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Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification...
Several techniques have been proposed so far in order to perform faint compact source detection in wide field interferometric radio images. However, all these methods can easily miss some detections or obtain a high number of false positive detections due to the low intensity of the sources, the noise ratio, and the interferometric patterns present in the images. In this paper we present a novel strategy...
A visual attention based approach is proposed to extract texts from complicated background in camera-based images. First, it applies the simplified visual attention model to highlight the region of interest (ROI) in an input image and to yield a map, named the VA map, consisting of the ROIs. Second, an edge map of image containing the edge information of four directions is obtained by Sobel operators...
The checkerboard pattern is widely used in computer vision techniques for camera calibration and simple geometry acquisition, both in practical use and research. However, most of the current techniques fail to recognize the checkerboard pattern under distorted, occluded or discontinuous conditions, especially when the checkerboard pattern is dense. This paper proposes a novel checkerboard recognition...
We propose a method to remove moving objects from an in-vehicle camera image sequence by fusing multiple image sequences. Driver assistance systems and services such as Google Street View require images containing no moving object. The proposed scheme consists of three parts: (i) collection of many image sequences along the same route by using vehicles equipped with an omni-directional camera, (ii)...
In this paper, we propose a new object detection method that does not need a learning mechanism. Given a hand-drawn model as a query, we can detect and locate objects that are similar to the query model in cluttered images. To ensure the invariance with respect to rotation, scaling, and translation (RST), high curvature points (HCPs) on edges are detected first. Each pair of HCPs is then used to determine...
A new method of moment computation based on decomposition of the object into rectangular blocks is presented. The decomposition is accomplished by means of distance transform. The method is compared with earlier morphological methods, namely with erosion decomposition to squares. All the methods are also compared with direct computation by definition.
A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required and a recognition is possible from a single...
This paper extends the transition method for binarization based on transition pixels, a generalization of edge pixels. This method originally computes transition thresholds using the quantile thresholding algorithm, that has a critical parameter. We achieved an automatic version of the transition method by computing the transition thresholds with the Rosin's algorithm. We experimentally tested four...
A deconvolution is a fundamental technique and used in various vision applications. A maximum a posteriori estimation is known as a powerful tool. In this paper, we propose a progressive MAP-based deconvolution algorithm with a pixel dependent Gaussian image prior. In the proposed algorithm, a mean and a variance for each pixel are adaptively estimated. Then, the mean and the variance are progressively...
This paper presents a novel scheme to automatically and directly detect smoking events in video. In this scheme, a color-based ratio histogram analysis is introduced to extract the visual clues from appearance interactions between lighted cigarette and its human holder. The techniques of color re-projection and Gaussian Mixture Models (GMMs) enable the tasks of cigarette segmentation and tracking...
Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have...
We propose a technique to in paint large missing regions in range images. Such a technique can be used to restore degraded/occluded range maps. It can also serve to reconstruct dense depth maps from sparse measurements which can speed up the acquisition. Our method uses the visual cue from segmentation of an intensity image registered to the range image. Our approach enforces that pixels in the same...
In this paper, a novel script-independent block-based text line extraction technique is proposed for multi-skewed document images. Three parameters are defined to adopt the method with various writings. Extensive experiments on different datasets demonstrate that the proposed algorithm outperforms previous methods.
Maximally Stable Extremal Regions (MSERs) are one of the most prominent interest region detectors in computer vision due to their powerful properties and low computational demands. In general MSERs are detected in single images, but given image sequences as input, the repeatability of MSER detection can be improved by exploiting correspondences between subsequent frames by feature based analysis....
Subspace projection techniques are known to be susceptible to the presence of partial occlusions in the image data. To overcome this susceptibility, we present in this paper a confidence weighting scheme that assigns weights to pixels according to a measure, which quantifies the confidence that the pixel in question represents an outlier. With this procedure the impact of the occluded pixels on the...
A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values...
Given an image from a biometric sensor, it is important for the feature extraction module to extract an original set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction. For some biometric systems, Type I feature extraction is used exclusively. However, a second form of feature extraction does exist and is concerned...
Text frame classification is needed in many applications such as event identification, exact event boundary identification, navigation, video surveillance in multimedia etc. To the best of our knowledge, there are no methods reported solely dedicated to text frame classifications so far. Hence this paper presents a new approach to text frame classification in video based on capturing local observable...
The local binary pattern (LBP) operator is a computationally efficient local texture descriptor and has found many useful applications. However, its sensitivity to noise and the high dimensionality of histogram associated with a mediocre size neighborhood have raised some concerns. In this paper, we attempt to improve the original LBP by proposing a novel extension named extended local ternary pattern...
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