Memory leak occurs when useless objects cannot be released for a long time during program execution. Memory leaked objects may cause memory overflow, system performance degradation and even cause the system to crash when they become serious. This paper presents a dynamic approach for detecting and measuring memory leaked objects in Java programs. First, our approach tracks the program by JDI and records heap information to find out the potentially leaked objects. Second, we present memory leaking confidence to measure the influence of these objects on the program. Finally, we select three open-source programs to evaluate the efficiency of our approach. Furthermore, we choose ten programs from DaCapo 9.12 benchmark suite to reveal the time overhead of our approach. The experimental results show that our approach is able to detect and measure memory leaked objects efficiently.

Objects tracking methods have been wildly used in the field of video surveillance, motion monitoring, robotics and so on. Particle filter is one of the promising methods, but it is difficult to apply to real-time objects tracking because of its high computation cost. In order to reduce the processing cost without sacrificing the tracking quality, this paper proposes a new method for real-time 3D objects tracking, using parallelized particle filter algorithms by MapReduce architecture which is running on GPGPU. Our methods are as follows. First, we use a Kinect to get the 3D information of objects. Unlike the conventional 2D-based objects tracking, 3D objects tracking adds depth information. It can track not only from the x and y axis but also from the z axis, and the depth information can correct some errors in 2D objects tracking. Second, to solve the high computation cost problem, we use the MapReduce architecture on GPGPU to parallelize the particle filter algorithm. We implement the particle filter algorithms on GPU and evaluate the performance by actually running a program on CUDA5.5.

This paper deals with a problem where a robot identifies an object that a human asks it to bring by voice when there is a set of objects that the human and the robot can see. When the robot knows the requested object, it must identify the object and when it does not know the object, it must say it does not. This paper presents a new method for discriminating unknown objects from known objects using object images and human speech. It uses a confidence measure that integrates image recognition confidences and speech recognition confidences based on logistic regression.

To represent interactive objects, we propose a choice-disjunctive declaration statement of the form $S add R$ where S, R are the (procedure or field) declaration statements within a class. This statement has the following semantics: request the user to choose one between S and R when an object of this class is created. This statement is useful for representing interactive objects that require interaction with the user.

This paper seeks to improve power-performance efficiency of embedded systems by the use of dynamic reconfiguration. Programmable logic devices (PLDs) have the competence to optimize the power consumption by the use of partial and/or dynamic reconfiguration. It is a non-exclusive approach, which can use other power-reduction techniques simultaneous, and thus it is applicable to a myriad of systems. The power-performance improvement by dynamic reconfiguration was evaluated through an augmented reality system that translates Japanese into English. It is a wearable and mobile system with a head-mounted display (HMD). In the system, the computing core detects a Japanese word from an input video frame and the translated term will be output to the HMD. It includes various image processing approaches such as pattern recognition and object tracking, and these functions run sequentially. The system does not need to prepare all functions simultaneously, which provides a function by reconfiguration only when it is needed. In other words, by dynamic reconfiguration, the spatiotemporal module-based pipeline can introduce the reduction of its circuit amount and power consumption compared to the naive approach. The approach achieved marked improvements; the computational speed was the same but the power consumption was reduced to around $rac{1}{6}$.

We previously proposed a query-by-sketch image retrieval system that uses an edge relation histogram (ERH). However, it is difficult for this method to retrieve partial objects from an image, because the ERH is a feature of the entire image, not of each object. Therefore, we propose an object-extraction method that uses edge-based features in order to enable the query-by-sketch system to retrieve partial images. This method is applied to 20,000 images from the Corel Photo Gallery. We confirm that retrieval accuracy is improved by using the edge-based features for extracting objects, enabling the query-by-sketch system to retrieve partial images.

In this paper, a novel high-performance heuristic algorithm, named relay-race algorithm (RRA), which was proposed to approach a global optimal solution by exploring similar local optimal solutions more efficiently within shorter runtime for NP-hard problem is investigated. RRA includes three basic parts: rough search, focusing search and relay. The rough search is designed to get over small hills on the solution space and to approach a local optimal solution as fast as possible. The focusing search is designed to reach the local optimal solution as close as possible. The relay is to escape from the local optimal solution in only one step and to maintain search continuity simultaneously. As one of typical applications, multi-objective placement problem in physical design optimization is solved by the proposed RRA. In experiments, it is confirmed that the computational performance is considerably improved. RRA achieves overall Pareto improvement of two conflicting objectives: power consumption and maximal delay. RRA has its potential applications to improve the existing search methods for more hard problems.

This paper proposes a novel method for determining a three-dimensional (3D) bounding box to estimate pose (position and orientation) and size of a 3D object corresponding to a segmented object region in an image acquired by a single calibrated camera. The method is designed to work upon an object on the ground and to determine a bounding box aligned to the direction of the object, thereby reducing the number of degrees of freedom in localizing the bounding box to 5 from 9. Observations associated with the structural properties of back-projected object regions on the ground are suggested, which are useful for determining the object points expected to be on the ground. A suitable base is then estimated from the expected on-ground object points by applying to them an assumption of bilateral symmetry. A bounding box with this base is finally constructed by determining its height, such that back-projection of the constructed box onto the ground minimally encloses back-projection of the given object region. Through experiments with some 3D-modelled objects and real objects, we found that a bounding box aligned to the dominant direction estimated from edges with common direction looks natural, and the accuracy of the pose and size is enough for localizing actual on-ground objects in an industrial working space. The proposed method is expected to be used effectively in the fields of smart surveillance and autonomous navigation.

Reducing accident severity is an effective way to improve road safety. In the literature of accident severity analysis, two main disadvantages exist: most studies use classification accuracy to measure the quality of a classifier which is not appropriate in the condition of unbalanced dataset; the other is the results are not easy to be interpreted by users. Aiming at these drawbacks, a novel multi-objective particle swarm optimization (MOPSO) method is proposed to identify the contributing factors that impact accident severity. By employing Pareto dominance concept, a set of Pareto optimal rules can be obtained by MOPSO automatically, without any pre-defined threshold or variables. Then the rules are used to form a non-ordered classifier. A MOPSO is applied to discover a set of Pareto optimal rules. The accident data of Beijing between 2008 and 2010 are used to build the model. The proposed approach is compared with several rule learning algorithms. The results show the proposed approach can generate a set of accurate and comprehensible rules which can indicate the relationship between risk factors and accident severity.

This paper proposes a novel method to segment the object of a specific class based on a rough detection window (such as Deformable Part Model (DPM) in this paper), which is robust to the positions of the bounding boxes. In our method, the DPM is first used to generate the root and part windows of the object. Then a set of object part candidates are generated by randomly sampling windows around the root window. Furthermore, an undirected graph (the minimum spanning tree) is constructed to describe the spatial relationships between the part windows. Finally, the object is segmented by grouping the part proposals on the undirected graph, which is formulated as an energy function minimization problem. A novel energy function consisting of the data term and the smoothness term is designed to characterize the combination of the part proposals, which is globally minimized by the dynamic programming on a tree. Our experimental results on challenging dataset demonstrate the effectiveness of the proposed method.

In this paper, we consider multi-source multi-relay power allocation in cooperative wireless networks. A new intelligent optimization algorithm, multi-objective free search (MOFS), is proposed to efficiently allocate cooperative relay power to better support multiple sources transmission. The existence of Pareto optimal solutions is analyzed for the proposed multi-objective power allocation model when the objectives conflict with each other, and the MOFS algorithm is validated using several test functions and metrics taken from the standard literature on evolutionary multi-objective optimization. Simulation results show that the proposed scheme can effectively get the potential optimal solutions of multi-objective power allocation problem, and it can effectively optimize the tradeoff between network sum-rate and fairness in different applications by selection of the corresponding solution.

A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.

In this paper, a novel hybrid technique for analyzing complex antennas around the coated object is proposed, which is termed as “iterative vector fields with Physical Optics (PO)”. A closed box is used to enclose the antennas and the complex field vectors on the box' surfaces can then be obtained using Huygens principle. The equivalent electromagnetic currents on Huygens surfaces are computed by Higher-order Method of Moments (HOB-MoM) and the fields scattered from the coated object are calculated by PO method. In addition, the parallel technique based on Message Passing Interface (MPI) and Scalable Linear Algebra Package (ScaLAPACK) is employed so as to accelerate the computation. Numerical examples are presented to validate and to show the effectiveness of the proposed method on solving the practical engineering problem.

The relative arrangement, such as relative positions and orientations among objects, can play an important role in expressing the situation such as sports games and race scenes. In this paper, we propose a retargeting method that allows maintaining the relative arrangement. Our proposed retargeting method is based on a warping method which finds an optimal transformation by solving an energy minimization problem. To achieve protection of object arrangement, we introduce an energy that enforces all the objects and the relative positions among these objects to be transformed by the same transformation in the retargeting process. In addition, our method imposes the following three types of conditions in order to obtain more satisfactory results: protection of important regions, avoiding extreme deformation, and cropping with preservation of the balance of visual importance. Experimental results demonstrate that our proposed method maintains the relative arrangement while protecting important regions.

This letter presents an automatic 2D-to-3D conversion method using a structure from motion (SfM) process for multi-object scenes. The foreground and background regions may have different depth values in an image. First, we detect the foreground objects and the background by using a depth histogram. Then, the proposed method creates the virtual image by projecting each region with its computed projective matrix. Experimental results compared to previous research show that the proposed method provides realistic stereoscopic images.

In this letter, we exploit a new framework for detecting the non-specific object via combing the top-down and bottom-up cues. Specifically, a novel supervised discriminative dictionaries learning method is proposed to learn the coupled dictionaries for the object and non-object feature spaces in terms of the top-down cue. Different from previous dictionary learning methods, the new data reconstruction residual terms of coupled feature spaces, the sparsity penalty measures on the representations and an inconsistent regularizer for the learned dictionaries are all incorporated in a unitized objective function. Then we derive an iterative algorithm to alternatively optimize all the variables efficiently. Considering the bottom-up cue, the proposed discriminative dictionaries learning is then integrated with an unsupervised dictionary learning to capture the objectness windows in an image. Experimental results show that the non-specific object detection problem can be effectively solved by the proposed dictionary leaning framework and outperforms some established methods.

The transfer of prior knowledge from source domains can improve the performance of learning when the training data in a target domain are insufficient. In this paper we propose a new strategy to transfer deformable part models (DPMs) for object detection, using offline-trained auxiliary DPMs of similar categories as source models to improve the performance of the target object detector. A DPM presents an object by using a root filter and several part filters. We use these filters of the auxiliary DPMs as prior knowledge and adapt the filters to the target object. With a latent transfer learning method, appropriate local features are extracted for the transfer of part filters. Our experiments demonstrate that this strategy can lead to a detector superior to some state-of-the-art methods.

Hardware acceleration is an essential technique for extracting and tracking moving objects in real time. It is desirable to design tracking algorithms such that they are applicable for parallel computations on hardware. Exclusive block matching methods are designed for hardware implementation, and they can realize detailed motion extraction as well as robust moving object tracking. In this study, we develop tracking hardware based on an exclusive block matching method on FPGA. This tracking hardware is based on a two-dimensional systolic array architecture, and can realize robust moving object extraction and tracking at more than 100 fps for QVGA images using the high parallelism of an exclusive block matching method, synchronous shift data transfer, and special circuits to accelerate searching the exclusive correspondence of blocks.

How do global warming and agriculture influence each other? It is possible to answer the question by searching knowledge about the relationship between global warming and agriculture. As exemplified by this question, strong demands exist for searching relationships between objects. Mining knowledge about relationships on Wikipedia has been studied. However, it is desired to search more diverse knowledge about relationships on the Web. By utilizing the objects constituting relationships mined from Wikipedia, we propose a new method to search images with surrounding text that include knowledge about relationships on the Web. Experimental results show that our method is effective and applicable in searching knowledge about relationships. We also construct a relationship search system named “Enishi” based on the proposed new method. Enishi supplies a wealth of diverse knowledge including images with surrounding text to help users to understand relationships deeply, by complementarily utilizing knowledge from Wikipedia and the Web.

This paper introduces a simple algorithm for pedestrian detection on low resolution images. The main objective is to create a successful means for real-time pedestrian detection. While the framework of the system consists of edge orientations combined with the local binary patterns (LBP) feature extractor, a novel way of selecting the threshold is introduced. Using the mean-variance of the background examples this threshold improves significantly the detection rate as well as the processing time. Furthermore, it makes the system robust to uniformly cluttered backgrounds, noise and light variations. The test data is the INRIA pedestrian dataset and for the classification, a support vector machine with a radial basis function (RBF) kernel is used. The system performs at state-of-the-art detection rates while being intuitive as well as very fast which leaves sufficient processing time for further operations such as tracking and danger estimation.