1080835
Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation
In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms. Keywords—Finite Gaussian mixture model, Hidden Markov random field model, image segmentation, MRI.
15282622
Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.
4650146
A New Coarse-to-Fine Framework for 3D Brain MR Image Registration
Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world biomedical image applications. For instance, in order to register 3D brain MR images, current state of the art registration methods use a multi-resolution coarse-to-fine algorithm, which typically involves starting with low resolution images and working progressively through to higher resolutions, with the aim to avoid the local maximum "traps". However, these methods do not always successfully avoid the local maximum. Consequently, various rather sophisticated optimization methods are developed to attack this problem. In this paper, we propose a novel viewpoint on the coarse-to-fine registration, in which coarse and fine images are distinguished by different scales of the objects instead of different resolutions of the images. Based on this new perspective, we develop a new image registration framework by combining the multi-resolution method with novel multi-scale algorithm, which could achieve higher accuracy and robustness on 3D brain MR images. We believe this work has great contribution to biomedical image analysis and related applications.
31267474
Skewed Data Partition and Alignment Techniques for Compiling Programs on Distributed Memory Multicomputers
Minimizing data communication over processors is the key to compile programs for distributed memory multicomputers. In this paper, we propose new data partition and alignment techniques for partitioning and aligning data arrays with a program in a way of minimizing communication over processors. We use skewed alignment instead of the dimension-ordered alignment techniques to align data arrays. By developing the skewed scheme, we can solve more complex programs with minimized data communication than that of the dimension-ordered scheme. Finally, the experimental results show that our proposed scheme has more opportunities to align data arrays such that data communications over processors can be minimized.
16174340
A new encryption algorithm for image cryptosystems
Abstract There are two major differences of the characteristics of the text data and image data. One difference is that the size of image data is usually much larger than that of text data. The other is that plain data rarely permit loss when a compression technique is used, but image data do. In this paper, we design an efficient cryptosystem for images. Our method is based on vector quantization, which is one of the popular image compression techniques. Our method can achieve the following two goals. One goal is to design a high security image cryptosystem. The other goal is to reduce computational complexity of the encryption and decryption algorithms.
8821639
Realistic video avatar
We present a system for the implementation of a photorealistic avatar using video captured from a user. This is achieved by constructing the dynamic video texture map and combining it with the 3D mesh model of the user to render the photorealistic avatar. The dynamic video texture map reflects the user's facial expressions and is generated by composing the cylindrical texture map of the user with constant updates either directly from the input video or derived from a temporal and spatial interpolation scheme. To derive the 3D mesh model for the user, we define feature points on a generic model and use the feature points identified on the video sequence to deform and obtain the avatar model. The goal of this realistic video avatar project is to provide a vivid representation of participants with a more realistic quality avatar, as compared to using only the facial animation parameters defined in MPEG-4 without the corresponding image updates. This scheme is suitable for conferencing applications because it requires much lower bandwidth than live video, and yet provides a 3D avatar representation for any virtual environment.
6997568
Gathering-Load-Balanced Tree Protocol for Wireless Sensor Networks
Wireless communications and digital electronics have enabled the development of low-power, low-cost, multifunctional sensors. The functionality of sensor network mainly is the measurement collection. The previous proposed protocols mainly focus on broadcasting tree structure, but it may not be efficient in data gathering. In this paper, we propose novel tree architecture that is a dynamic, adjustable, load-balanced tree in sensor networks to decrease the energy utilization and to prolong the lifetime of sensors. A load-balanced tree can share the forwarding loads between non-leaf nodes. Due to the limit of tree topology, we try to adjust forming a tree that has similar amount of children between non-leaf nodes. When the energy of non-leaf node is lower than a designated threshold, the tree structure can be readjusted to prolong the lifetime of tree. In simulation, we compare our protocol with different adjustment phases and an existed protocol. From the result, our protocol can have better performance than others
27503425
A More General Sufficient Condition for Partition Testing to be Better than Random Testing
Partition testing is an approach to program testing in which the input domain of the program is divided into partitions and test cases are selected from each partition. An alternative approach, known as random testing, is to select test cases at random from the entire input domain. Weyuker and Jeng (1991) observed that if all partitions are equal in sizes and the number of test cases selected from each partition is the same, then partition testing has a better chance of detecting at least one failure than random testing. This condition has been generalized by Chen and Yu (1994). They proved that partition testing is better than random testing so long as test cases are selected in proportion to the size of partitions. In this paper, we prove a more general sufficient condition. Partition testing performs better if the sampling rates are higher for partitions with higher failure rates. Some special cases that follow from this result are also considered.