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Indirect ImmunoFluorescence (IIF) is currently the recommended method for the detection of antinuclear autoantibodies(ANA). It is an effective technique to reveal the presence of auto immune diseases; however, it is a subjective method and hence dependent on the experience and expertise of the physician. Moreover, inter-observer variability limits the reproducibility of IIF reading. To this end, we...
This paper explores the challenge of optimally categorizing regions for man-made environment. We propose using the histogram of oriented gradients (HOG) features for characterizing image regions, and propose an algorithm based on the entropy of HOG to select relevant regions. We also propose a regionsensitive feature selection algorithm for image registration. The algorithms are applied to several...
We present an approach for detecting moving objects from a dynamic video sequence, using a stereo camera system. The detection of moving objects is a challenging problem, especially when backgrounds are also time-varying due to the concurrent changes of moving objects and backgrounds. Most of the previous approaches have been limited to the use of appearance information such as colors and 2D motions...
In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling...
In this paper we present a novel method for automatic text-line parameter selection for stereo image pairs. The parameters are selected such that correspondence between the same content in a stereo pair is maximized. Automatic parameter selection has been carried out by establishing robust text-line correspondence which is also a contribution of the presented work. The proposed method is applied to...
Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible...
An automatic text recognizer needs, in first place, to localize the text in the image the more accurately possible. For this purpose, we present in this paper a robust method for text detection. It is composed of three main stages: a segmentation stage to find character candidates, a connected component analysis based on fast-to-compute but robust features to accept characters and discard non-text...
We propose a new component-tree based method with efficient and effective pruning strategies for userintention guided text extraction from scene images. A grayscale image is represented first as two component-trees, whose nodes represent possible candidates of character components. The non-text candidates are then pruned by using contrast, geometric and text line information as well as the constraint...
In this paper, we propose a novel text detection approach based on stroke width. Firstly, a unique contrast-enhanced Maximally Stable Extremal Region(MSER) algorithm is designed to extract character candidates. Secondly, simple geometric constrains are applied to remove non-text regions. Then by integrating stroke width generated from skeletons of those candidates, we reject remained false positives...
In this paper, we propose a novel method for extracting a set of baseline-independent features, which are based on the combination of global and local information. A HMM-based recognition system is developed with 161 models that include a space model and a blank model. All of the models are trained using the standard Baum-Welch Algorithm with the state-tying technique, and are then decoded using the...
Text localization in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we proposed a novel and effective approach to accurately localize scene texts. Firstly, Maximally stable extremal regions(MSER) are extracted as letter candidates. Secondly, after elimination of non-letter candidates by using geometric information, candidate regions are...
Intuitive and easily interpretable performance measures, repeatability and matching performance, for local feature detectors and descriptors were introduced by Mikolajczyk et al. [10, 9]. They, however, measured performance in a wide baseline setting that does not correspond to the visual object categorisation problem which is a popular application of the detectors and descriptors. The limitation...
Short message service (SMS) is now an indispensable way of social communication. However the mobile spam is getting increasingly serious, troubling users' daily life and ruining the service quality. We propose a novel approach for spam message detection based on mining the underlying social network of SMS activities. Comparing with strategies on keywords or flow detection, our network-based approach...
Most of conventional object matching methods are based on comparing the local features, which are too computational demanding to achieve realtime performance on object detection in videos. Recently, Dominant Orientation Templates (DOT) method was proposed to make online feature detection and comparison feasible. However, it still suffers the problem of fragility due to the noise and partial occlusions...
In this paper, we present an effective approach to locate scene text in images based on connected components analysis (CCA). Our approach first utilizes a multi-scale adaptive local thresholding operator to convert an image into two complementary binary images. Then, connected components (CCs) are extracted from both of them, which ensures that bright or dark text in contrast to background can be...
Recently many appearance based visual tracking algorithms have been investigated, aimed at building robust appearance models against challenges brought by the varying appearance of the target as well as the unconstrained environment. More often adaptive appearance models were used to capture these variances over time, but this may sometimes result in losing the target (drifting) due to inappropriate...
This paper presents a method for feature-based 3D object recognition in cluttered scenes. It deals with the problem of non-uniform sampling density which is inherent in typical range sensing methods. We suggest a method operating on polygonal meshes which overcomes the problem by exploiting surface area in both establishing local frames and creating feature descriptors. The method is able to recognize...
3D-model processing plays an important role in numerous applications. In this paper, we present an approach for 3D-model retrieval by creating index of closed curves in R3 generated from the center of a 3D-model, using a commute time mapping function. Our mapping function respects important properties in order to compute robust closed curves. Each curve describes a small region of the 3D-model. To...
This paper proposes a robust detection method for circular objects in noisy and inhomogeneous contrast image. This method detects circular objects not by the difference in image intensities between the object interior and its surrounding, but by the separability and uniformity of the image intensity distributions as calculated by Bhattacharyya Coefficient. The proposed method can detect obscure and...
In this paper, we present a method of robust tracking by accounting for hard negatives (i.e., distractors) of the tracking target explicitly. Our method extends the recently proposed Tracking-Learning-Detection (TLD) approach [7] in two aspects: (i) When learning the on-line fern detector, instead of using a set of features which are first randomly generated and then fixed throughout the tracking,...
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