共查询到19条相似文献,搜索用时 78 毫秒
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手势识别是图像处理中一个主要研究方向,Hu矩由于其良好的不变性被广泛地应用在二值图像中进行图像识别.文中将传统的Hu矩引入到彩色图像中,经过大量的实验,得出了Hu矩在彩色图像中的特性,提出了基于Hu矩的图像旋转识别法,应用在彩色图像的手势识别中. 相似文献
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文章提出一种基于Contourlet 变换和Hu 不变矩的图像检索算法。首先,对每幅图像进行Contourlet 变换,得到低频子带与高频方向子带,把计算得到的低频子带的Hu 不变矩和各个高频方向子带的均值与标准差作为图像的特征向量,利用Manhattan距离进行相似度度量,完成基于内容的图像检索。为对该文提出的算法的检索效果进行检验,分别与基于Contourlet 变换特征的检索算法和基于Hu不变矩特征的检索算法等方法进行了对比实验研究。结果表明,该算法有效地融合了图像的纹理特征与低频子带的形状特征,较好地实现了基于内容的图像检索,平均查准率达到73.94%。 相似文献
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为了实现液晶屏字符高效、精准的自动化检测,针对液晶屏字符显示的断线、亮度缺陷,提出一种基于多特征矩融合的液晶屏字符缺陷检测算法。首先提取原始液晶屏图像中ROI区域,使用Hu不变矩来描述字符的结构特征,采用Zernike矩来弥补Hu矩所不能描述的高阶矩信息;然后使用颜色矩来描述字符的颜色特征,采用灰度矩弥补颜色矩所不能描述的灰度信息,并运用2DPCA技术融合上述矩阵,通过欧氏距离来衡量标准图像融合特征矩阵与待检测图像融合特征矩阵之间的相似度,通过设定相似度阈值来达到缺陷检测的目的。实验的客观和主观评价结果表明:主观上,该算法具有一定的稳定性、实用性;客观上,该算法相比于同类算法具有较低的误判率1%、漏判率0%以及较高的效率0.6 s,基本满足实际检测需求。 相似文献
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《电子技术与软件工程》2016,(4)
基于SIFT和Hu特征融合的单目视觉识别算法是一种融合了局部特征和整体特征的不变矩融合算法。通过基于SIFT和Hu特征融合的单目视觉识别算法能够对三维物体图像的整体信息进行定义和把握,进而实现对物体的合理定位分析。另外,局部特征能够辅助全局特征对三维物体的特征进行更准去的定位和匹配。这种算法具有良好的伸缩能力、旋转能力、位移能力和抵抗能力,能够有效解决三维物体的匹配问题,同时提升系统的识别能力和工作效率,基于此,文章对SIFT和Hu特征融合的单目视觉识别算法进行研究。 相似文献
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提出一种基于伪随机码加权的天线阵接收多波束自适应形成新方法。阐述了其工作原理,并以直线阵为例建立了波达方向估计和接收多波束形成算法,给出了伪随机码的选择和使用原则,通过仿真验证了其有效性和灵活性。该方法无需计算加权矢量,只需相关运算即可形成接收多波束,大大提高了波束形成效率,降低了接收机硬件成本,具有较高的工程应用价值。 相似文献
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针对单一特征无法表征铭文全部信息的问题,提出一种基于全局Hu矩和局部聚类加权加速鲁棒特征(TF-KSURF)的多测度青铜器铭文相似性度量方法.通过提取Hu矩特征描述子与加速鲁棒特征(SURF)矩阵,获取铭文图像的全局与局部特征;利用K-means算法和加权策略对局部SURF进行聚类加权,构建TF KSURF向量;最后设... 相似文献
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In recent years, artificial intelligence has been widely used in such fields as agricultural informatization, precision agriculture and precision animal husbandry. Due to limited research on deep learning in real-time agricultural and pastoral situations, deep learning and computer vision have become very important topics in the agricultural field. Recent studies have shown that the fusion of features under different attention mechanisms will help advance the utilization of such features, and will thus influence the accuracy and generalization ability of the models used. In this paper, we propose a lightweight network structure based on feature fusion under a dual attention mechanism with the same activation and joint loss functions. More specifically, we propose an innovative method to improve the network structure of two different attention mechanisms, and achieve feature fusion by combining the two. At the same time, we keep the activation functions consistent with those of the original network structure, and we develop a joint loss function to expand the use of various features. We also take the novel approach of applying the trajectory behavior analysis method to walking and standing. Experiments using both a publicly available data set and a data set obtained from a farm show that our algorithm achieves state-of-the-art performance in terms of accuracy and generalization ability, as compared to other methods. 相似文献
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提出了一种噪声功率谱估计算法,该算法对加权后的带噪语音进行递归平滑,可以持续更新噪声并可应用于非平稳噪声环境中。为了避免在强语音后的弱语音区域出现噪声过估计,本文提出了用于计算加权函数的投影平滑算法。本文噪声估计算法可以快速跟踪噪声的变化并且没有过估计。实验结果表明,本文噪声估计算法应用于一个语音增强系统时,取得了较小的噪声分段估计误差及较好的感知语音质量评价(PESQ)得分。 相似文献
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基于总错误率和特征关联的自适应融合多模态生物特征识别 总被引:1,自引:1,他引:0
针对单生物特征识别准确率和鲁棒性差的问题, 提出了一种基于总错误率(TER)和特征关联自适应融合多模态生物特 征识别方法。首先将TER作为判别特征引入到多模态识别,以代替传统的匹配分 数;其次在不确定度量理论的基 础上,考虑人脸特征和语音特征之间的时空关联性,提出了一种基于特征关联的多特征 自适应融合策略,利用特征关联 系数自适应调节不同识别特征对识别结果的贡献。仿真实验表明,与几种代表性的融合算法 相比,本文所 提出的融合模式可以有效提高多生物特征识别系统的准确性和鲁棒性。 相似文献
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在动作识别任务中,如何充分学习和利用视频的空间特征和时序特征的相关性,对最终识别结果尤为重要。针对传统动作识别方法忽略时空特征相关性及细小特征,导致识别精度下降的问题,本文提出了一种基于卷积门控循环单元(convolutional GRU, ConvGRU)和注意力特征融合(attentional feature fusion,AFF) 的人体动作识别方法。首先,使用Xception网络获取视频帧的空间特征提取网络,并引入时空激励(spatial-temporal excitation,STE) 模块和通道激励(channel excitation,CE) 模块,获取空间特征的同时加强时序动作的建模能力。此外,将传统的长短时记忆网络(long short term memory, LSTM)网络替换为ConvGRU网络,在提取时序特征的同时,利用卷积进一步挖掘视频帧的空间特征。最后,对输出分类器进行改进,引入基于改进的多尺度通道注意力的特征融合(MCAM-AFF)模块,加强对细小特征的识别能力,提升模型的准确率。实验结果表明:在UCF101数据集和HMDB51数据集上分别达到了95.66%和69.82%的识别准确率。该算法获取了更加完整的时空特征,与当前主流模型相比更具优越性。 相似文献
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Considering the inherent characteristics of incomplete fingerprint: local feature loss and global information distortion, the recognition progress has been mainly restricted by two critical problems: how to precisely extract informative features and still with compact representation of the incomplete fingerprint; and how to effectively measure the similarity between fingerprint images. In this paper, to handle the first problem, both the minutiae and orientation field feature are extracted and then fused to get a more comprehensive feature with scale and rotation invariability. Dealing with the second one, the pattern entropy is introduced to robustly measure the similarity of two incomplete fingerprints. Extensive experiments have been conducted on both those popular fingerprint databases and our extended databases containing more incomplete fingerprints. Meanwhile, thorough performance comparisons have been made with existing approaches. Experimental results show that our approach has more efficient ability especially in incomplete fingerprint recognition, and also performs well in both accuracy and efficiency. 相似文献
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To extract decisive features from gesture images and solve the problem of information redundancy in the existing gesture recognition methods, we propose a new multi-scale feature extraction module named densely connected Res2Net (DC-Res2Net) and design a feature fusion attention module (FFA). Firstly, based on the new dimension residual network (Res2Net), the DC-Res2Net uses channel grouping to extract fine-grained multi-scale features, and dense connection has been adopted to extract stronger features of different scales. Then, we apply a selective kernel network (SK-Net) to enhance the representation of effective features. Afterwards, the FFA has been designed to remove redundant information in features by fusing low-level location features with high-level semantic features. Finally, experiments have been conducted to validate our method on the OUHANDS, ASL, and NUS-II datasets. The results demonstrate the superiority of DC-Res2Net and FFA, which can extract more decisive features and remove redundant information while ensuring high recognition accuracy and low computational complexity. 相似文献