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.

/pdf/re-ranking-person-re-identification-with-k-reciprocal-3erzmilgv1.pdf

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

A connection diagram understanding system using a facsimile as its input device has been designed and implemented. The principle of this system is described and examples of its application to some hand-sketched diagrams are shown. In order to reduce processing time, the procedure is started by only accessing the pixels located on the borders of certain meshes to detect characteristic patterns and make a control map. Background judgment and long straight line segment extraction are executed on the control map. Other complicated areas, which are usually a small portion of the whole diagram area, are also indicated by special labels on the map and then processed by a detailed procedure which scans every pixel at these areas. Graph descriptions of diagrams are employed at different steps of the hierarchical understanding. Problems of data compression, diagram retrieval, and diagram editing are discussed.

Efficient diagram understanding with characteristic pattern detection

This paper describes the implementation of blog system in an international distance course between Japan and Taiwan. In the study, blog was used as a tool to encourage students' reflective learning and communication. Findings suggested that blog was effective for students to document their learning, share experience and knowledge, have direct interaction with peers especially internationally. Students preferred blog over the LMS where this course builds an course Web site and they proved to use blog in developing their e-learning experience. We proposed the use of blog could lead to a technologically enhanced support for instructional strategy that serves as an informative model for other Web-assisted international courses

http://www.mm.media.kyoto-u.ac.jp/old/research/doc/711/344lin.pdf

Blog as a Tool to Develop e-Learning Experience in an International Distance Course

Recently both face recognition and body-based person re-identification have been extended from single-image based scenarios to video-based or even more generally image-set based problems. Set-based recognition brings new research and application opportunities while at the same time raises great modeling and optimization challenges. How to make the best use of the available multiple samples for each individual while at the same time not be disturbed by the great within-set variations is considered by us to be the major issue. Due to the difficulty of designing a global optimal learning model, most existing solutions are still based on unsupervised matching, which can be further categorized into three groups: a) set-based signature generation, b) direct set-to-set matching, and c) between-set distance finding. The first two count on good feature representation while the third explores data set structure and set-based distance measurement. The main shortage of them is the lack of learning-based discrimination ability. In this paper, we propose a set-based discriminative ranking model (SBDR), which iterates between set-to-set distance finding and discriminative feature space projection to achieve simultaneous optimization of these two. Extensive experiments on widely-used face recognition and person re-identification datasets not only demonstrate the superiority of our approach, but also shed some light on its properties and application domain.

/pdf/set-based-discriminative-ranking-for-recognition-rvwombkatc.pdf

Set based discriminative ranking for recognition

Human communication is fostered in environments of regional communities and cultures and in different languages. Cultures are rooted in their unique histories. Communication media have been developed to circulate these cultural characteristics. The theme of our research is "Cultural Computing", which means the translation of cultures using scientific methods representing essential aspects of Japanese culture [1]. We study the reproduction of a traditional Japanese Haiku by computer. Our system can abstract an essence of human emotions and thoughts into a Haiku, a Japanese minimal poem form. A user chooses arbitrary phrases from a chapter of the essay "1000 Books and 1000 Nights" [2]. Using the phrases chosen by the user, our system generates the Haiku which includes the essence of these words.

/pdf/hitch-haiku-an-interactive-supporting-system-for-composing-51w9rxbka0.pdf

Hitch Haiku: An Interactive Supporting System for Composing Haiku Poem

In this paper, we present a public dataset for tracking people across multiple cameras. This dataset consists of more than 22,000 images of 24 people which are captured by 16 cameras installed in a shopping mall “Shinpuh-kan”. All images are manually cropped and resized to 48× 128 pixels, grouped into tracklets and added annotation. The number of tracklets of each person is 86. This dataset contains multiple tracklets in different directions for each person within a camera. To show the difficulty of the dataset, we evaluate it with some state-of-the-art methods.

http://www.mm.media.kyoto-u.ac.jp/old/research/doc/1168/kawanishi.pdf

Michihiko Minoh

Papers

Efficient diagram understanding with characteristic pattern detection

Blog as a Tool to Develop e-Learning Experience in an International Distance Course

Set based discriminative ranking for recognition

Hitch Haiku: An Interactive Supporting System for Composing Haiku Poem

Shinpuhkan2014: A Multi-Camera Pedestrian Dataset for Tracking People across Multiple Cameras