We present a simple and powerful scheme to allow CSG of implicit surfaces on the GPU. We decompose the boolean expression of surfaces into sum-of-products form. Our algorithm presented in this paper then renders each ...
详细信息
ISBN:
(纸本)9781479915880
We present a simple and powerful scheme to allow CSG of implicit surfaces on the GPU. We decompose the boolean expression of surfaces into sum-of-products form. Our algorithm presented in this paper then renders each product term, sum of products can be automatically by enabling depth test. Our Approximate CSG uses adaptive marching points algorithm for finding ray-surface intersection. Once we find an interval where root exists after root-isolation, this is used for presence of intersection. We perform root-refinement only for the uncomplemented terms in the product. Exact CSG is done by using the discriminant of the ray-surface intersection for the presence of the root. Now we can simply evaluate the product expression by checking all uncomplemented terms should be true and all complemented terms should be false. If our condition is met, we find the maximum of all the roots among uncomplemented terms to be the solution. Our algorithm is linear in the number of terms O(n). We achieve real-time rates for 4-5 terms in the product for approximate CSG. We achieve more than real-time rates for Exact CSG. Our primitives are implicit surfaces so we can achieve fairly complex results with less terms.
Development of computer-aided diagnosis (CAD) systems for early detection of the pathological brain is essential to save medical resources. In recent years, a variety of techniques have been proposed to upgrade the sy...
详细信息
ISBN:
(纸本)9781467385640
Development of computer-aided diagnosis (CAD) systems for early detection of the pathological brain is essential to save medical resources. In recent years, a variety of techniques have been proposed to upgrade the system's performance. In this paper, a new automatic CAD system for brain magnetic resonance (MR) image classification is proposed. the method utilizes two-dimensional discrete wavelet transform to extract features from the MR images. the dimension of the features have been reduced using principal component analysis (PCA) and linear discriminant analysis (LDA), to obtain the more significant features. Finally, the reduced set of features are applied to the random forests classifier to determine the normal or pathological brain. A standard dataset, Dataset-255 of 255 images (35 normal and 220 pathological) is used for the validation of the proposed scheme. To improve the generalization capability of the scheme, 5-fold stratified cross-validation procedure is utilized. the results of the experiments reveal that the proposed scheme is superior to other state-of-the-art techniques in terms of classification accuracy with substantially reduced number of features.
Skin colour detection under poor or varying illumination condition is a big challenge for various imageprocessing and human-computer interaction applications. In this paper, a novel skin detection method utilizing im...
详细信息
ISBN:
(纸本)9781450347532
Skin colour detection under poor or varying illumination condition is a big challenge for various imageprocessing and human-computer interaction applications. In this paper, a novel skin detection method utilizing image pixel distribution in a given colour space is proposed. the pixel distribution of an image can provide a better localization of the actual skin colour distribution of an image. Hence, a local skin distribution model (LSDM) is derived using the image pixel distribution model and its similarity withthe global skin distribution model (GSDM). Finally, a fusion-based skin model is obtained using boththe GSDM and the LSDM. Subsequently, a dynamic region growing method is employed to improve the overall detection rate. Experimental results show that proposed skin detection method can significantly improve the detection accuracy in presence of varying illumination conditions.
In this paper, a real time multi-view human activity recognition model using a RGB-D (Red Green BlueDepth) sensor is proposed. the method receives as input RGBD data streams in real time from a Kinect for Windows V2 s...
详细信息
ISBN:
(纸本)9781467385640
In this paper, a real time multi-view human activity recognition model using a RGB-D (Red Green BlueDepth) sensor is proposed. the method receives as input RGBD data streams in real time from a Kinect for Windows V2 sensor. Initially, a skeleton-tracking algorithm is applied which gives 3D joint information of 25 unique joints. the presented approach uses a weighted version of the Fast Dynamic Time Warping that weighs the importance of each skeleton joint towards the Dynamic Time Warping (DTW) similarity cost. To recognize multi-view human activities, the weighted Dynamic TimeWarping warps a time sequence of joint positions to reference time sequences and produces a similarity value. Experimental results demonstrate that the proposed method is robust, flexible and efficient with respect to multiple views activity recognition, scale and phase variations activities at different realistic scenes.
Dictionary learning has been used to solve inverse problems in imaging and as an unsupervised feature extraction tool in vision. the main disadvantage of dictionary learning for applications in vision is the relativel...
详细信息
ISBN:
(纸本)9781450347532
Dictionary learning has been used to solve inverse problems in imaging and as an unsupervised feature extraction tool in vision. the main disadvantage of dictionary learning for applications in vision is the relatively long feature extraction time during testing;owing to the requirement of solving an iterative optimization problem (10-minimization). the newly developed analysis framework of transform learning does not suffer from this shortcoming;feature extraction only requires a matrix vector multiplication. this work proposes an alternate formulation for transform learning that improves the accuracy even further. Experiments on benchmark databases show that our proposed transform learning yields results better than dictionary learning, autoencoder (AE) and restricted Boltzmann machine (RBM). the feature extraction time is fast as AE and RBM.
We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper ...
详细信息
We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set. (c) 2006 Elsevier B.V. All rights reserved.
We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensio...
详细信息
We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensionality reduction and construction of individual feature spaces. Recognition is done by probabilistically fusing the confidence weights derived from each feature space. the performance of the method is validated on FERET and AR databases. (c) 2006 Elsevier B.V. All rights reserved.
In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today re...
详细信息
ISBN:
(纸本)9781467385640
In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today remains to be handwritten signature. It is the most natural method of authenticating a person's identity as compared to other biometric and cryptographic forms of authentication. We propose a method for verifying the signatory's identity by using Zernike Moments as global shape descriptors. Zernike Moments are image moments that are rotation invariant. the moments are also orthogonal on a unit circle which ensures minimum redundancy between the features representing the object shape. the features extracted in our approach have a relatively low dimensionality as compared to other studies, while retaining high representation power of the moments. Moreover, the module developed using our approach was able to demonstrate high performance coupled with low computation times in testing phase, making it suitable for real time applications. Experiments show high overall performance of our approach with an equal error rate EER of 13.42% and area under the curve A(z) equal to 0.84 using 1564 images from the NFI SigComp2009 dataset.
In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coeffici...
详细信息
ISBN:
(纸本)9781450366151
In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coefficients are divided into non-overlapping blocks. the selected blocks based on a peace-wise non-linear chaotic map are transformed using discrete cosine transom followed by singular value decomposition. then a feature matrix is constructed in reliance on Hessian matrix and the final hash values are obtained. the proposed hashing system is resilient to different content-preserving image distortions such as geometric and filtering operations. the simulated results demonstrate the efficiency proposed framework in terms of security and robustness.
One of the common image forgery techniques is the splicing, where parts from different images are copied and pasted onto a single image. this paper proposes a new forensics method for detecting splicing forgeries in i...
详细信息
ISBN:
(纸本)9781450347532
One of the common image forgery techniques is the splicing, where parts from different images are copied and pasted onto a single image. this paper proposes a new forensics method for detecting splicing forgeries in images containing human faces. Our approach is based on extracting an illumination-signature from the faces of people present in an image using the dichromatic reflection model (DRM). the dichromatic plane histogram (DPH), which is calculated by applying the 2D Hough Transform on the face images, is used as the illumination-signature. the correlation measure is employed to compute the similarity between the DPHs obtained from different faces present in an image. Finally, a simple threshold on this similarity measure exposes splicing forgeries in the image. Experimental results show the efficacy of the proposed method.
暂无评论