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

http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

/pdf/detecting-faces-in-images-a-survey-4yhbbns6mr.pdf

Detecting faces in images: a survey

Most of the real world scenes have a very high dynamic range. However the common capture and display devices can handle only a limited dynamic range. General approach to solve this problem is to use multi-exposure images and composite them in the irradiance domain to get a High Dynamic Range (HDR) image [Reinhard et al. 2005]. The generated image will be able to represent the real world scene faithfully. However, it needs to be tone-mapped to a Low Dynamic Range (LDR) image for visualization in common displays and printers. Generation of the high-quality LDR image of the scene directly from multi-exposure images even in the absence of any knowledge of camera response function and the exposure settings of the camera is of interest to graphics community. We propose a gradient domain compositing technique to solve the above problem and call it Poisson Compositing. We compare the proposed methodology with similar existing techniques and show that the proposed method is very fast and accurate.

Poisson compositing

This paper describes a unique single camera-based dimensional measurement with a self-calibration method of image-based measurement. The system has been designed and implemented in one of the integrated steel plants in India. The purpose of the system is to obtain the frontal cross-sectional area of an ingot irrespective of its distance from the camera head. Automatic calibration is achieved by attaching a magnetic template of known area. This self-calibrating system is further refined to correct for the various distortions arising out of lens characteristics. The results obtained through field trials have been reported and found to be quite encouraging.

A distortion corrected single camera-based weight estimation technique for industrial objects.

Satellite image colorization is a broad challenging problem in the domain of remote sensing (RS) having huge potential applications. The problem becomes even more complicated under the few-shot setting yet it has barely been studied to date. In this paper, we propose a colorization framework for the RS scene for synthesizing optical images from their panchromatic (PAN) counterparts using color injection and attention fusion mechanism. Our proposed model ensures that the synthesized optical images are coherent with the structural variability of panchromatic images while constraining the realistic appearance in the optical domain from a few training image pairs. To accomplish the same, we introduce a novel Dual Attention fusion of Receptive Kernels (DARK) which considers the spatial nuances along with color injection conditioned on prior label allocation. DARK is a multi spectral-spatial feature generator that selectively accentuates important cross-spatial features based on attention fusion. We also employ a prior distribution constraint on color embedding generation for introducing vibrant yet diverse variance in a color generation. Our approach achieves state-of-the-art results on the publicly available EuroSAT and PatternNet datasets while demonstrating significant speedups. We showcase our results quantitatively by comparing the PSNR, mean squared error(MSE), and cosine similarity of generated images and qualitatively via visual perception. 

DARK: Few-shot remote sensing colorization using label conditioned color injection.

In this paper, we present a novel method for 3D reconstruction from epipolar plane (EP) representation of images and surface fitting for multiview or lightfield images. The proposed method detects parallelograms in EP images using mean shift segmentation. The slopes of the parallel lines along the left and right boundaries of the parallelograms are computed with Hough transform to estimate depth. In addition we also introduce a new concept of estimating depth from multiple EP images(diagonal EP images), making the resultant depth map denser. Multi-view depth maps are then combined and surface fitting is done to obtain the final depth map. Our method uses simple techniques to construct a quality depth map without being computationally demanding.

Multi-epipolar plane image based 3D reconstruction using robust surface fitting

In the real world, we often come across soft objects having spatially varying stiffness, such as human palm or a wart on the skin. In this paper, we propose a novel approach to render thin, deformable objects having spatially varying stiffness (inhomogeneous material). We use the classical Kirchhoff thin plate theory to compute the deformation. In general, the physics-based rendering of an arbitrary 3D surface is complex and time-consuming. Therefore, we approximate the 3D surface locally by a 2D plane using an area-preserving mapping technique - Gall-Peters mapping. Once the deformation is computed by solving a fourth-order partial differential equation, we project the points back onto the original object for proper haptic rendering. The method was validated through user experiments and was found to be realistic.

Subhasis Chaudhuri

Papers

Poisson compositing

A distortion corrected single camera-based weight estimation technique for industrial objects.

DARK: Few-shot remote sensing colorization using label conditioned color injection.

Multi-epipolar plane image based 3D reconstruction using robust surface fitting

Haptic Rendering of Thin, Deformable Objects with Spatially Varying Stiffness