The fitting of a causal dynamic model to an image is a fundamental problem in image processing, patternrecognition, and computervision. There are numerous other applications that require a causal dynamic model, such...
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ISBN:
(纸本)9781612848006
The fitting of a causal dynamic model to an image is a fundamental problem in image processing, patternrecognition, and computervision. There are numerous other applications that require a causal dynamic model, such as in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average (ARMA) and state-space models are the most widely known. In this paper we introduce a 2-D stochastic state-space system identification algorithm for obtaining stochastic 2-D, causal, recursive, and separable-in-denominator (CRSD) models in the Roesser state-space form. The algorithm is tested with a real image and the reconstructed image is shown to be almost indistinguishable to the true image.
Object detection is a fundamental task in computervision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based m...
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ISBN:
(纸本)9781457701221
Object detection is a fundamental task in computervision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based model such as structure learning, learning more discriminative features. To help researchers better understand the existing visual features’ potential for part based object detection and promote the deep research into part based object representation, we propose an evaluation framework to compare various visual features’ performance for part based model. The evaluation is conducted on challenging PASCAL VOC2007 dataset which is widely recognized as a benchmark database. We adopt Average Precision (AP) score to measure each detector’s performance. Finally, the full evaluation results are present and discussed.
Although for postal automation there are many pieces of work towards street name recognition on non-Indian languages, to the best of our knowledge there is no work on street name recognition on Indian languages. In th...
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Although for postal automation there are many pieces of work towards street name recognition on non-Indian languages, to the best of our knowledge there is no work on street name recognition on Indian languages. In this paper we proposed a scheme for recognition of Indian street name written in Bangla script. Because of the writing style of different individuals some of the characters in a street name may touch with its neighboring characters. Accurate segmentation of such touching into individual characters is a difficult task. To avoid such segmentation, here we consider a street name string as word and the street name recognition problem is treated as lexicon driven word recognition. Some of the street names may contain two or more words and we have concatenated these words to have a single word. In the proposed method, at first, street names are binarized and pre-segmented into possible primitive components (individual characters or its parts) analyzing their cavity portions. Pre-segmented components of a street name are then merged into possible characters to get the best street name. Dynamic programming (DP) is applied for the merging using total likelihood of characters as the objective function. To compute the likelihood of a character, modified quadratic discriminant function (MQDF) is used. Our proposed system shows 99.03% reliability with 18.80% rejection, and 0.79% error rates when tested on 4450 handwritten Bangla street name samples.
Scale-invariant features are widely used for image retrieval and shape classification. The curvature of a planar curve is a fundamental feature and it is geometrically invariant with respect it the coordinate system. ...
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Scale-invariant features are widely used for image retrieval and shape classification. The curvature of a planar curve is a fundamental feature and it is geometrically invariant with respect it the coordinate system. The curvature-based feature varies in position when multiscale analysis is performed. Therefore, it is important to recognize the scale in order to detect the feature point. Numerous shape descriptors based on contour shapes have been developed in the field of patternrecognition and computervision. A curvature scale-space (CSS) representation cannot be applied to a contour fragment and requires the tracking of feature points. In a gradient-based curvature computation, although the gradient computation considers the scale, the curvature is normalized with respect to not the scale but the contour length. The scale-invariant feature transform algorithm that detects feature points from an image solves similar problems by using the difference of Gaussian (DoG). It is difficult to apply the SIFT algorithm to a planar curve for feature extraction. In this paper, an automatic scale detection method for a contour fragment is proposed. The proposed method detects the appropriate scales and their positions on the basis of the difference of curvature (DoC) without the tracking of feature points. To calculate the differences, scale-normalized curvature is introduced. An advantage of the DoC algorithm is that the appropriate scale can be obtained from a contour fragment as a local feature. It then extends the application area. The validity of the proposed method is confirmed by experiments. The proposed method provides the most stable and robust scales of feature points among conventional methods such as curvature scale-space and gradient-based curvature.
In computervision research field shape matching plays a central role. A typical approach is based on the analysis of contour points of the objects. In many cases this task is faced by taking into account a contour su...
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In computervision research field shape matching plays a central role. A typical approach is based on the analysis of contour points of the objects. In many cases this task is faced by taking into account a contour subset made up by dominant points. This paper is based on a shape matching technique described in [1] by using dominant points as contour points set. In order to obtain better results we adopt modified shape descriptors (shape contexts) by getting rotational invariance. Experimental results demonstrate the accuracy improvement and the computational time reduction.
Crowd counting which aims at obtaining the number of people within a scene is an important computervision task. While most previous methods try to count people within one frame, this paper addresses this problem usin...
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Crowd counting which aims at obtaining the number of people within a scene is an important computervision task. While most previous methods try to count people within one frame, this paper addresses this problem using the detection flow which is defined as a set of object detection responses along the temporal video sequence. We argue that counting based on detection flow provides a better way to estimate the crowd size with following merits: 1) it can greatly alleviate the common weakness of an object detector including miss detection and false alarms; 2) it is robust to temporal object occlusions and noises; 3) it is more competent to give specific descriptions of the crowd, e.g. crowd moving directions and target locations. Experiment results on PETS 2009 dataset demonstrate the potential of this method.
This paper presents a new solution to the automatic localization and recognition of natural digits embossed on structured and rough surfaces. A new concept of combining periodic (e.g. FFT, DCT) with polynomial moments...
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This paper presents a new solution to the automatic localization and recognition of natural digits embossed on structured and rough surfaces. A new concept of combining periodic (e.g. FFT, DCT) with polynomial moments (e.g. Tchebichef moments) is shown that the new method can eliminate some of the problems associated with the Gibbs phenomena. QR decomposition is used to obtain a unitary basis, minimizing the numerical effort when modeling surfaces. The combined moment method is used to generate a smoothed global model of the surface structure. Following segmentation of the individual digits, patternrecognition based on moment function such as Hu’s moment invariants, Zernike moments, and Orthogonal Fourier Mellin Moments was used to recognize the stamped number after analyzing 3D surface data of the steel block. A prototype system of the laser-scanning instrument was implemented and test measurements were performed in a production line. The results showed the correct functionality of the method.
Single image haze removal (or de-haze) using dark channel prior model is effective when there exists a dark channel within the image. So for the images which do not meet the dark channel prior, the haze removal result...
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Single image haze removal (or de-haze) using dark channel prior model is effective when there exists a dark channel within the image. So for the images which do not meet the dark channel prior, the haze removal result may appear light pollution, cross-color. In this paper, we propose an algorithm to judge whether a single image meets the dark channel prior, and for the image that fails to meet the prior, it eliminates the cross-color influence in the image after de-hazing. Experimental results show that ours can determine whether the image meets dark channel prior, and has the better de-haze effect.
The proposed scheme is an approach which can be used to improve the performance of traditional image segmentation systems. The scheme is based on a framework that employs the output of an existing image segmentation p...
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The proposed scheme is an approach which can be used to improve the performance of traditional image segmentation systems. The scheme is based on a framework that employs the output of an existing image segmentation process together with hierarchical clustering using an information theoretic similarity measure. Experimental results clearly show that when the scheme operates in conjunction with a state of the art image segmentation algorithm, it yields significantly superior performance over a wide spectrum of natural images. These results are based on informal subjective evaluation tests as well as on objective measurements obtained from processing the Berkeley BSDS 300 image dataset.
In this study, main goal is to estimate scene illuminant chromaticity values for each of the three color channels (red, green, blue). It is found that analysis of the pixel values, which are expressed by dichromatic r...
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In this study, main goal is to estimate scene illuminant chromaticity values for each of the three color channels (red, green, blue). It is found that analysis of the pixel values, which are expressed by dichromatic reflection and inverse-intensity chromaticity space definitions, with appropriate statistical methods gives aproximate values for light source chromaticity within an acceptable error range. Single image of a scene is sufficient for calculations. We saw that chromaticity values of light sources with different colors can be approximately calculated.
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