ID 原文 译文
23895 频谱域光学相干层析技术是一种广泛应用于眼科疾病诊断的成像技术，而视网膜层分割对青光眼的诊断有很好的参考价值。 Spectral Domain Optical Coherence Tomography (SD-OCT) imaging technique is widely used in the diagnosis of ophthalmology diseases. The segmentation of retinal layers plays a very important role in the diagnosis of glaucoma.
23896 该文利用随机森林分类器寻找视网膜层间单像素宽的边界，随机森林分类器由 12 个特征训练产生，其中相对灰度特征和邻域特征较好地解决灰度不均匀的分割误差大问题。 In this paper, a random forest classifier is used which is trained by twelve different features to find the boundaries between layers. What's more, the relative gray feature and the neighbor features are used to solve the problem of large errors under the condition of uneven illumination.
23897 对 10 组带有青光眼病变的视网膜图像进行分割，并与传统算法和 Iowa 软件进行比较，平均边界绝对误差为 9.20±2.57 μm, 11.33±2.99 μm 和 10.27±3.01 μm。 In the last, the segmentation results of the proposed algorithm, a traditional algorithm and Iowa segmentation software on ten sets of retinal images are compared with manual segmentation, and the average absolute boundary errors are 9.20±2.57 μm, 11.33±2.99 μm, 10.27±3.01 μm, respectively.
23898 实验结果表明，改进算法可以较好地分割视网膜神经纤维层。 The experiments show that the proposed algorithm can segment the Retinal Never Fiber Layer (RNFL) better.
23899 视觉跟踪中，高效鲁棒的特征表达是解决复杂环境下跟踪漂移问题的关键。 In visual tracking, the efficient and robust feature representation is the key factor to solve the problem of tracking drift in complex environments.
23900 该文针对深层网络预训练复杂费时及单网络跟踪易漂移的问题，在粒子滤波框架下，提出一种基于自适应深度稀疏网络的在线跟踪算法。 Therefore, to solve the problems of the complex and time-consuming of the pre-training process of deep neural network and the drift of the single network tracking, an online tracking method based on an adaptive deep sparse network is proposed under the tracking structure of particle filter.
23901 该算法利用 ReLU 激活函数，针对不同类型目标构建了一种具有自适应选择性的深度稀疏网络结构，仅通过有限标签样本的在线训练，就可得到鲁棒的跟踪网络。 A deep sparse neural network architecture, which can be adaptively selected according to different types of targets, is constructed with the implementation of the Rectifier Linear Unit (ReLU) activation function. The robustness of deep tracking network can be easily achieved only through the online training of limited labeled samples.
23902 实验数据表明：与当前主流的跟踪算法相比，该算法的平均跟踪成功率和精度均为最好，且与同样基于深度学习的 DLT 算法相比分别提高了 20.64%和 17.72%。 The results of experiments show that, compared with the state-of-the-art tracking algorithm, the average success ratio and precision of the proposed algorithm are both the highest, and they are raised by 20.64% and 17.72% respectively contrasted with the Deep Learning Tracker (DLT) algorithm based on deep learning.
23903 在光照变化、相似背景等复杂环境下，该算法表现出了良好的鲁棒性，能够有效地解决跟踪漂移问题。 The proposed method can solve the problems of tracking drift efficiently, and shows better robustness, especially for the complex environment such as illumination changes, background clutter and so on.
23904 为了解决高分辨率遥感影像中相同地物目标异质性和空间破碎性增大及不同地物目标的相似性增强所带来的分割新问题，该文提出一种融入空间关系的高斯混合模型(GMM)高分辨遥感影像监督分割方法。 This paper proposes a supervised image segmentation algorithm for high resolution remote sensing images by introducing the Gaussian Mixture Model (GMM) with spatial relationship in order to solve the problem of the increasing dissimilarity in the same object and the decreasing of dissimilarity between two different objects.