共查询到20条相似文献,搜索用时 62 毫秒
1.
2.
本文首先介绍了基于窄带信号的空气动力目标识别的几种方法,着重讨论了目标回波的调制谱,并对喷气式飞机和旋翼飞机的调制谱进行了介绍,对调制谱特征中的的谱线间隔、谱线宽度、谱线个数等特征分量进行了介绍,确定利用目标回波调制谱中谱线间隔对目标类型进行识别,通过实测数据,验证了这一方法的可行性。 相似文献
3.
目标噪声特征提取是被动声纳目标识别系统的关键技术。首先提出了一种利用从噪声极限环中提取的非线性特征来分析舰船噪声信号的新方法,然后采用基于自适应遗传BP算法的神经网络对提取的特征进行分类。实验结果表明,该系统具有较好的分类效果。 相似文献
4.
针对传统人脸图像纹理特征识别方法中存在的计算量大,样本训练与测试时间较长,识别正确率较低等问题,提出一种新的基于PCA模型的人脸图像纹理特征高精度识别方法。在人脸图像预处理过程中,采用Gabor滤波器确定人脸图像训练样本中的双眼位置,结合卷积运算与人脸几何模型从图像中分割出目标人脸区域,并对分割得到的图像进行规范化处理;采用PCA模型对预处理后的图像进行降维与特征向量提取,并根据选取的主要纹理特征以及欧式距离近似度量结果,实现人脸图像纹理特征高精度识别。实验结果表明,所提方法的识别准确度高于实验对比方法,且样本训练时间与测试时间明显缩短,具有较好的鲁棒性。 相似文献
6.
针对基于单一的节点或骨架模型变形方法会产生局部扭曲现象的问题,提出了一种基于图形边界提取与基于边界变形相结合的方法。在手动指定变形定位点后,根据源图像与指定图像中变形点的对应,将源图像中的图形按照变形点变换到指定图像中。实验结果表明,该方法快速、灵活、有效,且变形后的目标对象具有光滑、自然的特点。 相似文献
7.
8.
为了提升目标物体的跟踪性能,分析了跟踪系统的不足之处,针对多变场景中跟踪目标特征弱化问题,采用了多特征融合的跟踪模式,并设计了场景识别、多特征融合策略和特征学习模块以改善目标跟踪系统的跟踪性能,实验表明该方法对多变场景的跟踪性能具有一定的改善作用。 相似文献
9.
面部识别被认为是生物特征识别领域甚至人工智能领域最困难的研究课题之一,如何进行好面部识别工作对于生活、生产都有着十分重要的意义。本文提出一种基于特征点提取的面部识别方法,在得到已知图像和待识别图像以后,先对其进行边缘提取和点特征提取,然后基于提取的特征点进行灰度模板匹配,通过图像匹配的结果对图像进行识别。 相似文献
10.
基于模体的科学家合作网络基元特征分析 总被引:2,自引:0,他引:2
基于复杂网络构建科学家合作网络并分析其全局结构特征,是理解科学家间合作机制的重要手段和研究热点。实际上,辨识其局部结构可以进一步增进对合作机制的理解。基于复杂网络局部结构研究的前沿理念和方法——模体,构建并辨析若干大型科学家合作网络的基元特征:模体特性、子图浓度以及子图自下而上的组合机制。研究表明不同领域科学家合作网络的模体与反模体形式具有共性特征,网络的子图相对浓度则因不同领域科学家之间合作强度及网络发展程度差异而具有不同的分布特征。此外,科学家合作网络具有相似的自下而上构建机制。研究提供了基于模体的科学家合作网络结构辨识方法。 相似文献
11.
基于认知科学的研究提出一个新颖的计算模型用于物体识别.特征整合理论为计算模型提供了总体路线.基于最大熵原理构建学习过程,获得必要的先验知识构成认知网络.利用认知网络,将底层的图像特征和高层知识捆绑起来.利用条件随机场的基本概念和原理建模捆绑过程.将计算模型应用于现实世界的物体识别,在标准图像库上进行评估,取得了很好的效果. 相似文献
12.
13.
为了将协方差矩阵算法应用于自动目标检测,提出了特征相似度和协方差矩阵相似度.特征相似度是目标特征的相似程度,协方差矩阵相似度融合各个特征相似度.另外,鉴于特征具有不同的有效性和重要性,提出了最小特征相似度.最小相似度可以用于剔除基本无效的特征.通过实验证明,本方法能有效地将协方差矩阵算法应用于自动目标检测,具有较高的准确率. 相似文献
14.
15.
《Information processing & management》2022,59(3):102863
Few-shot intent recognition aims to identify user’s intent from the utterance with limited training data. A considerable number of existing methods mainly rely on the generic knowledge acquired on the base classes to identify the novel classes. Such methods typically ignore the characteristics of each meta task itself, resulting in the inability to make full use of limited given samples when classifying unseen classes. To deal with such issues, we propose a Contrastive learning-based Task Adaptation model (CTA) for few-shot intent recognition. In detail, we leverage contrastive learning to help achieve task adaptation and make full use of the limited samples of novel classes. First, a self-attention layer is employed in the task adaptation module, which aims to establish interactions between samples of different categories so that new representations are task-specific rather than relying entirely on the base classes. Then, the contrastive-based loss functions and the semantics of the label name are respectively used for reducing the similarity between sample representations in different categories while increasing it in the same categories. Experimental results on a public dataset OOS verify the effectiveness of our proposal by beating the competitive baselines in terms of accuracy. Besides, we conduct the cross-domain experiments on three datasets, i.e., OOS, SNIPS as well as ATIS. We find that CTA gains obvious improvements in terms of accuracy in all cross-domain experiments, indicating that it has a better generalization ability than other competitive baselines in both cross-domain and single-domain settings. 相似文献
16.
电子计算机广泛应用于信息处理中,有极强的算术和逻辑运算能力,有极高的运算速度、精确度和可靠度。但是,它的形象思维能力与人相距甚远。如果计算机具备了模式识别能力,人们就可以使用机器来执行感知任务。文章运用了人工神经网络,模式识别的方法及原理,以Matlab软件作为平台来探讨应用神经网络对汉字进行识别。并通过对汉字样本图象采集输入,汉字图象二值化,行字切分,十进制存储等预处理,分别在有、无干扰的情况下对汉字进行识别,从而评价其性能的优劣。 相似文献
17.
18.
《Information processing & management》2022,59(6):103101
On-shelf book segmentation and recognition are crucial steps in library inventory management and daily operation. In this paper, a detailed investigation of related work is conducted. RFID and barcode-based solutions suffer from expensive hardware facilities and long-term maintenance. Digital Image processing and OCR techniques are flawed due to a lack of accuracy and robustness. On this basis, we propose a visual and non-character system utilizing deep learning methods to accomplish on-shelf book segmentation and recognition tasks. Firstly, book spine masks are extracted from the image of on-shelf books by instance segmentation model, followed by affine transformation to rectangle images. Secondly, a spine feature encoder is trained to learn the deep visual features of spine images. Finally, the book inventory search space is constructed and the similarity metric between spine visual representations is calculated to recognize the target book identity. To train the models we collect high-resolution datasets of 10k-level and develop a data annotation software accordingly. For validation, we design simulated scenarios of recognizing 3.6k IDs from 5.6k book spines and achieve a best top1 accuracy of 99.18% and top5 accuracy of 99.91%. Furthermore, we develop a prototype of a mobile library management robot with embedded edge intelligence. It can automatically perform on-shelf book image capturing, spine segmentation and recognition, and target book grasping workflow. 相似文献
19.
Teacher-directed learning in view-independent face recognition with mixture of experts using single-view eigenspaces 总被引:1,自引:0,他引:1
We propose a new model for view-independent face recognition by multiview approach. We use the so-called “mixture of experts”, ME, in which, the problem space is divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our model, instead of leaving the ME to partition the face space automatically, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. To force an expert towards a specific view of face, in the representation layer, we provide each expert with its own eigenspace computed from the faces in the corresponding view. Furthermore, we use teacher-directed learning, TDL, in a way that according to the pose of the input training sample, only the weights of the corresponding expert are updated. The experimental results support our claim that directing the experts to a predetermined partitioning of face space improves the performance of the conventional ME for view-independent face recognition. In particular, for 1200 images of unseen intermediate views of faces from 20 subjects, the ME with single-view eigenspaces yields the average recognition rate of 80.51% in 10 trials, which is noticeably increased to 90.29% by applying the TDL method. 相似文献
20.
针对蠕虫仿真中大量小型对象的特点,对GTNetS的内存管理机制进行系统研究,提出小型对象内存分配技术.实验结果表明,小型对象分配技术可以有效减少系统的最大内存使用量,进而提高蠕虫仿真的规模和效率. 相似文献