2544976
Generalized 2D principal component analysis for face image representation and recognition
In the tasks of image representation, recognition and retrieval, a 2D image is usually transformed into a 1D long vector and modelled as a point in a high-dimensional vector space. This vector-space model brings up much convenience and many advantages. However, it also leads to some problems such as the Curse of Dimensionality dilemma and Small Sample Size problem, and thus produces us a series of challenges, for example, how to deal with the problem of numerical instability in image recognition, how to improve the accuracy and meantime to lower down the computational complexity and storage requirement in image retrieval, and how to enhance the image quality and meanwhile to reduce the transmission time in image transmission, etc. In this paper, these problems are solved, to some extent, by the proposed Generalized 2D Principal Component Analysis (G2DPCA). G2DPCA overcomes the limitations of the recently proposed 2DPCA (Yang et al., 2004) from the following aspects: (1) the essence of 2DPCA is clarified and the theoretical proof why 2DPCA is better than Principal Component Analysis (PCA) is given; (2) 2DPCA often needs much more coefficients than PCA in representing an image. In this work, a Bilateral-projection-based 2DPCA (B2DPCA) is proposed to remedy this drawback; (3) a Kernel-based 2DPCA (K2DPCA) scheme is developed and the relationship between K2DPCA and KPCA (Scholkopf et al., 1998) is explored. Experimental results in face image representation and recognition show the excellent performance of G2DPCA.
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.
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.
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
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.
21998790
NetTopo: Beyond Simulator and Visualizer for Wireless Sensor Networks
Deploying real WSN testbed provides a realistic testing environment, and allows users to get more accurate test results. However, deploying real testbed is highly constrained by the available budget when the test needs a large scale WSN environment. By leveraging the advantages of both simulators and real testbed, an approach that integrates simulation environment and testbed can effectively solve both scalability and accuracy issues. In this paper, we present NetTopo for providing both simulation and visualization functions to assist the investigation of algorithms in WSNs. Two case studies are described to prove the effectiveness of NetTopo.
6708583
Reducing error accumulation effect in multithreaded memory systems
Chip multithreading is exposed to dual challenges of increasing chip complexity and design variations. It is important to develop effective reliability-enhancing schemes to ensure robust computing. In this paper, we propose the immediate write-back and self-recovery schemes to reduce error accumulation effect in multithreaded memory systems. These schemes are built upon the previously proposed dynamic multithreading redundancy technique for improving reliability in multithreaded architectures. The idea of DMR is to exploit self-generated hardware redundancy due to inter-thread variations inherent in multithreaded architectures. Simulation results demonstrate that the proposed solutions improve error-control performance with minimal hardware overheads. The proposed immediate write-back and self-recovery schemes also feature good scalability for future process generations
15142417
Low-Complexity Analysis of Repetitive Regularities for Biometric Applications
Presented in this paper is a joint algorithm optimization and architecture design framework for analysis of repetitive regularities. Two closely coupled algorithm optimization techniques, referred to as the prime subspace periodicity transform (PSPT) and circular periodicity transform (CPT), are developed that significantly reduce computational complexity while enable the extraction of a wide spectrum of periodic features. The proposed PSPTCPT algorithms lead to a parallel and resource sharing VLSI architecture. While most of the current systems rely on software solutions to performance feature extraction, the performance benefits rendered by the proposed framework show advantages in dealing with data-intensive computation for emerging applications in biometrics and bioinformatics. The explosive growth in database complexity combined with demand for fast analysis make the proposed framework a promising solution. Experimental results on an iris identification system demonstrate up to 99.2% accuracy and 24.6% − 41.9% reduction in computational complexity.
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.
35028524
SmartOSEK: A Real-Time Operating System for Automotive Electronics
This paper puts forward SmartOSEK, a dependable platform for automobile electronics, which consists of an operating system compliant with OSEK/VDX specifications and an integrated development environment (IDE) that consists of many convenient tools, such as visual designer, system generator, time analyst, scheduling analyst, and running tracer. In the operating system, SmartOSEK OS and SmartOSEK COM are presented. In the IDE, we apply the graphic design, automatic code generation and time analysis to help developers devote their mind to the modeling of the applications. A design example of automated transmission system based on SmartOSEK platform is given, and good results are achieved.