In this paper, we consider the clustering problem of independent general subspaces. That is, with given data points lay near or on the union of independent low-dimensional linear subspaces, we aim to recover the subspaces and assign the corresponding label to each data point. To settle this problem, we take advantages of both greedy strategy and energy minimization strategy to propose a simple yet effective algorithm based on the assumption that an m-branched (i.e., perfect m-ary) tree which is constructed by collecting m-nearest neighbor points in each node has a high probability of containing the near-exact subspace. Specifically, at first, subspace candidates are enumerated by multiple m-branched trees. Each tree starts with a data point and grows by collecting nearest neighbors in the breadth-first search order. Then, subspace proposals are further selected from the enumeration to initialize the energy minimization algorithm. Eventually, both the proposals and the labeling result are finalized by iterative re-estimation and labeling. Experiments with both synthetic and real-world data show that the proposed method can outperform state-of-the-art methods and is practical in real application.

Numerous methods have been developed to handle lighting variations in the preprocessing step of face recognition. However, most of them only use the high-frequency information (edges, lines, corner, etc.) for recognition, as pixels lied in these areas have higher local variance values, and thus insensitive to illumination variations. In this case, information of low-frequency may be discarded and some of the features which are helpful for recognition may be ignored. In this paper, we present a new and efficient method for illumination normalization using an energy minimization framework. The proposed method aims to remove the illumination field of the observed face images while simultaneously preserving the intrinsic facial features. The normalized face image and illumination field could be achieved by a reciprocal iteration scheme. Experiments on CMU-PIE and the Extended Yale B databases show that the proposed method can preserve a very good visual quality even on the images illuminated with deep shadow and high brightness regions, and obtain promising illumination normalization results for better face recognition performance.

This paper proposes a novel approach to generate stereo video in which the zoom magnification is not constant. Although this has been achieved mechanically in a conventional way, it is necessary for this approach to develop a mechanically complex system for each stereo camera system. Instead of a mechanical solution, we employ an approach from the software side: by using a pair of zoomed and non-zoomed video, a part of the non-zoomed video image is cut out and super-resolved for generating stereo video without a special hardware. To achieve this, (1) the zoom magnification parameter is automatically determined by using distributions of intensities, and (2) the cutout image is super-resolved by using optically zoomed images as exemplars. The effectiveness of the proposed method is quantitatively and qualitatively validated through experiments.

The MR image segmentation is always a challenging problem because of the intensity inhomogeneity. Many existing methods don't reach their expected segmentations; besides their implementations are usually complicated. Therefore, we originally interleave the extended Otsu segmentation with bias field estimation in an energy minimization. Via our proposed method, the optimal segmentation and bias field estimation are achieved simultaneously throughout the reciprocal iteration. The results of our method not only satisfy the required classification via its applications in the synthetic and the real images, but also demonstrate that our method is superior to the baseline methods in accordance with the performance analysis of JS metrics.

We investigate a design strategy for subthreshold circuits focusing on energy-consumption minimization and yield maximization under process variations. The design strategy is based on the following findings related to the operation of low-power CMOS circuits: (1) The minimum operation voltage (VDDmin) of a circuit is dominated by flip-flops (FFs), and VDDmin of an FF can be improved by upsizing a few key transistors, (2) VDDmin of an FF is stochastically modeled by a log-normal distribution, (3) VDDmin of a large circuit can be efficiently estimated by using the above model, which eliminates extensive Monte Carlo simulations, and (4) improving VDDmin may substantially contribute to decreasing energy consumption. The effectiveness of the proposed design strategy has been verified through circuit simulations on various circuits, which clearly show the design tradeoff between voltage scaling and transistor sizing.

We propose a new method to precisely detect pupil contours in face images. Pupil contour detection is necessary for various applications using face images. It is, however, difficult to detect pupils precisely because of their weak edges or lack of edges. The proposed method is based on minimizing the energy of pattern and edge. The basic idea of this method is that the energy, which consists of the pattern and the edge energy, has to be minimized. An efficient search method is also introduced to overcome the underlying problem of efficiency in energy minimization methods. "Guide patterns" are introduced for this purpose. Moreover, to detect pupils more precisely we use an ellipse model as pupil shape in this paper. Experimental results show the effectiveness of the proposed method.

We proposed a soft-decision decoding algorithm for cyclic codes based on energy minimization principle. This letter presents the algorithm which improves decoding performance and decoding complexity of the previous method by giving more initial positions and introducing a new criterion for terminating the decoding procedure. Computer simulation results show that both the decoded block error rate and the decoding complexity decrease by this method more than by the previous method.

We propose a novel soft-decision decoding algorithm for cyclic codes based on energy minimization principle. The well-known soft-decision decoding algorithms for block codes perform algebraic (hard-decision) decoding several times in order to generate candidate codewords using the reliability of received symbols. In contrast, the proposed method defines energy as the Euclidean distance between the received signal and a codeword and alters the values of information symbols so as to decrease the energy in order to seek the codeword of minimum energy, which is the most likely codeword. We let initial positions be the information parts of signals obtained by cyclically shifting a received signal and look for the point, which represents a codeword, of minimum energy by moving each point from several initial positions. This paper presents and investigates reducing complexity of the soft-decision decoding algorithm. We rank initial positions in order of reliability and reduce the number of initial positions in decoding. Computer simulation results show that this method reduces decoding complexity.

This paper describes a new feature extraction model (Active Model) which is extended from the active contour model (Snakes). Active Model can be applied to various energy minimizing models since it integrates most of the energy terms ever proposed into one model and also provides the new terms for multiple images such as motion and stereo images. The computational order of energy minimization process is estimated in comparison with a dynamic programming method and a greedy algorithm, and it is shown that the energy minimization process in Active Model is faster than the others. Some experimental results are also shown.