11066290
Robust multi-level video representation using mean shift analysis
A robust method for multi-level video representation based on the mean shift analysis (MSA) of low-level visual features is proposed in this paper. By tuning the bandwidth of MSA, video representation from the coarse level to the fine level can be achieved. This representation form provides a flexible scheme for content-based video analysis such as summarization, classification, and retrieval. Compared with the conventional k-means or fuzzy c-means algorithms, our method can adjust the resolution of representation in a more straightforward way, and is more robust since it does not need to initialize the cluster centers
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
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.
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.
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.
1674451
A defect/error-tolerant nanosystem architecture for DSP
Emerging technologies such as silicon NanoWires (NW) and Carbon NanoTubes (CNT) have shown great potential for building the next generation of computing systems in the nano ranges. However, the excessive number of defects originating from bottom-up fabrication (such as a self-assembly process) poses a pressing challenge for achieving scalable system integration. This article proposes a new nanosystem architecture that employs nanowire crossbars for Digital Signal Processing (DSP) applications. Distributed arithmetic is utilized such that complex signal processing computation can be mapped into regular memory operations, thus making this architecture well suited for implementation by nanowire crossbars. Furthermore, the inherent features of DSP-type computation provide new insights to remedy errors (as logic/computational manifestation of defects). A new defect/error-tolerant technique that exploits algorithmic error compensation is proposed; at system level different trade-offs between correctness in output and performance are established while retaining low overhead in its implementation. As an instance of its application, the proposed approach has been utilized to a generic DSP nanosystem performing frequency-selective filtering. Simulation results show that the proposed nanoDSP introduces only a minor performance degradation under high defect rates and at a range of operational conditions. The proposed technique also features good scalability and viability for various DSP applications.
12761861
A Robust Closed-Loop Control Algorithm for Mean Arterial Blood Pressure Regulation
This paper presents an expert body sensor network (BSN) algorithm for autonomous control of mean arterial blood pressure using vasoactive drugs. A robust Proportional Integral Differential (PID) control algorithm based on a statistics model was designated. Extensive simulation results indicated that the light-weighted expert BSN system is robust enough against sensitivity variations and various artificial disturbances.
195719977
Improved Hierarchical Network Model Based on Pastry Scheme
The traditional Pastry design is a flat overlay network and limited-scope searching, in which all nodes have identical capabilities and responsibilities and all communication is symmetric. But in practice, because of a variety of available bandwidth and delay, each node has different capabilities. in this paper, a hierarchical Pastry network model is presented to address this limitation. Furthermore, the routing procedure and handling algorithm of the arrival and departure of nodes will be carefully discussed. Finally, some elementary experimental results obtained with a prototype implementation on JXTA platform indicate that the improved Pastry network model is superior in routing performance.
30078088
Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimator
This paper presents a novel clustering-based image segmentation method, which incorporates the features of robust statistics. To overcome the sensitivity to noise and outliers in fuzzy clustering, a simple but efficient M-estimator, Gaussian estimator, has been introduced to clustering analysis as weight or membership function. When applied to image analysis, the proposed Robust Gaussian Clustering/Segmentation (RGCS) algorithm exhibits more reasonable pixel classification and noise suppression performance with respect to FCM. Moreover, by using a uniform resolution parameter scheme, this method avoids producing coincident clusters, which occurs often in possibilistic clustering segmentation. Experiments on both the synthetic data and real image demonstrate the validity and power of the proposed algorithm.
14183415
Context-aware application programming for mobile devices
This research aims at facilitating the development of context-aware application software for mobile devices by providing a programming model, an implementation framework and a development environment. The programming model provides a multi-layered software architecture for context-aware application programming. The model supports developers to define contexts, behaviors and context-behavior binding rules through specifications and automates generation of context-aware application code based on the specifications. The implementation framework is a backbone program that implements the programming model. It facilitates the development in reducing the effort on the common tasks of context-awareness and help developers focus on the application-specific components. The development environment provides a series of tools to support the development of context-aware applications. These tools simplify the development process and provide the developed applications with robustness and testability.