THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.

/pdf/metrics-for-evaluating-3d-medical-image-segmentation-9r93of3dkp.pdf

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems 参加報告

https://repository.ust.hk/ir/bitstream/1783.1-2663/1/paper_14June2006.pdf

Foot measurements from three-dimensional scans: A comparison and evaluation of different methods

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance. KEYWORDS: Forest flame, Color segmentation, Static feature, Shapematching, Dynamic feature, SVM Language: en

/pdf/svm-based-forest-fire-detection-using-static-and-dynamic-42nxa152xp.pdf

SVM based forest fire detection using static and dynamic features

Footwear comfort is primarily determined by the fit between the foot and the shoe. Foot deformations that cannot be accommodated by the flexibility of the material of the shoe upper have to be dealt with by other adaptive means. The major purpose of this study was to determine foot defor- mations using nine foot dimensions—foot length, arch length, foot width, midfoot width, heel width, midfoot height, medial malleolus height of the most protruding point, lateral malleolus height of the most protruding point and ball girth—as well as the eversion/inversion of the foot while bearing weight. Each foot of 30 Hong Kong Chinese adults (15 males and 15 females) was laser scanned under the conditions of no body-weight (NWB), half body-weight (HWB) and full body-weight (FWB). The nine dimensions and foot rotation were determined with a computer program. The results show that the foot became significantly longer, wider, and reduced in height while everting with increased load- ing on the foot. The midfoot had relatively large changes in width and height when compared to the forefoot and rearfoot. Even though there were no obvious patterns between the left and right foot for both males and females, considerable differences did exist between the two feet of a few participants. Foot length as a percentage of stature changed from 14.94% to 15.10% to 15.13% in males as the load on the foot increased from NWB to HWB to FWB, respectively. These percentages were 14.56%, 14.72%, and 14.77% for females. Even though foot width was significantly correlated with the body weight of males, no such relationship exists in Hong Kong females.

/pdf/foot-deformations-under-different-load-bearing-conditions-vgldm69ck6.pdf

Foot deformations under different load-bearing conditions and their relationships to stature and body weight

Dynamic texture based smoke detection using Surfacelet transform and HMT model

3D tracking and positioning of surgical instruments is an indispensable part of virtual Surgery training system, because it is the unique interface for trainee to communicate with virtual environment. A suit of 3D tracking and positioning of surgical instruments based on stereoscopic vision is proposed. It can capture spatial movements of simulated surgical instrument in real time, and provide 6 degree of freedom information with the absolute error of less than 1 mm. The experimental results show that the 3D tracking and positioning of surgical instruments is highly accurate, easily operated, and inexpensive. Combining with force sensor and embedded acquisition device, this 3D tracking and positioning method can be used as a measurement platform of physical parameters to realize the measurement of soft tissue parameters.

https://www.researchgate.net/profile/Zhaoliang_Duan/publication/220590733_3D_Tracking_and_Positioning_of_Surgical_Instruments_in_Virtual_Surgery_Simulatio/links/0f31753cc8d40978b8000000.pdf

Jianhui Zhao

Papers

Foot measurements from three-dimensional scans: A comparison and evaluation of different methods

SVM based forest fire detection using static and dynamic features

Foot deformations under different load-bearing conditions and their relationships to stature and body weight

Dynamic texture based smoke detection using Surfacelet transform and HMT model

3D Tracking and Positioning of Surgical Instruments in Virtual Surgery Simulation