A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transf...A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.展开更多
To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy ...To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.展开更多
目的 比较韶音后挂式骨导助听器对不同类型听力损失患者的听力干预短期效果,探讨其临床应用前景。方法 55例听力损失患者(年龄18~82岁;传导性听力损失9例,感音神经性听力损失15例,混合性听力损失31例;左右耳0.5、1、2、4 kHz四个频率的...目的 比较韶音后挂式骨导助听器对不同类型听力损失患者的听力干预短期效果,探讨其临床应用前景。方法 55例听力损失患者(年龄18~82岁;传导性听力损失9例,感音神经性听力损失15例,混合性听力损失31例;左右耳0.5、1、2、4 kHz四个频率的骨导纯音听阈均≤60 dB HL)配戴韶音后挂式骨导助听器,分别于配戴助听器前和配戴第14±2 d行声场总体听阈、单音节识别率及安静环境语句识别阈测试,比较配戴助听器前后的结果差异。并于配戴第14±2 d使用IOI-HA问卷对助听器使用效果进行评估。结果 患者配戴后挂式骨导式助听器后声场四个频率平均听阈(39.3±4.9 dB HL)较配戴前(56.5±8.2 dB HL)显著改善,差异有统计学意义(P<0.001)。患者助听前单音节识别率(给声强度:患者助听前双音节言语识别阈减5 dB)为29.8%±11.4%,配戴第14±2 d为72.4%±14.4%,配戴后单音节识别率显著提高,差异有统计学意义(P<0.001)。患者语句识别阈由配戴前的48.6±9.7 dB HL降至34.3±5.6 dB HL,差异有统计学意义(P<0.001)。配戴14±2 d时IOI-HA问卷评估总分平均值为29.0±3.8分。结论 后挂式骨导助听器可显著提高传导性、0.5~4 kHz骨导纯音听阈不超过60 dB HL的混合性及感音神经性听力损失患者的听力及言语识别能力。展开更多
文摘A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.
文摘To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.
基金Supported by National Natural Science Foundation ot China（60572100, 60673122）, Royal Society （U.K.） International Joint Projects 2006/R3-Cost Share with NSFC （60711130233）, Science Foundation of Shenzhen City （CXQ2008019, 200706）, and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry （2008[890]）.
文摘目的 比较韶音后挂式骨导助听器对不同类型听力损失患者的听力干预短期效果,探讨其临床应用前景。方法 55例听力损失患者(年龄18~82岁;传导性听力损失9例,感音神经性听力损失15例,混合性听力损失31例;左右耳0.5、1、2、4 kHz四个频率的骨导纯音听阈均≤60 dB HL)配戴韶音后挂式骨导助听器,分别于配戴助听器前和配戴第14±2 d行声场总体听阈、单音节识别率及安静环境语句识别阈测试,比较配戴助听器前后的结果差异。并于配戴第14±2 d使用IOI-HA问卷对助听器使用效果进行评估。结果 患者配戴后挂式骨导式助听器后声场四个频率平均听阈(39.3±4.9 dB HL)较配戴前(56.5±8.2 dB HL)显著改善,差异有统计学意义(P<0.001)。患者助听前单音节识别率(给声强度:患者助听前双音节言语识别阈减5 dB)为29.8%±11.4%,配戴第14±2 d为72.4%±14.4%,配戴后单音节识别率显著提高,差异有统计学意义(P<0.001)。患者语句识别阈由配戴前的48.6±9.7 dB HL降至34.3±5.6 dB HL,差异有统计学意义(P<0.001)。配戴14±2 d时IOI-HA问卷评估总分平均值为29.0±3.8分。结论 后挂式骨导助听器可显著提高传导性、0.5~4 kHz骨导纯音听阈不超过60 dB HL的混合性及感音神经性听力损失患者的听力及言语识别能力。
文摘针对目前智能机器人领域中,利用多帧连续视觉和触觉信息时,对时空信息和模态间的异构信息处理不足的问题,提出了一种结合时空注意力的视触融合目标识别方法。该方法利用Swin Transformer模块从视觉和触觉图像中分别提取特征,减轻模态间的异构性;使用基于注意力瓶颈机制的时空Transformer模块,实现视觉和触觉特征信息的时空交互和跨模态交互;通过多头自注意力融合模块,实现视触觉特征中信息的自适应聚合,提高了算法对目标识别的准确性;通过全连接层获得目标识别的结果。该模型在The Touch and Go公共数据集上的精确率和F1分数分别为98.38%和96.83%,比效果最好的对比模型提高了0.90和0.63个百分点。此外,消融实验也验证了提出的各个模块的有效性。
文摘近年来,随着羊只养殖向大规模和精细化的方向发展,羊场对智能化管理的需求日益增加。因此,精准的个体识别和行为监测变得尤为重要,对多目标跟踪(Multiple object tracking, MOT)算法的准确性提出了更高要求。然而,现有的MOT算法在目标遮挡和动态场景下的性能仍不理想。本文提出两种跟踪线索:深度调制交并比(Depth modulated intersection over union, DIoU)和轨迹方向建模(Tracklet direction modeling, TDM),旨在补充交并比(Intersection over union, IoU)线索,提高多目标跟踪的精准度和鲁棒性。DIoU线索通过引入目标的深度信息改进了传统的IoU计算方法。TDM聚焦于目标的运动趋势,预测其未来的移动方向。本文将DIoU和TDM跟踪线索集成到BoT-SORT算法中,形成改进的多目标跟踪算法。在两个私有数据集上,改进算法相比基线方法,MOTA(Multiple object tracking accuracy)指标分别提高1.6、1.7个百分点,IDF1(Identification F1 score)指标分别提高1.9、1.0个百分点。结果显示,改进算法在复杂场景中的跟踪连续性和准确性显著提升。
