Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

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

We investigate efficient on-line lossless coding of motion capture (mocap) data in this work. The mocap data consist of a set of curves as the temporal trajectory of variables known as the degrees of freedom (dofs). To achieve compression, we sample each dof's curve temporally, interpolate the curve by a piece-wise Hermite spline as the prediction, and encode residuals with DPCM and Huffman coding. We compare the fixed-interval and the adaptive-interval sampling methods. Both schemes can achieve an averaged bit rate saving of around 80% (or, equivalently, a compression ratio of 20%) with real-time encoding/decoding.

On-line lossless mocap data compression

A technique that combines temporal filtering and spatial beamforming for joint PN (pseudonoise) code acquisition and DOA (direction-of-arrival) estimation in asynchronous direct-sequence code-division multiple access (DS-CDMA) systems with antenna arrays is proposed in this research. Traditionally, the joint code acquisition and DOA estimation is a 2-D search problem in the space-time domain. In this work, we apply window-based spatial filtering to antenna outputs to result in a set of temporal vectors, and then perform PN code acquisition on these vectors. Search results from several 1-D temporal vectors are combined to determine delay positions. Once delay positions are determined, the soft information provided by the acquisition process can be fully exploited by the next stage, i.e. DOA estimation. The proposed algorithm is a blind approach, which means that the training sequence is not necessary. Besides, the number of antennas required can be much less than that of the incoming signals, and the DOA search range can be largely reduced based on the soft information from the acquisition phase. Numerical simulations are given to demonstrate that the proposed solution is resistant to the near-far effect and robust to the change of the fading environment.

https://ieeexplore.ieee.org/iel5/8574/27150/01207106.pdf

Joint PN code acquisition and DOA estimation in asynchronous DS-CDMA systems

We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a scale-limited time-concentrated operator, and examine various vector spaces associated with such an operator. It is proved that the scale-limited signal space can be decomposed into the direct sum of two subspaces and only the component in one subspace can be exactly reconstructed, where the reconstructable subspace can be interpreted as a space consisting of scale/time-limited signals. Due to the ill-posedness of scale-limited extrapolation, a regularization process is introduced for noisy data extrapolation.

Regularization approach for signal extrapolation with wavelets

A lightweight human facial expression recognition (FER) solution aiming at mobile/edge applications is proposed in this work. The solution, called ExpressionHop, consists offour modules: 1) cropping out patches based on facial landmarks, 2) applying filter banks to each patch to generate a rich set of joint spatial-spectral features, 3) conducting the discriminant feature test (DFT) to select features with higher discriminant power, and 4) performing the final classification task with a classifier. We conduct performance benchmarking on ExpressionHop, traditional and deep learning methods on several commonly used FER datasets such as JAFFE, CK + and KDEF. Experimental results show that ExpressionHop achieves comparable or better classification accuracy. Yet, its model size only has 30K parameters, which is significantly lower than those of deep learning methods.

C.-C. Jay Kuo

Papers

On-line lossless mocap data compression

Joint PN code acquisition and DOA estimation in asynchronous DS-CDMA systems

Regularization approach for signal extrapolation with wavelets

ExpressionHop: A Lightweight Human Facial Expression Classifier

Special Issue on High Fidelity Media Processing