16645940
Discovering Prediction Model for Environmental Distribution Maps
Currently environmental distribution maps, such as for soil fertility, rainfall and foliage, are widely used in the natural resource management and policy making. One typical example is to predict the grazing capacity in particular geographical regions. This paper uses a discovering approach to choose a prediction model for real-world environmental data. The approach consists of two steps: (1) model selection which determines the type of prediction model, such as linear or non-linear; (2) model optimisation which aims at using less environmental data for prediction but without any loss on accuracy. The latter step is achieved by automatically selecting non-redundant features without using specific models. Various experimental results on real-world data illustrate that using specific linear model can work pretty well and fewer environment distribution maps can quickly make better/comparable prediction with the benefit of lower cost of data collection and computation.
7983053
Auto-Calibration of a Compound-Type Omnidirectional Camera
We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.
17672081
A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components are weighted to reflect the high-level concepts, and a user's subjective perception, embodied in the images labelled by the user in the feedback. However, the number of labelled images is often small and the covariance matrix needed for weighting will be singular. For this reason, the commonly used methods discard the mutual correlation among the feature components completely and use a diagonal covariance matrix. In this paper, a sub-vector weighting scheme is proposed. This scheme partitions a multi-dimensional visual feature vector into multiple low-dimensional sub-vectors. The singularity of the covariance matrix for each sub-vector can be avoided due to the lower dimensionality of the sub-vectors. Thus, the mutual correlation in each sub-vector can be retained for weighting and an optimally weighted similarity metric can be applied on each sub-vector. The similarity scores obtained from different sub-vectors are combined, as the final scor...
2447873
Multilabel SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
34620301
Video Local Pattern based Image Matching for Visual Mapping
Image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. Based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visually-similar and temporally-adjacent to that frame. Instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. A model is then estimated from a local pattern, and the matching of images is performed by comparing these models. Experimental results demonstrated its improvement over that comparing individual frames
42393300
Synchronization between Two Different Hyperchaotic Dynamical Systems Using Nonlinear Control
This paper presents synchronization between two different hyperchaotic systems by nonlinear control laws. First, we introduced hyperchaotic Rossler system and hyperchaotic Chen system, and then synchronization problem between hyperchaotic Rossler system and hyperchaotic Chen system has been investigated. We consider the hyperchaotic Rossler system as the drive system and hyperchaotic Chen system as the response system. Last, Numerical simulations are shown to verify the results.
17154425
Background Subtraction using Incremental Subspace Learning
Background modeling and subtraction is a basic component of many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and activity analysis. In this paper, we propose an effective and adaptive background modeling and subtraction approach that can deal with dynamic scenes. The proposed approach uses a subspace learning method to model the background and the subspace is updated on-line with a sequential Karhunen-Loeve algorithm. A linear prediction model is also used to make the detection more robust. Experimental results show that the proposed approach is able to model the background and detect moving objects under various type of background scenarios with close to real-time performance.
8968963
Offense based temporal segmentation for event detection in soccer video
Sports video is regarded as a good testing bed for techniques on content based video analysis and processing. Although partially successful systems have been designed for specific sports domains with limited data, most previous works do not adequately address the problem of temporal segmentation for event detection, nor the event representation problem. In this paper, we present an analysis of soccer video for detecting the semantic notion of <i>offense</i>. It is not only useful as a new semantic concept of sports video analysis, but also provides temporal segmentation for video event detection and representation. We propose a system to detect the offensive unit in soccer video automatically. The offensive unit is then used to calculate new semantics like <i>possession</i>, as well as to detect goal events in video. Experimental results on various sources of soccer video have verified that our approach extracts the new semantic notions successfully and facilitates video event detection and representation
368510
A Performance Model for Domino Mail Server
Using performance model to analyze the Internet service is prevailing. However, the research of the performance model for mail server retrains in the immature status. In this paper, based on the queue theory, we propose a performance model for Domino mail server. The verification and analysis of the model are also presented. The experimental results show that this model characterizes the behavior of Domino mail server well and prove that it is useful for the qualitative performance measurement of Domino mail server.
15186113
Association based immune network for multimodal function optimization
For the problem of serious resources waste, indeterminate direction of local search and degeneration in the original opt-aiNet, a novel association based immune network is proposed for multimodal function optimization. The hexabasic model mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune association. The antibody population scale is semi-fixed reducing the time and space required to execute it. The information of the antibody population and the memory cells population is effective utilized to point out the direction of local search, to regulate the ratio between local search and global search, and to enhance the affinity of new antibodies. The elitist selection mechanism is adopted to ensure the convergence and stability of our algorithm respectively. The experiments on 10 benchmark functions show that when compared with opt-aiNet method, the new algorithm is capable of improving the search performance significantly in global convergence, convergence speed, computational cost, search ability, solution quality and algorithm stability.