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.
In order to reduce the detection time of Escherichia coli(E. coli) on fresh, minimally processed vegetables, a new close to real-time method based on artificial intelligence technology is presented. The ability of thr...
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In order to reduce the detection time of Escherichia coli(E. coli) on fresh, minimally processed vegetables, a new close to real-time method based on artificial intelligence technology is presented. The ability of three-layer multilayer perceptron neural network(MLP) models to predict E. coli contaminated condition using feature parameters extracted from E. coli images was compared with multivariate statistical model(discriminant analysis, DA) with respect to the identification accuracy of E. coli cells. The performance of MLP was 10% better than that from the DA. Comparisons of the counts of E. coli by rapid method and by traditional method(multiple-tube fermentation technique) closed correlated(R2=0.9867). Besides the high accuracy, the total detection time was within 1 h and the detection limit was 10 cells /ml. This is a clear improvement compared with the 6 days taken to obtain results from the traditional method.
The problem of calibrating paracatadioptric camera from sphere images is still open. In this paper, we propose a calibration method for paracatadioptric camera based on spheres. We notice that, under central catadiopt...
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The problem of calibrating paracatadioptric camera from sphere images is still open. In this paper, we propose a calibration method for paracatadioptric camera based on spheres. We notice that, under central catadioptric camera, a sphere is projected to two conies on the image plane, which can also be seen as the projections of two parallel circles on the viewing sphere by a virtue camera. These two conies are called a pair of antipodal sphere images. Firstly, we study properties of K(K >; 3) pairs of antipodal sphere images under paracatadioptric camera. Then, they can be estimated using these properties. Finally, paracatadioptric camera can be calibrated by three pairs of antipodal sphere images or more. The method only requires the projected contour of parabolic mirror is visible on the image plane in one view. Experimental results on both simulated and real image data have demonstrated the effectiveness of our method.
Wii Remote is the standard controller of the game console Nintendo Wii®. Despite its low cost, it has a very high performance and high resolution infrared camera. It also has a built-in chip for tracking up to 4 ...
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Wii Remote is the standard controller of the game console Nintendo Wii®. Despite its low cost, it has a very high performance and high resolution infrared camera. It also has a built-in chip for tracking up to 4 points from the infrared images. With these properties, the Wii Remote has attracted several attempts to calibrate and use them for metric 3D information. Thanks to Wii Remote's 4 point tracking hardware, people were able to calibrate Wii Remotes using carefully designed square patterns with infrared LEDs at each corner. However, it is not easy for average Wii Remote users to implement such calibration tools. In this paper, we give an overview of the current Wii Remote calibration implementations and present a novel method to fully calibrate multiple Wii Remotes using only two points, such as the LEDs of the Sensor Bar that comes with the Wii console. For the 3D reconstruction, only two Wii Remotes and a PC is needed. We also demonstrate the usability of a full 3D tracker with a multiplayer game that receives inputs from up to 4 users with only two Wii Remotes.
In radiation therapy, prior to each treatment fraction, the patient must be aligned to computed tomography (CT) data. Patient setup verification systems based on range imaging (RI) can accurately verify the patient po...
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In radiation therapy, prior to each treatment fraction, the patient must be aligned to computed tomography (CT) data. Patient setup verification systems based on range imaging (RI) can accurately verify the patient position and adjust the treatment table at a fine scale, but require an initial manual setup using lasers and skin markers. We propose a novel markerless solution that enables a fully-automatic initial coarse patient setup. The table transformation that brings template and reference data in congruence is estimated from point correspondences based on matching local surface descriptors. Inherently, this point-based registration approach is capable of coping with gross initial misalignments and partial matching. Facing the challenge of multi-modal surface registration (RI/CT), we have adapted state-of-the-art descriptors to achieve invariance to mesh resolution and robustness to variations in topology. In a case study on real data from a low-cost RI device (Microsoft Kinect), the performance of different descriptors is evaluated on anthropomorphic phantoms. Furthermore, we have investigated the system's resilience to deformations for mono-modal RI/RI registration of data from healthy volunteers. Under gross initial misalignments, our method resulted in an average angular error of 1.5° and an average translational error of 13.4 mm in RI/CT registration. This coarse patient setup provides a feasible initialization for subsequent refinement with verification systems.
This book is written in a cookbook style. Each chapter consists of recipes, each of which is composed of step-by-step instructions to complete a particular task. Each recipe also includes tips, alternatives, and refer...
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ISBN:
(数字)9781849514873
ISBN:
(纸本)9781849514866
This book is written in a cookbook style. Each chapter consists of recipes, each of which is composed of step-by-step instructions to complete a particular task. Each recipe also includes tips, alternatives, and references to other recipes or appropriate external sources. The book can be explored chapter by chapter or in no particular order. This book is intended for all levels of network administrators. If you are an advanced user of pfSense, then you can flip to a particular recipe and quickly accomplish the task at hand, while if you are new to pfSense, you can read chapter by chapter and learn all of the features of the system from the ground up.
A novel algorithm for image-based forest fire smog feature extraction based on Pulse-Coupled neural network (PCNN) is *** PCNN is derived from the phenomena of synchronous pulse burst in mammals' visual *** output...
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A novel algorithm for image-based forest fire smog feature extraction based on Pulse-Coupled neural network (PCNN) is *** PCNN is derived from the phenomena of synchronous pulse burst in mammals' visual *** outputs of PCNN represent unique features of imported images,and has been proved to be invariant to translation,rotation and *** this paper the image from video surveillance monitor is split into three dimensions in RGB color space,then input each dimension to PCNN to extract texture feature,the one-class support vector machine is applied to predict the feature in order to test feature's *** experimental results show that the algorithm we propose accurately distinguishes smog and non-smog images which outperform both the traditional Euclidean distance algorithms and the algorithms based on grey level co-occurrence matrix (GLCM) we applied in our earlier *** recognition accuracy is 98% with robustness on our smog image database.
We present a novel feature, named Spatio-Temporal Interest Points Chain (STIPC), for activity representation and recognition. This new feature consists of a set of trackable spatio-temporal interest points, which corr...
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We present a novel feature, named Spatio-Temporal Interest Points Chain (STIPC), for activity representation and recognition. This new feature consists of a set of trackable spatio-temporal interest points, which correspond to a series of discontinuous motion among a long-term motion of an object or its part. By this chain feature, we can not only capture the discriminative motion information which space-time interest point-like feature try to pursue, but also build the connection between them. Specifically, we first extract the point trajectories from the image sequences, then partition the points on each trajectory into two kinds of different yet close related points: discontinuous motion points and continuous motion points. We extract local space-time features around discontinuous motion points and use a chain model to represent them. Furthermore, we introduce a chain descriptor to encode the temporal relationships between these interdependent local space-time features. The experimental results on challenging datasets show that our STIPC features improves local space-time features and achieve state-ofthe- art results.
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