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Motivated by increased use in practice and increased interest in the media and among academics, a performance evaluation system was built based on Economic Value Added (EVA) and Balanced Scorecard (BSC) for logistics enterprises. Correlation test and linear regression were run to give empirical support for the system. Leading the long-term vision, EVA is at the top of this system. Other measures distributed...
Motivated by the increased use and interest, we examine the value relevance of economic value added (EVA) based on sample of Chinese logistics. Empirical evidence supports significant incremental information content of EVA compared with traditional performance measures, but the incremental information is not sufficient enough to support EVA dominating them. Nevertheless, the significant coefficients...
Joint alignment for an image ensemble can rectify images in the spatial domain such that the aligned images are as similar to each other as possible. This important technology has been applied to various object classes and medical applications. However, previous approaches to joint alignment work on an ensemble of a single object class. Given an ensemble with multiple object classes, we propose an...
This paper addresses the problem of developing facial image quality metrics that are predictive of the performance of existing biometric matching algorithms and incorporating the quality estimates into the recognition decision process to improve overall performance. The first task we consider is the separation of probe/gallery qualities since the match score depends on both. Given a set of training...
Facial action provides various types of messages for human communications. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, frequent head movements, and ambiguous and uncertain facial motion measurements. As a result, current research in facial action recognition is limited to posed facial actions and often in frontal view.Spontaneous facial action...
Landmark labeling of training images is essential for many learning tasks in computer vision, such as object detection, tracking, and alignment. Image labeling is typically conducted manually, which is both labor-intensive and error-prone. To improve this process, this paper proposes a new approach to estimate a set of landmarks for a large image ensemble with only a small number of manually labeled...
For many computer vision problems, it is very important to produce the ground truth data. Manual data labeling is labor-intensive and prone to the human errors, whereas fully automatic data labeling is not feasible and reliable. In this paper, we propose an interactive labeling technique for efficient and accurate data labeling. Constructed on a Bayesian network (BN), the automatic image labeler produces...
Graphical models such as Bayesian networks (BNs) are being increasingly applied to various computer vision problems. One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and representative training data, which proves to be difficult to acquire for many computer vision tasks. On the other hand, there is often available qualitative prior knowledge...
Since training a SVM requires solving a constrained quadratic programming problem which becomes difficult for very large datasets, an improved particle swarm optimization algorithm is proposed as an alternative to current numeric SVM training methods. In the improved algorithm, the particles studies not only from itself and the best one but also from the mean value of some other particles. In addition,...
Since training a SVM requires solving a constrained quadratic programming problem which becomes difficult for very large datasets, an improved particle swarm optimization algorithm is proposed as an alternative to current numeric SVM training methods. In the improved algorithm, the particles studies not only from itself and the best one but also from the mean value of some other particles. In addition,...
Facial activities are the most natural and powerful means of human communication. Spontaneous facial activity is characterized by rigid head movements, non-rigid facial muscular movements, and their interactions. Current research in facial activity analysis is limited to recognizing rigid or non-rigid motion separately, often ignoring their interactions. Furthermore, although some of them analyze...
A carrier-less impulse-based UWB transceiver (TRX) chipset is presented. The TRX employs high-order pulse transmission with analog pulse-position modulation. Realized in a 0.18mum CMOS process, the TRX achieves a NF in the range of 7.7 to 8.1dB, an IIP3 of -12.3dBm, and a sensitivity of -80 to -72dBm. It consumes 76mW and 81 mW from a 1.8V supply in transmit and receive modes, respectively
Dynamically tracking facial features with large variation of face pose has been shown as one of the most challenging issues in facial feature tracking. The traditional statistical models employ a principal component analysis (PCA) to characterize the statistics of a set of example shapes, however they are restricted to a narrow view due to the global linearity assumption. In this paper, a novel model-based...
This paper presents a multi-state hierarchical approach for facial feature tracking. A hierarchical formulation of statistical shape models is proposed to characterize both global shape constraints of human faces and local structural details of facial components. Gabor wavelets and gray level profiles are integrated for effective and efficient representation of feature points. Furthermore, multi-state...
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