Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still...Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.展开更多
In this paper, a new predictive model, adapted to QTM (Quaternary Triangular Mesh) pixel compression, is introduced. Our approach starts with the principles of proposed predictive models based on available QTM neighbo...In this paper, a new predictive model, adapted to QTM (Quaternary Triangular Mesh) pixel compression, is introduced. Our approach starts with the principles of proposed predictive models based on available QTM neighbor pixels. An algorithm of ascertaining available QTM neighbors is also proposed. Then, the method for reducing space complexities in the procedure of predicting QTM pixel values is presented. Next, the structure for storing compressed QTM pixel is proposed. In the end, the experiment on comparing compression ratio of this method with other methods is carried out by using three wave bands data of 1 km resolution of NOAA images in China. The results indicate that: 1) the compression method performs better than any other, such as Run Length Coding, Arithmetic Coding, Huffman Cod- ing, etc; 2) the average size of compressed three wave band data based on the neighbor QTM pixel predictive model is 31.58% of the origin space requirements and 67.5% of Arithmetic Coding without predictive model.展开更多
For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed co...For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.展开更多
Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and localization.I...Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and localization.In this paper,we study the synchrosqueezed transform(SST)algorithm and the texture color distribution(TCD)based HVI source identification and localization using impact images.The extracted SST and TCD image features are fused for HVI image representation.To achieve more accurate detection and localization,the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features.Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization.To demonstrate the effectiveness of the proposed algorithm,the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation.The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms.Finally,based on sensor distribution,an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.展开更多
基金supported by National Basic Research Project of China(2013CB329006)National Natural Science Foundation of China(No.61622110,No.61471220,No.91538107)
文摘Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.
基金Project 40471108 supported by the National Natural Science Foundation of China
文摘In this paper, a new predictive model, adapted to QTM (Quaternary Triangular Mesh) pixel compression, is introduced. Our approach starts with the principles of proposed predictive models based on available QTM neighbor pixels. An algorithm of ascertaining available QTM neighbors is also proposed. Then, the method for reducing space complexities in the procedure of predicting QTM pixel values is presented. Next, the structure for storing compressed QTM pixel is proposed. In the end, the experiment on comparing compression ratio of this method with other methods is carried out by using three wave bands data of 1 km resolution of NOAA images in China. The results indicate that: 1) the compression method performs better than any other, such as Run Length Coding, Arithmetic Coding, Huffman Cod- ing, etc; 2) the average size of compressed three wave band data based on the neighbor QTM pixel predictive model is 31.58% of the origin space requirements and 67.5% of Arithmetic Coding without predictive model.
基金the Natural Science Foundation of Beijing(4172026)Capability Innovation Project of Beijing Academy of Agriculture and Forestry(KJCX20170706).
文摘For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.
基金Project supported by the National Natural Science Foundation of China(Nos.U1909209 and 61503104)the Open Foundation of Hypervelocity Impact Research Center of China Aerodynamics Research and Development Centerthe Research Start-up Funding,China(No.2019RC020)。
文摘Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and localization.In this paper,we study the synchrosqueezed transform(SST)algorithm and the texture color distribution(TCD)based HVI source identification and localization using impact images.The extracted SST and TCD image features are fused for HVI image representation.To achieve more accurate detection and localization,the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features.Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization.To demonstrate the effectiveness of the proposed algorithm,the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation.The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms.Finally,based on sensor distribution,an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.