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
16543010
A novel multi-resolution video representation scheme based on kernel PCA
Content-based video analysis calls for efficient and flexible video representation. In this paper, a novel multi-resolution video representation (MRVR) scheme is proposed and realized by performing the kernel principal component analysis (KPCA) on the low-level visual features extracted from a video sequence. By simply changing the kernel parameters or the dimensionality of the subspace, this scheme can represent video content from coarser to finer levels in the subspace, according to its intrinsic structure. An application of keyframe extraction is investigated to show the advantages of this representation scheme. Furthermore, based on this scheme, a two-level video summarization approach is proposed to represent long video sequences. The experimental results of both short and long video sequences have demonstrated the effectiveness and flexibility of the proposed video representation scheme.
15085849
A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.
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.
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.
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.
10422931
Chaos Control of Lorenz System Using Small Gain Theorem
This paper studies the problem of controlling chaos in Lorenz system. By dividing Lorenz system into two subsystems, the small gain theorem is used to design linear feedback controllers to make the equilibrium points of the controlled system globally asymptotically stable. Thus chaos in Lorenz system disappears under the controller.
11038275
Exploiting idle register classes for fast spill destination
On today's microprocessors, there often exist several different types of registers, e.g. general purpose registers and floating point registers. A given program may use one type of registers much more frequently than other types. This creates an opportunity to employ the infrequently used registers as spill destinations for the more frequently used register types. In this paper, we present a code optimization method named idle register exploitation (IRE) to exploit such opportunities. We developed a model, called the IRE model, or IREM, to determine the static performance gains of IRE versus spilling to the stack. On a microprocessor with fast data paths between different types of registers, we find that IRE method speeds up the execution of the SPECint benchmark suite from 1.7% to 10%. In contrast, on microprocessors with less efficient data transfer paths, the performance gain is limited. In some cases, performance may even suffer degradation. This result argues strongly for the adoption of fast data paths between different types of registers for the purpose of reducing register spills, which is important in view of the increased significance of memory bottlenecks on future microprocessors.
8970759
Linux kernels as complex networks: A novel method to study evolution
In recent years, many graphs have turned out to be complex networks. This paper presents a novel method to study Linux kernel evolution — using complex networks to understand how Linux kernel modules evolve over time. After studying the node degree distribution and average path length of the call graphs corresponding to the kernel modules of 223 different versions (V1.1.0 to V2.4.35), we found that the call graphs of the file system and drivers module are scale-free small-world complex networks. In addition, both of the file system and drivers module exhibit very strong preferential attachment tendency. Finally, we proposed a generic method that could be used to find major structural changes that occur during the evolution of software systems.
32712174
Hailstone Detection Based on Image Mining
Hailstone is one of the main meteorological disasters and it is very difficult to be forecasted effectively. A novel method of automatic recognition hail cloud is proposed base on association rules in this paper. Firstly, the method preprocesses the original reflectivity image. Then the color features and texture features are extracted from the radar echo image. Lastly, the association rules, which are used to distinguish hailstorm from rainstorm and super refract, are constructed, namely hail echo ensemble forecast model based on image mining is constructed. The results show that hail echo ensemble forecast model based on image mining has higher accuracy compared with the PUP products.
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.