视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

基于密度聚类与SVM的非法广播信号识别研究

针对非法广播信号的危害,以及传统人工检测效率低的问题,提出一种基于密度聚类与SVM的信号识别模型。首先,采用标准欧式距离对特征信号进行提取;其次,以聚类样本为基础,采用SVM分类器对信号分类;最后,以青海广播电视局中波台整点时刻前后300帧的数据为样本,以静音信号作为评价指标,对信号进行识别。结果表明,在正常信号中加入非法信号后,频谱中有少量的静音信号,且SVM训练时间和识别正确率都要优于传统算法。     

Younger and older users׳ recognition of virtual agent facial expressions

As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent׳s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell et al., 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck and Reichenbach, 2005, Courgeon et al., 2009, Courgeon et al., 2011, Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent׳s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition.     

A hand gesture recognition technique for human–computer interaction

We propose an approach to recognize trajectory-based dynamic hand gestures in real time for human–computer interaction (HCI). We also introduce a fast learning mechanism that does not require extensive training data to teach gestures to the system. We use a six-degrees-of-freedom position tracker to collect trajectory data and represent gestures as an ordered sequence of directional movements in 2D. In the learning phase, sample gesture data is filtered and processed to create gesture recognizers, which are basically finite-state machine sequence recognizers. We achieve online gesture recognition by these recognizers without needing to specify gesture start and end positions. The results of the conducted user study show that the proposed method is very promising in terms of gesture detection and recognition performance (73% accuracy) in a stream of motion. Additionally, the assessment of the user attitude survey denotes that the gestural interface is very useful and satisfactory. One of the novel parts of the proposed approach is that it gives users the freedom to create gesture commands according to their preferences for selected tasks. Thus, the presented gesture recognition approach makes the HCI process more intuitive and user specific.     

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Frontiers of Mechanical Engineering - Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and...     

Engineering of Lipid Nanoparticles by the Multifunctionalization of the Surface with Amino Acid Derivatives for the Neutralization of a Target Toxic Peptide

Protein affinity reagents (e.g., antibodies) are often used for basic research, diagnostics, separations, and disease therapy. Although a lot of “synthetic” protein affinity reagents have been developed as a cost-effective alternative to antibodies, their low biocompatibility is a considerable problem for clinical application. Lipid nanoparticles (LNP) represent a highly biocompatible drug delivery agent. However, little has been reported that LNP itself works as a protein affinity reagent in living animals. Here, LNP is engineered for binding to and neutralizing a target toxic peptide in living animals by multifunctionalization with amino acid derivatives. Multifunctionalized LNP (MF-LNP) is prepared using amino acid derivative-conjugated lipids. Optimized MF-LNP exhibits nanomolar affinity to the target toxic peptide and inhibits toxic peptide-dependent hemolysis and cytotoxicity. In addition, MF-LNP captures and neutralizes the toxic peptide after intravenous injection in the bloodstream; in addition, MF-LNP does not release the toxic peptide in the accumulated organ. These results reveal the potential of using LNP as a highly biocompatible protein affinity reagent such as an antidote.     

Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

结构化加权稀疏低秩恢复算法在人脸识别中的应用

针对训练样本或测试样本存在污损的情况，提出一种结构化加权稀疏低秩恢复算法（structured and weighted-sparse low rank representation，SWLRR）。SWLRR对低秩表示进行加权稀疏约束和结构化约束，使得低秩表示系数更加趋近于块对角结构，进而可获得具有判别性的低秩表示。SWLRR将训练样本恢复成干净训练样本后，再根据原始训练样本和恢复后的训练样本学习到低秩投影矩阵，把测试样本投影到相应的低秩子空间，即可有效地去除测试样本中的污损部分。在几个人脸数据库上的实验结果验证了SWLRR在不同情况下的有效性和鲁棒性。