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

This paper describes a passive stereo system for capturing the 3D geometry of a face in a single-shot under standard light sources. The system is low-cost and easy to deploy. Results are submillimeter accurate and commensurate with those from state-of-the-art systems based on active lighting, and the models meet the quality requirements of a demanding domain like the movie industry. Recovered models are shown for captures from both high-end cameras in a studio setting and from a consumer binocular-stereo camera, demonstrating scalability across a spectrum of camera deployments, and showing the potential for 3D face modeling to move beyond the professional arena and into the emerging consumer market in stereoscopic photography. Our primary technical contribution is a modification of standard stereo refinement methods to capture pore-scale geometry, using a qualitative approach that produces visually realistic results. The second technical contribution is a calibration method suited to face capture systems. The systemic contribution includes multiple demonstrations of system robustness and quality. These include capture in a studio setup, capture off a consumer binocular-stereo camera, scanning of faces of varying gender and ethnicity and age, capture of highly-transient facial expression, and scanning a physical mask to provide ground-truth validation.

High-quality single-shot capture of facial geometry

International Geoscience and Remote Sensing Symposium (IGARSS)

From the Publisher: 
This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

数字信号处理 = Understanding digital signal processing

The rich information available in hyperspectral imagery has provided significant opportunities for material classification and identification. Due to the problem of the “curse of dimensionality” (called Hughes phenomenon) posed by the high number of spectral channels along with small amounts of labeled training samples, dimensionality reduction is a necessary preprocessing step for hyperspectral data. Generally, in order to improve the classification accuracy, noise bands generated by various sources (primarily the sensor and the atmosphere) are often manually removed in advance. However, the removal of these bands may discard some important discriminative information, eventually degrading the classification accuracy. In this paper, we propose a new strategy to automatically select bands without manual band removal. Firstly, wavelet shrinkage is applied to denoise the spatial images of the whole data cube. Then affinity propagation, which is a recently proposed feature selection approach, is used to choose representative bands from the noise-reduced data. Experimental results on three real hyperspectral data collected by two different sensors demonstrate that the bands selected by our approach on the whole data (containing noise bands) could achieve higher overall classification accuracies than those by other state-of-the-art feature selection techniques on the manual-band-removal (MBR) data, even better than the bands identified by the proposed approach on the MBR data, indicating that the removed “noise” bands are valuable for hyperspectral classification, which should not be eliminated.

Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal

This paper presents an automatic scratch detection and removal approach for archive film sequences. The proposed detector mainly exploits temporal coherency of candidate scratch positions, which are obtained using an automatic scratch detection method proposed in the literature. In the restoration stage, both spatial and temporal information are employed. The proposed edge priority based scratch removal algorithm successfully removes scratch effects from archive film sequences.

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Scratch detection via temporal coherency analysis and removal using edge priority based interpolation

Hyperspectral images provide significant information about the chemical constituents of materials and increased classification and recognition capability, due to the high content of spectral information they contain. Therefore, hyperspectral imaging has found use in many application areas, including recycling. The spectra of paper, cardboards, glass, metal and plastics can be measured and used for their identification, and consequently sorting, due to different chemical properties. In this study, various recycling material samples are successfully sorted by using shortwave infrared (SWIR) hyperspectral imaging system and the proposed algorithms. The application is the most comprehensive in terms of different recycling classes in hyperspectral imaging literature.

Automatic waste sorting using shortwave infrared hyperspectral imaging system

This letter proposes the use of denoising in conjunction with 2-D empirical mode decomposition (2D-EMD) of hyperspectral image bands for higher classification accuracy. Initially, 2D-EMD is performed to hyperspectral image bands for decomposition into intrinsic mode functions (IMFs). Then, denoising is applied to the first IMF of each band because this IMF includes local high-spatial-frequency components. Features reconstructed as the sums of lower order IMFs are then used for classification. Support vector machine classification is used as a classification approach in this letter. Experimental results show that the proposed technique can provide a higher classification accuracy.

Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions

Blotch detection and removal is an important issue for archive film restoration. In this work, a two-stage Simplified Ranked Order Difference (SROD) detector that takes local motion changes into consideration has been proposed to increase blotch detection performance. Furthermore a novel pixel-based correction method that determines the new values of blotched pixels from spatio-temporal correlation considering edge information is developed. Experimental results show that the proposed approaches give successful detection and correction performances and outperform previously proposed techniques.

M. Kemal Gullu

Papers

Scratch detection via temporal coherency analysis and removal using edge priority based interpolation

Automatic waste sorting using shortwave infrared hyperspectral imaging system

Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions

Blotch detection and removal for archive film restoration

An investigation of the usability of sound recognition for source separation of packaging wastes in reverse vending machines.