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

Belief Network based Disambiguation of Object Reference in Spoken Dialogue System

A simple and effective method is proposed for object recognition via collaborative representation with ridge regression Different from existing sparse representation and collaborative representation based approaches, the proposal does not need extensive training samples for each testing class and it is robust to localization errors and large within-class variations, thus being applicable to various real-world object recognition tasks instead of handling only the well-controlled face recognition problem Its discriminative power is explored from a third-party dataset which can be different from the training and testing datasets, therefore, it enables using an existing dictionary for testing new data without time-consuming data annotation and model re-training As an example, the proposal is extensively tested on the representative and very challenging task of person re-identification, defining novel state-of-the-art results on widely adopted benchmark datasets using only simple and common features

http://www.mm.media.kyoto-u.ac.jp/old/research/doc/1098/12C04_TPCR_ICPR_w_correction.pdf

Robust object recognition via third-party collaborative representation

We develop a method for combined object detection and segmentation in natural scene. In our approach segmentation and detection are considered as two faces of the same coin that should be combined into a single framework. There are two main steps in our strategy. First we focus on the learning of a visual vocabulary that efficiently encompasses objects' appearance, spatial configuration and underlying segmentation. This vocabulary is used within a Hough voting framework to produces object's configuration. The second step consists in searching for valid objects' configurations by interpreting and scoring them in terms of both detection and segmentation. This allows us to prune false detections and hallucinated object-like segmentation. Experiments show the advantage of the combined approach and the improvements over recent related methods.

/pdf/combined-object-detection-and-segmentation-1ayh5bmypt.pdf

Combined Object Detection and Segmentation

An advanced multimedia lecture recording system is presented in this paper. The purpose of our system is to capture multimodal information that can be received only when lectures are being held in a classroom where a teacher and students share the same time and space. The system captures not only hand-writings and slide switching intervals but also audio and video of the people with their spatial location information. These recorded media will be served to users in distance learning process. We designed the system so that it does not interfere with classes. It can archive lectures while they hold classes on regular basis and generate a multimedia archive automatically. Teachers are neither asked to remain at a certain place nor wired with devices they would have to put on. We have implemented our approach and our lecture archive system currently works for six classes per week, since October, 2002.

/pdf/carmul-concurrent-automatic-recording-for-multimedia-lecture-1sk78oaukz.pdf

CARMUL: concurrent automatic recording for multimedia lecture

We propose a method for recognizing ingredients in food preparing activity. The research for object recognition mainly focuses on only visual information; however, ingredients are difficult to recognize only by visual information because of their limited color variations and larger within-class difference than inter-class difference in shapes. In this paper, we propose a method that involves some physical signals obtained in a cutting process by attaching load and sound sensors to the chopping board. The load may depend on an ingredient's hardness. The sound produced when a knife passes through an ingredient reflects the structure of the ingredient. Hence, these signals are expected to facilitate more precise recognition. We confirmed the effectiveness of the integration of the three modalities (visual, auditory, and load) through experiments in which the developed method was applied to 23 classes of ingredients.

http://www.mm.media.kyoto-u.ac.jp/old/research/doc/1081/cea001_hashimoto.pdf

Michihiko Minoh

Papers

Belief Network based Disambiguation of Object Reference in Spoken Dialogue System

Robust object recognition via third-party collaborative representation

Combined Object Detection and Segmentation

CARMUL: concurrent automatic recording for multimedia lecture

Recognizing ingredients at cutting process by integrating multimodal features