the polyp segmentation problem. Our formulation ai作者: 冥界三河 時(shí)間: 2025-3-27 00:08
F. v. Neureiter,F. Pietrusky,E. Schüttso on. With the development of ground-based Adaptive Optics (AO) systems, the imaging quality of optical systems for space targets has been greatly improved. We can estimate the space target pose in these AO images with computer vision algorithms. However, the image degradation during the imaging pr作者: Calibrate 時(shí)間: 2025-3-27 04:24 作者: byline 時(shí)間: 2025-3-27 06:26
F. v. Neureiter,F. Pietrusky,E. Schüttacker (GPAPF), which is an extension of the annealed particle filter tracker and uses Gaussian Process Dynamical Model (GPDM) in order to reduce the dimensionality of the problem, increase the tracker’s stability and learn the motion models. Motion of human body is described by concatenation of low 作者: LASH 時(shí)間: 2025-3-27 09:51
Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen KriminalistikIn Gemeinschaft mit 作者: obeisance 時(shí)間: 2025-3-27 16:58
F. Neureiter (o. Professor Dr. Med. Dr. h. c., Dir作者: evanescent 時(shí)間: 2025-3-27 17:45
Handw?rterbuch der Gerichtlichen Medizin und Naturwissenschaftlichen Kriminalistik978-3-642-51321-3
