10254746
Where's the Weet-Bix?
This paper proposes a new retrieval problem and conducts the initial study. This problem aims at finding the location of an item in a supermarket by means of visual retrieval. It is modelled as object-based retrieval and approached using the local invariant features. Two existing retrieval methods are investigated and their similarity measures are modified to better fit this new problem. More importantly, through the study this new retrieval problem proves itself to be a challenging task. An instant application of it is to help the customer find what they want without physically wandering around the shelves but a wide range of potential applications could be expected.
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.
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.
41493711
Car Plate Localization Using Modified PCNN in Complicated Environment
Car plate Localization, which remains a difficult problem under complicated environment, is the key problem in many traffic related applications. In this paper we describe a new method based on modified Pulse Coupled Neural Network (PCNN) with adaptive threshold, which can capture relatively complete objects in human perception. After inverse filtering, PCNN processing is applied to produce a firing time sequence image. Then car plates' position and rotated angle can be extracted from the firing image. Experiment results show that the correct car plate locating rate reaches 98%, which is higher than other Localization methods on the same image database.
6110501
A backoff copying scheme for contention resolution in wireless sensor networks
This paper aims at improving the throughput and fairness of the wireless sensor networks (WSN). Due to the disproportionate larger number of packets accumulated in the sensors that are closer to the base station, it's necessary to have a better contention resolution scheme to improve the throughput and fairness in WSN. In this paper, we propose a new mechanism named BCCR (Backoff Copying for Contention Resolution), which is targeted to a system-wide proportional fairness in WSN. The new method is designed particularly for a many-to-one data collection tree structure, employing a mechanism that translates a fairness model into contention resolution. In BCCR, the size of the backoff window is dynamically adjusted to the channel allocation rate of the flow sent by the sensors. And the backoff algorithm does not focus on each node, but focus on each region which is divided according to the traffic flow contention. In every contention region, sensor nodes will share the same backoff window by a copying scheme. With extensive simulations and testbed experiments, we show that BCCR can achieve the expected fairness and throughput increase over the traditional CSMA, especially when the network is congested.
16539998
Lossy to lossless image compression based on reversible integer DCT
A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).
5460989
End-To-End Worst-Case Response Time Analysis for Hard Real-Time Distributed Systems
The verification of end-to-end response time for distributed hard real-time systems is quite necessary for safety-critical applications. Although current time analysis techniques can precisely analyze the response time, the performance is not quite satisfying, especially for large size distributed systems. This paper presents a novel end-to-end worst-case response time analysis approach for hard real-time distributed systems. This technique is based on the critical instant analysis and the canonical form transformation. It extends the traditional holistic analysis technique by exploiting the precedence relations between tasks on both different processors and the same processor. Simulation results have shown that this algorithm can achieve accurate results and offers good performance for systems with wide range of CPU utilizations and task set size, and therefore is applicable to schedulability analysis of complex distributed systems.
14258683
A framework for checking behavioral compatibility for component selection
Component selection and composition are the main issues in component-based development (CBD). Existing approaches suffer from the problem that the components retrieved usually do not exactly fit with other components in the system being developed. While formal methods can be used to describe and check semantic characteristics to better match components, there are practical limitations which restrict their adoption. In this paper, we propose a framework to support a semantic description and selection of components. Towards this we first introduce a Simple Component Interface Language (SCIL). SCIL files can be translated to a variety of models including those that have a formal basis. We report our experience with two specific tools, viz., Reactive Modules and Alloy with a view to using tools based on formal methods but without exposing the details of the tools