Titlebook: Hip Biomechanics; Shinichi Imura,Noriya Akamatsu,Seisuke Tanaka Book 1993 Springer-Verlag Berlin Heidelberg 1993 Arthrose.Coxarthrose.Oste

dapper

ment acquired by a certain ToF camera and corresponding dense ground truth depth. Experimental results demonstrate the superior performance of the proposed iterative method in removing various ToF depth errors, compared to state-of-the-art methods, on both the newly developed datasets and existing p

继而发生

Yutaka Ohneda,Kenji Kawate,Susumu Tamaite matrix multiplication in parallel on a multi-core processor, achieving high computational efficiency. Furthermore, we use a double buffer mechanism to optimize data transfer and shorten execution time. Overall, using MobileNet to evaluate depthwise separable convolution, multi-vector parallel con

易发怒

Mitsuo Suzuki,Nobutoshi Yamazakie the architecture of the deep model for better hierachical classification and adjust the hierarchical evaluation metrics for multiple label structures. Experimental results on CIFAR100 and Car196 show that our method obtains significantly better results than using a flat classifier or a hierarchica

Carcinogen

破译

FEAT

Makoto Okuno,Kichizo Yamamoto,Ryota Teshima,Tetsuya Otsuka,Noriyuki Takasu edge loss together to train our GLUNet. Experimental results demonstrate that our proposed method outperforms the original U-Net method and other state-of-the-art methods for lung segmentation in Computed Tomography (CT) images, cell/nuclei segmentation and vessel segmentation in retinal images.

excursion

gene-therapy

or further improvements. SiamSNN is the first deep SNN tracker that achieves short latency and low precision loss on the visual object tracking benchmarks OTB2013/2015, VOT2016/2018, and GOT-10k. Moreover, SiamSNN achieves notably low energy consumption and real-time on Neuromorphic chip TrueNorth.

合唱队

https://doi.org/10.1007/978-4-431-68237-0Arthrose; Coxarthrose; Osteotom; arthroplasty; biomaterial; biomechanics; computed tomography; computed tom