A two-level strategy for segmenting center of interest from pictures

Segmenting center of interests (COIs) from pictures is an important but highly challenging problem for researchers in computer vision and image processing. The capability of understanding the meanings of pictures by computers can lead to breakthroughs in a wide range of applications including Web image search and online picture-sharing communities. In this paper, a two-level strategy is presented, which consists of a rough segmentation stage and a fine segmentation stage. In the first level, a picture is partitioned into four regions by using a block clustering method based on color and texture features, and the COI within the picture is distinguished from the background according to the principles of photographic composition. This stage aims to determine the approximate region of the target. In the second level, a novel active contour model is established based on shape information and vector method, where the image energy is defined by a hue gradient and the external energy is generated from either a triangular inner force or a supplementary force. This stage tries to extract the boundary of the target accurately. Experimental results on photos downloaded from the Internet show the feasibility and effectiveness of the proposed method.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

五年制高职动漫专业毕业设计考核评价探究

提升职业教育教学质量是高职院校的核心任务。毕业设计是专业教学的重要环节,它不仅关系到毕业生的质量,也关系到专业的未来发展。从考核评价的角度,对五年制高职动漫专业学生的毕业设计进行可操作性的探讨。     

用于半自动视频对象提取的自适应网格图像分割

随着MPEG－4标准的发展和基于内容的视频处理研究,视频对象平面（VOP）的有效产生成为一个关键问题。本文提出一种基于区域的自适应网格彩色图像分割方法,可用于获得半自动视频对象跟踪和提取所需的初始VOP。该方法利用CIE L＊a*b＊色彩空间的特征量,对视频序列的第一帧进行三角形网格的分裂与合并。对MPEG－4标准测试序列的分割实验取得了较好的结果。     

“和欣”手机的数据完整性技术研究

"和欣"嵌入式操作系统是基于CAR构件技术、支持构件化应用的操作系统,是国家863支持的TD-SCDMA的操作系统标准。本文简要介绍了"和欣"操作系统上的数据库系统SQLite,并着重分析了SQLite的数据库原子性在单文件数据库与多文件数据库中的具体实现过程,并且介绍了错误发生时执行数据恢复的过程。     

A discriminative learning framework with pairwise constraints for video object classification

To deal with the problem of insufficient labeled data in video object classification, one solution is to utilize additional pairwise constraints that indicate the relationship between two examples, i.e., whether these examples belong to the same class or not. In this paper, we propose a discriminative learning approach which can incorporate pairwise constraints into a conventional margin-based learning framework. Different from previous work that usually attempts to learn better distance metrics or estimate the underlying data distribution, the proposed approach can directly model the decision boundary and, thus, require fewer model assumptions. Moreover, the proposed approach can handle both labeled data and pairwise constraints in a unified framework. In this work, we investigate two families of pairwise loss functions, namely, convex and nonconvex pairwise loss functions, and then derive three pairwise learning algorithms by plugging in the hinge loss and the logistic loss functions. The proposed learning algorithms were evaluated using a people identification task on two surveillance video data sets. The experiments demonstrated that the proposed pairwise learning algorithms considerably outperform the baseline classifiers using only labeled data and two other pairwise learning algorithms with the same amount of pairwise constraints.     

溪洛渡水电站拱坝施工期坝基变形监测分析

为了解溪洛渡水电站拱坝坝基在施工期、蓄水初期及运行期不同阶段的变形情况和工作状态,确保大坝安全,同时为给工程建设及运行期管理提供科学依据,对坝基垂直位移和水平位移进行了监测,并对监测资料进行了初步分析.监测结果表明拱坝坝基沉降变形比较均匀,变化规律正常.     

基于农业用水转移背景下的水价改革

水资源供需矛盾突出,产生了农业用水的非农转移。从农业用水转移的理论内涵,阐述了深化水价改革的具体方向和政策措施,指出我国水价管理必须坚持财政、制度、市场三位一体的改革导向。