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
A non-specific symptom of one or more physical, or psychological processes in which screaming, shouting, complaining, moaning, cursing, pacing, fidgeting or wandering pose risk or discomfort, become disruptive or unsa...A non-specific symptom of one or more physical, or psychological processes in which screaming, shouting, complaining, moaning, cursing, pacing, fidgeting or wandering pose risk or discomfort, become disruptive or unsafe or interfere with the delivery of care are called agitation. Individuals in agitation manifest their condition through ＂pain behavior＂, which includes facial expressions. Clinicians regard the patient＇s facial expression as a valid indicator for pain and pain intensity. Hence, correct interpretation of the facial agitation of the patient and its correlation with pain is a fundamental step in designing an automated pain assessment system. Computer vision techniques can be used to quantify agitation in sedated patients in Intensive Care Unit （ICU）. In particular, such techniques can be used to develop objective agitation measurements from patient motion. In the case of paraplegic patients, whole body movement is not available, and hence, monitoring the whole body motion is not a viable solution. Hence in this case, the author measured head motion and facial grimacing for quantifying facial patient agitation in critical care based on Fuzzy k-NN.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.
文摘A non-specific symptom of one or more physical, or psychological processes in which screaming, shouting, complaining, moaning, cursing, pacing, fidgeting or wandering pose risk or discomfort, become disruptive or unsafe or interfere with the delivery of care are called agitation. Individuals in agitation manifest their condition through ＂pain behavior＂, which includes facial expressions. Clinicians regard the patient＇s facial expression as a valid indicator for pain and pain intensity. Hence, correct interpretation of the facial agitation of the patient and its correlation with pain is a fundamental step in designing an automated pain assessment system. Computer vision techniques can be used to quantify agitation in sedated patients in Intensive Care Unit （ICU）. In particular, such techniques can be used to develop objective agitation measurements from patient motion. In the case of paraplegic patients, whole body movement is not available, and hence, monitoring the whole body motion is not a viable solution. Hence in this case, the author measured head motion and facial grimacing for quantifying facial patient agitation in critical care based on Fuzzy k-NN.
