42412948
Hallucinating 3D Faces
Super resolution technique could produce a higher resolution image than the originally captured ones. However, a few works have been done in 3D models. In this paper, we focus on generating the 3D face model of higher resolution from one input of 3D face model. In our method, the 3D face models including the training samples and input model are remeshed to construct consistent mesh to find out the correspondence among the models. The super resolution then is performed in the remeshed models to reconstruct the high resolution model. The experiments using USF HumanID 3D face database of 100 3D face models are carried out, and demonstrate the presented algorithm is promising.
15030913
Automatic Real-Time Barcode Localization in Complex Scenes
Many applications exist for automatically finding and reading barcodes in complex scenes with a camera. The key problem is to search barcodes in a complex scene and supply them for a reading subsystem. However, illumination, rotation, perspective distortion and multiple barcodes circumstances make barcode localization difficult. By jointly analyzing texture and shape, we propose a realtime barcode localization method that is robust to the above problems. A user only needs to put a barcode in front of a camera at around 15 cm for common Web cameras, and the system then automatically locates and decodes the barcode. This paper only focuses at automatic localization. We will demonstrate the complete system live at the conference.
10475402
Human face orientation detection using power spectrum based measurements
This work presents a new approach to human face orientation detection. We start the work with establishing a frequency domain model to analyze the orientation distribution of the oriented patterns in an image, according to the power spectrum of its bi-valued image. This model is derived from the perspective of the rendering rules in classical computer graphics. On the assumptions of the crossed distribution rule and symmetry rule of facial textures, we use the model above to define a series of measurements, called power spectrum based measurements, to quickly determine the orientation of a human face. The experimental results on MIT, ORL and BiolD face databases demonstrate our approach is effective and promising.
8312634
Probabilistic Continuous Update Scheme in Location Dependent Continuous Queries
It is difficult to maintain the exact location of mobile objects due to the limited resources in a mobile network. A consequence of this problem is that the update cost for a location-dependent continuous query for moving objects can be quite high using traditional methods. In this paper, we propose a probabilistic update method to maintain the fidelity of the query results without incurring significant update cost. Our scheme makes use of two types of updates, one to keep the uncertainty of the mobile object’s position within a specific confidence interval, and the other using probability that the moving object’s location uncertainty will affect the query result as the threshold to decide whether an update should be generated or not. The effectiveness of our approach is demonstrated using a series of simulation experiments.
12065835
Privacy-Preserving Self-Organizing Map
Privacy-preserving data mining seeks to allow the cooperative execution of data mining algorithms while preserving the data privacy of each party concerned. In recent years, many data mining algorithms have been enhanced with privacy-preserving feature: decision tree induction, frequent itemset counting, association analysis, k-means clustering, support vector machine, Naive Bayes classifier, Bayesian networks, and so on. In this paper, we propose a protocol for privacy-preserving self-organizing map for vertically partitioned data involving two parties. Self-organizing map (SOM) is awidely used algorithmfor transforming data sets to a lower dimensional space to facilitate visualization. The challenges in preserving data privacy in SOM are (1) to securely discover the winner neuron from data privately held by two parties; (2) to securely update weight vectors of neurons; and (3) to securely determine the termination status of SOM. We propose protocols to address the above challenges. We prove that these protocols are correct and privacy-preserving. Also, we prove that the intermediate results generated by these protocols do not violate the data privacy of the participating parties.
7999297
A deferrable scheduling algorithm for real-time transactions maintaining data freshness
Periodic update transaction model has been used to maintain freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus in the past studies such as the more-less scheme by Xiong and Ramamrithan (2004) in which update transactions are guaranteed by the deadline monotonic scheduling algorithm by Leung and Whitehead (1982) to complete by their deadlines. In this paper, we propose a novel algorithm, namely deferrable scheduling, for minimizing imposed workload while maintaining temporal validity of real-time data. In contrast to previous work, update transactions scheduled by the deferrable scheduling algorithm follow a sporadic task model. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical analysis of its processor utilization, which is verified in our experiments. Our experimental results also demonstrate that the deferrable scheduling algorithm is a very effective approach, and it significantly outperforms the more-less scheme in terms of reducing processor workload
9276774
Privacy-preservation for gradient descent methods
Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multi-party computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.