There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Image Super-Resolution Using Deep Convolutional Networks

In the future, multimedia technology will be able to provide video frame rates equal to or better than 30 frames per second (FPS). Until that time the hearing impaired community will be using band limited communication systems over unshielded twisted pair copper wiring. As a result, multimedia communication systems will use a coder/decoder (CODEC) to compress the video and audio signals for transmission. For these systems to be usable by the hearing impaired community, the algorithms within the CODEC have to be designed to account for the perceptual boundaries of the hearing impaired. We investigate the perceptual boundaries of speech reading and multimedia technology, which are the constraints that effect speech reading performance. We analyze and draw conclusions on the relationship between viseme groupings, accuracy of viseme recognition, and presentation rate. These results are critical in the design of multimedia systems for the hearing impaired.

Frame rate and viseme analysis for multimedia applications

A major problem in object oriented video coding and MPEG-4 is the encoding of object boundaries. In our previous work, we presented efficient methods for the lossy encoding of object boundaries which were optimal in the rate distortion sense. In this paper, we extend our work to utilize both the original image and an initial segmentation to obtain the optimal shape representation based on the criteria we impose. THe boundary detection and encoding problems are considered simultaneously. If there is low confidence on the location of the boundary, a large approximation error is allowed when encoding the boundary and vise versa. Experimental results demonstrate the effectiveness of the proposed algorithm.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Joint optimal object shape estimation and encoding

A recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and intensity field from a noisy-blurred image sequence is developed. By simultaneously estimating these two fields, information is made available to each filter regarding the reliability of estimates that they are dependent upon. Nonstationary models are used for the DVF and the intensity field in the proposed estimator, thus avoiding the smoothing of boundaries present in both. The advantage of the proposed SDIE (simultaneous displacement and intensity field estimation) algorithm is that the error inherent in estimating the DVF is taken into account in the filtering of the intensity field. A second advantage is that, through the use of the nonstationary VCGM (vector coupled Gauss-Markov) and STCGM (spatiotemporal coupled Gauss-Markov) models, boundaries in both the DVF and the intensity fields are preserved. The performance of the proposed SDIE algorithm was shown to be superior to that of the Wiener-based PR algorithm and the 2-D Kalman filter in estimating the DVF and intensity field, respectively, from a noisy-blurred image sequence. >

Recursive displacement estimation and restoration of noisy-blurred image sequences

General formulation of adaptive iterative image restoration algorithms.

In this paper, we briefly describe a single detector passive millimeter-wave imaging system, which has been previously presented. The system uses a cyclic sensing matrix to acquire incoherent measurements of the observed scene and then reconstructs the image using a Bayesian approach. The cyclic nature of the sensing matrix allows for the design of a single unified and compact mask that provides all the required random masks in a convenient way, such that no mechanical mask exchange is needed. Based on this setup, we primarily propose the optimal adaptive selection of sampling submasks out of the full cyclic mask to obtain improved reconstruction results. The reconstructed images show the feasibility of the imaging system as well as its improved performance through the proposed sampling scheme.

Aggelos K. Katsaggelos

Papers

Frame rate and viseme analysis for multimedia applications

Joint optimal object shape estimation and encoding

Recursive displacement estimation and restoration of noisy-blurred image sequences

General formulation of adaptive iterative image restoration algorithms.

Optimized compressive sampling for passive millimeter-wave imaging