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

Automatic lecture archiving system for recording bilateral communication by shooting students attending the lecture

This article discusses face translation (FT), which replaces the user's face with an expression by a different synthetic face, such as the face of an avatar or a cartoon character, with the same expression In order to realize FT, we have to establish a criterion to define which expression on the synthetic face is the “same” expression as the user wears on his/her face Different from previous work, it is proposed in this article to adopt the user’s subjective viewpoint for this criterion The user ’s viewpoint is acquired online through the interaction with the user When the user is not satisfied with the synthetic facial expression given as the result of FT by the system, the user corrects it by a graphical user interface (GUI) The system obtains examples of the FT from the user's subjective viewpoint every time the user corrects the results of translation With increase of examples, the result of FT by the system gradually comes to reflect the user’s subjective viewpoint, so that the user does not need to correct the result

http://www.mm.media.kyoto-u.ac.jp/old/research/doc/425/InProceedings.pdf

User-oriented face translation based on examples obtained by user interaction

This paper focuses on live video distribution by video streaming technology. In general, it is an important challenge for video streaming to preserve spatial, temporal, continuous, and real-time quality of video. Pre-roll streaming, which buffers a certain amount of data before video playback, can retain spatial and temporal quality without degradation of continuous quality, while the problem about real-time quality remains because it is difficult to know data amount of video filmed in future on live video distribution. Hence, we propose the method to apply pre-roll sequentially for segmented video data divided by points where continuity of video content breaks, and show it can meet spatial, temporal and continuous quality with few degradation of real-time quality. In the experiment, we show the proposed method met other three qualities with few degradation of real-time quality for lecture video streaming.

Pre-roll Control of Live Video Streaming Based on Separateness

We propose a method for synthesizing an undistorted highresolution image of a lecture room. A certain degree of high-resolution videos can be captured using commercially available high-resolution cameras. However, the resolution of objects captured by these cameras is not enough for viewers of a lecture video. To get sufficient high-resolution lecture videos, we synthesize multiple images captured by low cost cameras using a planar perspective projection. However, lecturers are distorted by the projection because they are not planar objects and do not exist on planar objects in a lecture room. To get an undistorted figure of a lecturer, we introduce a fixed-viewpoint projection using a lecturer tracking camera. As a result, we could get more high-resolution images including an undistorted figure of a lecturer than ones captured by a standard high-resolution camera.

http://www.mm.media.kyoto-u.ac.jp/old/research/doc/926/nishiguchi_icme2009.pdf

Synthesizing a high-resolution image of a lecture room using lecturer tracking camera and planar object capturing camera

Most shape and reflectance acquisition methods use the light reflected from objects' surfaces. When the reflection cannot be observed, e.g., when the object's surface is black matte or highly specular, its shape and reflectance are difficult to acquire. In this paper, we propose a method that can measure shape and reflectance with another approach. Our method involves the use of the scattering of reflected light in a participating media instead of using only the light reflection. We place the target in a participating medium and focus a laser beam on it. Cameras can observe the scattering of reflected light toward all reflection angles even if they can only observe light reflected toward the cameras. Experimental results showed that our method can acquire the shape and reflectance of various surfaces.

Michihiko Minoh

Papers

Automatic lecture archiving system for recording bilateral communication by shooting students attending the lecture

User-oriented face translation based on examples obtained by user interaction

Pre-roll Control of Live Video Streaming Based on Separateness

Synthesizing a high-resolution image of a lecture room using lecturer tracking camera and planar object capturing camera

Shape and Reflectance from Scattering in Participating Media