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

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

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Color image segmentation: advances and prospects

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.

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Re-ranking Person Re-identification with k-Reciprocal Encoding

Determination of appropriate fishing spots is one of the most important activities in the fishing industry. Inspired by the approach followed by fishermen to determine fishing spots, this paper presents a new machine-learning method for uncovering oceanographic patterns related to good fishing spots. Our method uses a sea temperature map as the input, extracts sea temperature patterns from arbitrary points on the map, and evaluates whether the patterns correspond to good fishing spots by using two machine learning techniques; one-class support vector machine (SVM) and spectral clustering. We evaluated the efficiency of our method using fishery data on neon flying squid.

Fishing Spot Prediction by Sea Temperature Pattern Learning

This study designed a distance education support system (DESS) by integrating face-recognition techniques and blog system. The DESS system was then applied in an international distance course conducted between two universities of Taiwan and Japan with an evaluation study for investigating the usability and effectiveness of the system in supporting distance education. Findings suggested that the system is helpful and clear for representing students' learning profiles as individual, within group and in class meeting. Besides, students reported that they are comfortable about using it in class, especially for improving interaction between instructors and students. However, students do not prefer instructors to use it for assessment purpose. We concluded that this DESS system is has potential in improving interaction and reducing students' feeling of isolation in distance course. Also we provided some suggestions in future studies and system development.

Integrating face recognition techniques with blog as a distance education support system (DESS) in international distance learning

In recent years, networked appliances, which are the home appliances with a networking function, have been opening a new gate for home ubiquitous environments and services implemented on them. Although it is important to regulate data formats, protocols and so for interoperability, the service scenarios portrayed by just connecting stand-alone appliances are limited. For users, it would be useful to construct their own service dynamically by combining the individual functions in the appliances. The UKARI-Core, a communication middleware platform for networked appliances, has been developed to make the flexible and dynamical service construction based on the individual functions a reality. This paper describes a plan of extension of the UKARI-Core by adding XML descriptions for sensor functions, a service scenario description framework and a user accessibility control mechanism to the functions. The extended middleware platform is called the UKARI-Kernel.

Distributed and Cooperative Service Platform "UKARI-Kernel"Plan for networked Appliances

あらまし 個人の体形を表現する個人体形モデルを,あらかじめ用意した原形モデルとその個人のシルエット 画像から変形によって作成する手法を考案した.個人体形モデルは,ユーザが仮想空間で試着を行える仮想試着 システムにおいて使用することを想定している.現在,このような目的のための個人体形モデルは,大掛りな 3 次元計測装置を用いるか,多数の体形モデルを用意して一番近い形状のものを使用する.本手法では,変形によ り個人体形モデルを生成するため,精度や,あらかじめ必要とする体形モデル数が少ないという点で優れている. 原形モデルからの普遍的な形状特徴を維持しつつシルエット画像からの個人体形の形状情報を反映するため,シ ルエット画像と変形結果との間に輪郭一致制約を,原形モデルと変形結果の間に局所相似制約を与えて変形する. 局所相似制約とは,人体の各部分の局所的な形状は,個人間で相似形であるという仮定を適用するものである. 本手法を,実在の女性の 3次元計測データから得たシルエット画像と原形モデルに適用し,各個人の体形差と比 較して小さい誤差で結果が得られることを確認した. キーワ ドー 体形モデル,3次元変形,シルエット画像,局所相似制約,仮想試着室

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A Deformation Method of Body Shape Models Based on Silhouette Images of Persons For Virtual Fitting Rooms

A 3D model, particularly a 3D shape model, takes an important role in both CV and CG in the context of image media application such as VR. Here, 3D model centered framework is proposed in which the importance of 3D model processing is emphasized on. In our framework, images are considered as constraint of the 3D model processing, which gives clear strategy of taking an image in the real world. The research based on the framework are described as examples to show the advantages of our framework.

Michihiko Minoh

Papers

Fishing Spot Prediction by Sea Temperature Pattern Learning

Integrating face recognition techniques with blog as a distance education support system (DESS) in international distance learning

Distributed and Cooperative Service Platform "UKARI-Kernel"Plan for networked Appliances

A Deformation Method of Body Shape Models Based on Silhouette Images of Persons For Virtual Fitting Rooms

3D Model Centered Framework for CV and VR