On the design of globally optimal communication strategies for real-time noisy communication systems with noisy feedback

A real-time communication system with noisy feedback is considered. The system consists of a Markov source, forward and backward discrete memoryless channels, and a receiver with limited memory. The receiver can send messages to the encoder over the backward noisy channel. The encoding at the encoder and the decoding, the feedback, and the memory update at the receiver must be done in real-time. A distortion metric that does not tolerate delays is given. The objective is to design an optimal real-time communication strategy, i.e., design optimal real-time encoding, decoding, feedback, and memory update strategies to minimize a total expected distortion over a finite horizon. This problem is formulated as a decentralized stochastic optimization problem and a methodology for its sequential decomposition is presented. This results in a set of nested optimality equations that can be used to sequentially determine optimal communication strategies. The methodology exponentially simplifies the search for determining an optimal real-time communication strategy.     

3-D object recognition using 2-D views

We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood of each hypothesis is then computed using the probabilistic models of shape appearance. Only hypotheses ranked high enough are considered for further verification with the most likely hypotheses verified first. The proposed approach has been evaluated using both artificial and real data, illustrating promising performance. We also present preliminary results illustrating extensions of the AFoVs framework to predict the intensity appearance of an object. In this context, we have built a hybrid recognition framework that exploits geometric knowledge to hypothesize the location of an object in the scene and both geometrical and intesnity information to verify the hypotheses.     

Capacity/Storage Tradeoff in High-Dimensional Identification Systems

The asymptotic tradeoff between the number of distinguishable objects and the necessary storage space (or equivalently, the search complexity) in an identification system is investigated. In the discussed scenario, high-dimensional (and noisy) feature vectors extracted from objects are first compressed and then enrolled in the database. When the user submits a random query object, the extracted noisy feature vector is compared against the compressed entries, one of which is output as the identified object. The first result this paper presents is a complete single-letter characterization of achievable storage and identification rates (measured in bits per feature dimension) subject to vanishing probability of identification error as the dimensionality of feature vectors becomes very large. This single-letter characterization is then extended for a multistage system whereby depending on the number of entries, the identification is performed by utilizing part or all of the recorded bits in the database. Finally, it is shown that a necessary and sufficient condition for a two-stage system to achieve single-stage capacities at each stage is Markovity of the optimal test channels.     

基于聚类分析的客体聚合信息级别推演方法

多级客体关系的复杂性,使得等级化网络存在着客体聚合引起信息泄露的问题。针对这一问题,该文提出了基于聚类分析的客体资源聚合信息级别的推演方法,首先依据属性重要程度,对客体属性进行约简,形成属性矢量;然后通过形式概念分析,计算概念引力,对同一安全域内的客体资源进行相似性分析,实现客体资源聚类;最后,依据属性或属性子集级别模糊集可能性测度,推演出由同类客体推导出更高级别信息的可能性。通过该方法,能够有效地制定等级化网络区域边界访问控制策略,控制主体对同一类客体的受限访问,从而降低信息系统失泄密的风险。     

Stochastic differential equations and geometric flows

In previous years, curve evolution, applied to a single contour or to the level sets of an image via partial differential equations, has emerged as an important tool in image processing and computer vision. Curve evolution techniques have been utilized in problems such as image smoothing, segmentation, and shape analysis. We give a local stochastic interpretation of the basic curve smoothing equation, the so called geometric heat equation, and show that this evolution amounts to a tangential diffusion movement of the particles along the contour. Moreover, assuming that a priori information about the shapes of objects in an image is known, we present modifications of the geometric heat equation designed to preserve certain features in these shapes while removing noise. We also show how these new flows may be applied to smooth noisy curves without destroying their larger scale features, in contrast to the original geometric heat flow which tends to circularize any closed curve.     

A modular approach to facial feature segmentation on real sequences

In this paper a modular approach of gradual confidence for facial feature extraction over real video frames is presented. The problem is being dealt under general imaging conditions and soft presumptions. The proposed methodology copes with large variations in the appearance of diverse subjects, as well as of the same subject in various instances within real video sequences. Areas of the face that statistically seem to be outstanding form an initial set of regions that are likely to include information about the features of interest. Enhancement of these regions produces closed objects, which reveal—through the use of a fuzzy system—a dominant angle, i.e. the facial rotation angle. The object set is restricted using the dominant angle. An exhaustive search is performed among all candidate objects, matching a pattern that models the relative position of the eyes and the mouth. Labeling of the winner features can be used to evaluate the features extracted and provide feedback in an iterative framework. A subset of the MPEG-4 facial definition or facial animation parameter set can be obtained. This gradual feature revelation is performed under optimization for each step, producing a posteriori knowledge about the face and leading to a step-by-step visualization of the features in search.     

A multiple-hypothesis approach for multiobject visual tracking.

In multiple-object tracking applications, it is essential to address the problem of associating targets and observation data. For visual tracking of multiple targets which involves objects that split and merge, a target may be associated with multiple measurements and many targets may be associated with a single measurement. The space of such data association is exponential in the number of targets and exhaustive enumeration is impractical. We pose the association problem as a bipartite graph edge covering problem given the targets and the object detection information. We propose an efficient method of maintaining multiple association hypotheses with the highest probabilities over all possible histories of associations. Our approach handles objects entering and exiting the field of view, merging and splitting objects, as well as objects that are detected as fragmented parts. Experimental results are given for tracking multiple players in a soccer game and for tracking people with complex interaction in a surveillance setting. It is shown through quantitative evaluation that our method tracks through varying degrees of interactions among the targets with high success rate.     

Optimality and Complexity of Pure Nash Equilibria in the Coverage Game

In this paper, we investigate the coverage problem in wireless sensor networks using a game theory method.We assume that nodes are randomly scattered in a sensor field and the goal is to partition these nodes into K sets. At any given time, nodes belonging to only one of these sets actively sense the field. A key challenge is to achieve this partition in a distributed manner with purely local information and yet provide near optimal coverage. We appropriately formulate this coverage problem as a coverage game and prove that the optimal solution is a pure Nash equilibrium. Then, we design synchronous and asynchronous algorithms, which converge to pure Nash equilibria. Moreover, we analyze the optimality and complexity of pure Nash equilibria in the coverage game. We prove that, the ratio between the optimal coverage and the worst case Nash equilibrium coverage, is upper bounded by 2 ? 1 m+1 (m is the maximum number of nodes, which cover any point, in the Nash equilibrium solution s*). We prove that finding pure Nash equilibria in the general coverage game is PLS-complete, i.e. ?as hard as that of finding a local optimum in any local search problem with efficient computable neighbors?. Finally, via extensive simulations, we show that, the Nash equilibria coverage performance is very close to the optimal coverage and the convergence speed is sublinear. Even under the noisy environment, our algorithms can still converge to the pure Nash equilibria.     

Stochastic re-grasp planning for vision aided capture of deforming and moving object

In the last two decades several researchers have studied the problem of grasping of a moving rigid object based on vision data. However the problem of grasping a moving and deforming object still remains unsolved. In this paper we present the development of a fast algorithm for the computation of the optimal force closure grasp points on a slowly moving and deforming object. The main focus is to find the best grasp points as the object deforms, track its position at a future instant and then transfer grasp at that location. At first the potential grasping configurations satisfying force closure are evaluated through an objective function that maximizes the grasping span while minimizing the distance between the object centroid and the intersection of the fingertip normal. A population based stochastic search strategy is adopted for computing the optimal configurations and re-localizing them as the shape undergoes translations, rotations and scaling. Experiments have been conducted to prove that the object can be tracked in real time and the optimal grasp points determined so that a three finger robot can capture it. This method works in real time so it has great potential for application in industries for grasping objects whose shapes are not clearly defined (e.g. cloth), deforming objects, or objects that are partially occluded.     

Outer Bounds for Multiple-Access Channels With Feedback Using Dependence Balance

We use the idea of dependence balance to obtain a new outer bound for the capacity region of the discrete memoryless multiple-access channel with noiseless feedback (MAC-FB). We consider a binary additive noisy MAC-FB whose feedback capacity is not known. The binary additive noisy MAC considered in this paper can be viewed as the discrete counterpart of the Gaussian MAC-FB. Ozarow established that the capacity region of the two-user Gaussian MAC-FB is given by the cut-set bound. Our result shows that for the discrete version of the channel considered by Ozarow, this is not the case. Direct evaluation of our outer bound is intractable due to an involved auxiliary random variable whose large cardinality prohibits an exhaustive search. We overcome this difficulty by using a composite function and its properties to explicitly evaluate our outer bound. Our outer bound is strictly less than the cut-set bound at all points on the capacity region where feedback increases capacity. In addition, we explicitly evaluate the Cover-Leung achievable rate region for the binary additive noisy MAC-FB in consideration. Furthermore, using the tools developed for the evaluation of our outer bound, we also explicitly characterize the boundary of the feedback capacity region of the binary erasure MAC, for which the Cover-Leung achievable rate region is known to be tight. This last result confirms that the feedback strategies developed by Kramer for the binary erasure MAC are capacity achieving.     

Design, analysis, and implementation of a telemedicine remoteconsultation and diagnosis session playback using discrete event systemspecification

Telemedicine remote consultation and diagnosis (RCD) software is a complex and distributed system. RCD allows physicians to collaborate on radiology or pathology cases from distributed geographic locations. It is very important to simplify design, construction, and maintenance of such a system. Currently, an object-oriented design methodology is used to design and develop a software system in a modular fashion. Object-oriented software is made of various objects that work together. From the design of the software system, we get information about object methods and inheritance. We also get information about which objects are contained in a particular object and which objects are used by another object. One important element that the traditional object-oriented design misses is time. We propose the use of discrete event system specification (DEVS) in the design and analysis of a software system, such as RCD. With DEVS, coupling between objects can be specified explicitly and an object behavior can be shown in time. We introduce DEVS, show the time-line analysis of remote consultation and diagnosis session playback using DEVS, and then describe its implementation     

Boosting image object retrieval and indexing by automatically discovered pseudo-objects

State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. An image object search is performed by comparing query object’s feature vector with those for database images. However, a database image vector generally carries mixed information of the entire image which may contain multiple objects and background. Search quality is degraded by such noisy (or diluted) feature vectors. To tackle this problem, we propose a novel representation, pseudo-objects – a subset of proximate feature points with its own feature vector to represent a local area, to approximate candidate objects in database images. In this paper, we investigate effective methods (e.g., grid, G-means, and GMM–BIC) to estimate pseudo-objects. Additionally, we also confirm that the pseudo-objects can significantly benefit inverted-file indexing both in accuracy and efficiency. Experimenting over two consumer photo benchmarks, we demonstrate that the proposed method significantly outperforms other state-of-the-art object retrieval and indexing algorithms.     

Siamese visual tracking with multilayer feature fusion and corner distance IoU loss

The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. However, due to the lack of semantic information, they have difficulty meeting the accuracy requirements of the task when faced with complex backgrounds and other challenging scenarios. In response to this problem, we propose a new model, which uses the improved ResNet-22 network to extract deep features with more semantic information. Multilayer feature fusion is used to obtain a high-quality score map to reduce the influence of interference factors in the complex background on the tracker. In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and the visual object tracking data sets, VOT2016 and VOT2017, and achieved competitive performance, proving the effectiveness of this method.     

Optimal Transmission and Limited Feedback Design for OFDM/MIMO Systems in Frequency Selective Block Fading Channels

In this paper, we propose a systematic design framework to deal with the problem of limited CSIT feedback for MIMO-OFDM systems with correlated subcarriers. Based on the framework, we obtain the optimal transmission and CSI feedback strategies given the limited CSI feedback constraint. We propose a MIMO-OFDM design with combined adaptive power control and beam-forming framework for optimizing MIMO-OFDM link capacity with limited feedback in frequency selective fading channels. We derive a computationally efficient algorithm which exploits subcarrier correlation to search for the design of the optimal transmission and feedback strategy. We found that with a small number of bits for CSIT feedback, there is already significant capacity gain in the MIMO-OFDM systems     

Cramer-Rao bounds for parametric shape estimation in inverse problems

We address the problem of computing fundamental performance bounds for estimation of object boundaries from noisy measurements in inverse problems, when the boundaries are parameterized by a finite number of unknown variables. Our model applies to multiple unknown objects, each with its own unknown gray level, or color, and boundary parameterization, on an arbitrary known background. While such fundamental bounds on the performance of shape estimation algorithms can in principle be derived from the Cramer-Rao lower bounds, very few results have been reported due to the difficulty of computing the derivatives of a functional with respect to shape deformation. We provide a general formula for computing Cramer-Rao lower bounds in inverse problems where the observations are related to the object by a general linear transform, followed by a possibly nonlinear and noisy measurement system. As an illustration, we derive explicit formulas for computed tomography, Fourier imaging, and deconvolution problems. The bounds reveal that highly accurate parametric reconstructions are possible in these examples, using severely limited and noisy data.     

Depth segmentation in real-world scenes based on U–V disparity analysis

Depth segmentation has the challenge of separating the objects from their supporting surfaces in a noisy environment. To address the issue, a novel segmentation scheme based on disparity analysis is proposed. First, we transform a depth scene into the corresponding U-V disparity map. Then, we conduct a region-based detection method to divide the object region into several targets in the processed U-disparity map. Thirdly, the horizontal plane regions may be mapped as slant lines in the V-disparity map, the Random Sample Consensus (RANSAC) algorithm is improved to fit such multiple lines. Moreover, noise regions are reduced by image processing strategies during the above processes. We respectively evaluate our approach on both real-world scenes and public data sets to verify the flexibility and generalization. Sufficient experimental results indicate that the algorithm can efficiently segment and label a full-view scene into a group of valid regions as well as removing surrounding noise regions.     

Depth-based detection with region comparison features

Most object detection approaches proposed over the years rely on visual features that help to segregate objects from their backgrounds. For instance, segregation may be facilitated by depth features because they provide direct access to the third dimension. Such access enables accurate object-background segregation. Although they provide a rich source of information, depth images are sensitive to background noise. This paper addresses the issue of handling background noise for accurate foreground–background segregation. It presents and evaluates the Region Comparison (RC) features for fast and accurate body part detection. RC features are depth features inspired by the well-known Viola–Jones detector. Their performances are compared to the recently proposed Pixel Comparison (PC) features, which were designed for fast and accurate object detection from Kinect-generated depth images. The results of our evaluation reveal that RC features outperform PC features in detection accuracy and computational efficiency. From these results we may conclude that RC features are to be preferred over PC features to achieve accurate and fast object detection in noisy depth images.     

Distributed multitarget classification in wireless sensor networks

We study distributed strategies for classification of multiple targets in a wireless sensor network. The maximum number of targets is known a priori but the actual number of distinct targets present in any given event is assumed unknown. The target signals are modeled as zero-mean Gaussian processes with distinct temporal power spectral densities, and it is assumed that the noise-corrupted node measurements are spatially independent. The proposed classifiers have a simple distributed architecture: local hard decisions from each node are communicated over noisy links to a manager node which optimally fuses them to make the final decision. A natural strategy for local hard decisions is to use the optimal local classifier. A key problem with the optimal local classifier is that the number of hypotheses increases exponentially with the maximum number of targets. We propose two suboptimal (mixture density and Gaussian) local classifiers that are based on a natural but coarser repartitioning of the hypothesis space, resulting in linear complexity with the number of targets. We show that exponentially decreasing probability of error with the number of nodes can be guaranteed with an arbitrarily small but nonvanishing communication power per node. Numerical results based on real data demonstrate the remarkable practical advantage of decision fusion: an acceptably small probability of error can be attained by fusing a moderate number of unreliable local decisions. Furthermore, the performance of the suboptimal mixture density classifier is comparable to that of the optimal local classifier, making it an attractive choice in practice.