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

The PASCAL Visual Object Classes Challenge

A morphable model for the synthesis of 3D faces

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

This paper addresses the problem of recognizing free-form 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme. The global model description consists of all model point pair features and represents a mapping from the point pair feature space to the model, where similar features on the model are grouped together. Such representation allows using much sparser object and scene point clouds, resulting in very fast performance. Recognition is done locally using an efficient voting scheme on a reduced two-dimensional search space. We demonstrate the efficiency of our approach and show its high recognition performance in the case of noise, clutter and partial occlusions. Compared to state of the art approaches we achieve better recognition rates, and demonstrate that with a slight or even no sacrifice of the recognition performance our method is much faster then the current state of the art approaches.

/pdf/model-globally-match-locally-efficient-and-robust-3d-object-4hksf8mv37.pdf

Model globally, match locally: Efficient and robust 3D object recognition

An apparatus and method for image-based 3D photorealistic head modeling are provided. The method for creating a 3D photorealistic head model includes: detecting frontal and profile features in input frontal and profile images; generating a 3D head model by fitting a 3D genetic model using the detected facial features; generating a realistic texture from the input frontal and profile images; and mapping the texture onto the 3D head model. In the apparatus and method, data obtained using a relatively cheap device, such as a digital camera, can be processed in an automated manner, and satisfactory results can be obtained even from imperfect input data. In other words, facial features can be extracted in an automated manner, and a robust “human-quality” face analysis algorithm is used.

Method and apparatus for image-based photorealistic 3D face modeling

In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.

https://image.inha.ac.kr/paper/TPDS10PIK.pdf

Design and Performance Evaluation of Image Processing Algorithms on GPUs

A family of node structures for representing 3-dimensional objects using depth image are provided. These node structures can be adopted into MPEG-4 AFX for conventional polygonal 3D representations. Main formats of the family are DepthImage, PointTexture and OctreeImage. DepthImage represents an object by a union of its reference images and corresponding depth maps. PointTexture represents the object as a set of colored points parameterized by projection onto a regular 2D grid. OctreeImage converts the same data into hierarchical octree-structured voxel model, set of compact reference images and a tree of voxel-image correspondence indices. DepthImage and OctreeImage have animated versions, where reference images are replaced by videostreams. DIBR formats are very convenient for 3D model construction from 3D range-scanning and multiple source video data. MPEG-4 framework allows construction of a wide variety of representations from the main DIBR formats, providing flexible tools for effective work with 3D models. Compression of the DIBR formats is achieved by application of image (video) compression techniques to depth maps and reference images (videostreams).

Apparatus and method for depth image-based representation of 3-dimensional object

Depth estimation is essential in many light field applications. Numerous algorithms have been developed using a range of light field properties. However, conventional data costs fail when handling noisy scenes in which occlusion is present. To address this problem, we introduce a light field depth estimation method that is more robust against occlusion and less sensitive to noise. Two novel data costs are proposed, which are measured using the angular patch and refocus image, respectively. The constrained angular entropy cost (CAE) reduces the effects of the dominant occluder and noise in the angular patch, resulting in a low cost. The constrained adaptive defocus cost (CAD) provides a low cost in the occlusion region, while also maintaining robustness against noise. Integrating the two data costs is shown to significantly improve the occlusion and noise invariant capability. Cost volume filtering and graph cut optimization are applied to improve the accuracy of the depth map. Our experimental results confirm the robustness of the proposed method and demonstrate its ability to produce high-quality depth maps from a range of scenes. The proposed method outperforms other state-of-the-art light field depth estimation methods in both qualitative and quantitative evaluations.

Robust Light Field Depth Estimation Using Occlusion-Noise Aware Data Costs

Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.

/pdf/robust-light-field-depth-estimation-for-noisy-scene-with-cl5mym7azw.pdf

In Kyu Park

Papers

Method and apparatus for image-based photorealistic 3D face modeling

Design and Performance Evaluation of Image Processing Algorithms on GPUs

Apparatus and method for depth image-based representation of 3-dimensional object

Robust Light Field Depth Estimation Using Occlusion-Noise Aware Data Costs

Robust Light Field Depth Estimation for Noisy Scene with Occlusion