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This article discusses how to quantify the risk with Value at Risk model, and then proposes some problems in the application of this model.

To improve the efficiency of multi-object recognition in complex scenes, a hybrid system is proposed to learn the concurrencies and spatial relationships among different objects, and to apply such relationships for better recognitions. The hybrid system includes a bottom-up saliency map to generate regions of interest (ROIs), an independent classifiers to recognize these ROIs based on object appearances, and a what-where-memory (WWM) to cast the top-down knowledge of object relationships to help the recognitions provided by independent classifiers. The WWM learns not only the concurrencies but also the spatial layouts of different classes, which can filter out the classes that unlikely appear, and distinguish the correct class from ambiguous classes provided by independent classifiers. Experiments of multi-object recognition on two well-known image datasets demonstrate the efficiency of the proposed system.

Multi-class object recognition is a critical capability for an intelligence robot to perceive its environment. In this paper, a new approach consisting of a number of modular neural networks is proposed to recognize multiple classes of objects for a robotic system. The population of the modular neural networks depends on the class number of the objects to be recognized and each modular network only focuses on learning one object class. For each modular neural network, both the bottom-up (sensory-driven) and top-down (expectation-driven) pathways are fused together, and a supervised learning algorithm is applied to update corresponding weights of both pathways. Furthermore, two different training strategies are evaluated: positive-only training and positive-and-negative training. Experiments on visual image recognition demonstrate the efficiencies of the proposed approach and the corresponding training strategies.

Investing in overseas oil and gas resources is part of the investment strategy of Chinese petroleum enterprises. Investment of overseas project is marked by greater uncertainty and more intense competitiveness thus making evaluation of the project and the assessment of investment opportunity more valuable. Based on the Game Option Method, value of investment opportunity and trigger point under monopoly condition were discussed. Then effects of competitiveness on investment opportunity and trigger point were also analyzed. The best investment strategies for the monopoly enterprise and enterprise under competitive condition were given.

In this paper, an artificial neural network model is built up with two pathways: bottom-up sensory-driven pathway and top-down expectation-driven pathway, which are fused to train the neural network for visual object recognition. During the supervised learning process, the bottom-up pathway generates hypotheses as network outputs. Then target label will be applied to update the bottom-up connections. On the other hand, the hypotheses generated by the bottom-up pathway will produce expectations on the sensory input through the top-down pathway. The expectations will be constrained by the real data from the sensory input which can be used to update the top-down connections accordingly. This two-pathway based neural network can also be applied to semi-supervised learning with both labeled and unlabeled data, where the network is able to generate hypotheses and corresponding expectations. Experiments on visual object recognition suggest that the proposed neural network model is promising to recover the object for the cases with missing data in sensory inputs.

This paper presents a new object tracking algorithm that embeds swarming particles into generic particle filter framework to achieve more robustness and flexibility. Firstly a group of particles associated with potential solutions are initialized in a high-dimensional space. Then particle swarm optimization (PSO) is used to drive particles flying. The object is tracked when the particles reach convergence. This PSO-based algorithm contains resample, similarity measure, and integration together such that the degeneracy problem of particle filter can be avoided. Furthermore, a multiple feature model is proposed for object description to enhance the tracking accuracy and efficiency. The proposed algorithm is independent with specific objects and can be used for any free-selected object tracking. Some experimental results demonstrate efficiency and robustness of the algorithm.

This paper presents a new object tracking algorithm by using the particle swarm optimization (PSO), which is a bio-inspired population-based searching algorithm. Firstly the potential solutions of the problem are projected into a state space called solution space where every point in the space presents a potential solution. Then a group of particles are initialized and start searching in this solution space. The swarm particles search for the best solution within this solution space using the particle swarm optimization (PSO) algorithm. An accumulative histogram of the object appearance is applied to build up the fitness function for the interested object pattern. Eventually the swarming particles driven by the fitness function converge to the optimal solution. Experimental results demonstrate that the proposed PSO method is efficient and robust in visual object tracking under dynamic environments.

This paper presents an automatic object detection and tracking algorithm by using particle swarm optimization (PSO) based method, which is a searching algorithm inspired by the behaviors of social insect in the nature. A cascade of boosted classifiers based on Haar-like features is trained and employed to detect objects. To improve the searching efficiency, first the object model is projected into a high-dimensional feature space, and the PSO-based algorithm is applied to search over this high-dimensional space and converge to some global optima, which are well-matched candidates in terms of object features. Then, a Bayes-based filter is used to identify the best match with the highest possibility among these candidates under the constraint of object motion estimation. The proposed algorithm considers not only the object features but also the object motion estimation to speed up the searching procedure. Experimental results of tracking on vehicle and face demonstrate that the proposed method is efficient and robust under dynamic environment.

This paper presents a robust tracking algorithm using an adaptive tracking window associated with five parameters, where the parameters of the tracking window are optimized by a particle swarm optimization (PSO) algorithm. Basically, the optimization of a tracking window is transformed into a searching algorithm in a five-dimension feature space, which constrains the possibilities of the window. Particles associated with different parameters fly around the searching space independently, while they are sharing information from the society and adjust their behaviors to achieve the global optimization, which means the most optimized parameters for the tracking window. Appearance histogram is employed to calculate the fitness function for particles, where the distance between histograms is measured by histogram intersection. Estimated people motion is utilized to expedite the convergence of particles. Experimental results of people tracking demonstrate that the algorithm is efficient, robust, and adaptive to various rigid and non-rigid people motions