nils-herrmann/clean_scirepeval huggingface dataset:
Temporal spotting of human actions from videos containing actor's unintentional motions

This paper proposes a method for temporal action spotting: the temporal segmentation and classification of human actions in videos. Naturally performed human actions often involve actor's unintentional motions. These unintentional motions yield false visual evidences in the videos, which are not related to the performed actions and degrade the performance of temporal action spotting. To deal with this problem, our proposed method empolys a voting-based approach in which the temporal relation between each action and its visual evidence is probabilistically modeled as a voting score function. Due to the approach, our method can robustly spot the target actions even when the actions involve several unintentional motions, because the effect of the false visual evidences yielded by the unintentional motions can be canceled by other visual evidences observed with the target actions. Experimental results showed that the proposed method is highly robust to the unintentional motions.
TV Viewing Interval Estimation for Personal Preference Acquisition

The importance of personalized information services has been increasing. Description of personal preferences needs to be prepared beforehand to realize such services. We propose a system for automatically acquiring personal preferences from TV viewer's behaviors. Considering "when" a viewer is watching TV is highly related to the viewer's preferences, we focus on estimating the time interval during which a pre-registered viewer is watching TV. In this paper, we firstly describe the outline of the personal preference acquisition system, and address a method for estimating the TV viewing intervals based on the appearance of frontal faces. Experiments resulted in a precision rate of 97.1% and a recall rate of 70.6% on average for TV viewing interval estimation
Media information processing in documents-generation of manuals of mechanical parts assembling

The authors view a document as a multi-modal media in the sense that it presents information by using figures and/or images as well as text. By showing examples of media conversion and media integration, the authors discuss the essential processing techniques of media information, especially document media information. First, media conversion is briefly addressed by an example of converting a graph diagram in a document to either an alternative graph diagram or a table. The main topic is the generation of a manual of a mechanical parts assembly as an example of media integration in a document. Thereby, necessary input information is presented for generating the manual.
Real-time privacy protection system for social videos using intentionally-captured persons detection

Most social videos, which are uploaded and shared through social networking services (SNSs), e.g., YouTube and Facebook, contain not only intentionally-captured persons (ICPs) but also non-ICPs who are unexpectedly framed in, such as passers-by. Sharing such social videos may infringe on the non-ICPs' privacy but not on the ICPs' in many cases; however, existing systems for video privacy protection simply obscure persons without distinguishing ICPs from non-ICPs. This naive obscuration may spoil the videos. Since this is a critical problem especially for social videos, in this paper, we propose a novel system for automatically generating privacy-protected videos in real-time. Our system localizes ICPs and non-ICPs using ICP detection leveraging the spatial and temporal consistency of ICPs/non-ICPs and obscures the non-ICPs. We have experimentally evaluated the performance of ICP detection and demonstrated the applicability of our system.
Generation of sketch map image and its instructions to support the understanding of geographical information

In this paper, we propose a method of generating a sketch map drawing and its instructions in order to support the route understanding of a human. Both are given from a structured data, called the road network, which is a graph augmented by the attributes of roads and crossings. In generating a sketch map drawing, the pictorial information about the shape and the direction of roads is modified. The sketch map drawing can be generated at any simplification level by controlling a parameter called simpleness. The instructions corresponding to the sketch map are produced by assigning appropriate terms into sentence templates. We verified that favorable results are obtained by this method.
Learning Personal Preference From Viewer's Operations for Browsing and Its Application to Baseball Video Retrieval and Summarization

Personalization is one of the most important mechanisms to make multimedia systems easy to use. In video applications, its embodiment is to tailor video contents for a particular viewer. For this purpose, we are now developing a system of retrieving and browsing video segments, called video portal with personalization (VIPP). VIPP is characterized by 1) supporting the viewer's access to video contents and making a summarized video clip by taking his/her preference into account and 2) acquiring the viewer's profile from his/her operations automatically. In this paper, we propose a method for learning to personalize from the viewer's operations such as retrieval and browsing, as well as describe how the personalized retrieval and summarization of videos can be realized. From the experiments, we clarify the effect of personalization on retrieval and summarization of baseball videos on VIPP.
Mining temporal information and web-casting text for automatic sports event detection

In this paper, the generic framework for automatically detecting event based on Allen temporal algebra and external text information support is presented. The motivation of the proposed method is (1) to relax the need of domain knowledge that requires significant human interference; and (2) to take into account the temporal information that has been paid less attention though it is critical to convey event meaning. In order to solve two these problems, in the proposed method, the temporal information is captured by presenting events as the temporal sequences using a lexicon of Allen-based non-ambiguous temporal patterns. These sequences are then used to mine temporal patterns with web-casting text supports by using technique of mining class association rules. Then, the results of previous steps are tailored to build the event detector. Thorough experimental results and comparisons that are carried on more than 30 hours of soccer video corpus captured at different broadcasters and conditions demonstrates that the proposed method meets two aforementioned motivations with high efficiency, effectiveness, and robustness.
Interactive approach to the extraction of logical structures from unformatted document images using a sub-structure model

Describes a new document analysis method for unformatted documents such as advertisements or catalogs. Conventional model-based approaches to the extraction of logical structures are hard to apply to advertisements or catalogs, because a model of a page can't be defined. However, these kinds of documents have similar configurations of the regions that represent each product, where a local model of a local layout and logical structures can be defined. This model, which we call a sub-structure model, can be used as a template to extract the logical structures from other regions that represent the same kinds of products. In proposed system, a sub-structure model is captured through an interactive process with a user. The system was tested on advertisements in Japanese computer magazines and the experiments show promising results.
Non-monotonic inference system handling knowledge allowing classified exceptions

We propose a nonmonotonic formalism for knowledge handling allowing exceptions, the Exc-Representation (ER), and its goal-directed proof procedure, the SLD-EXC resolution, for the nonmonotonic inference system, NISE. ER always provides a unique extension, which is a set of conclusions, and makes tractable the membership problem in nonmonotonic reasoning by introducing the conditional facts, and classifying exceptions into two types based on the relationships among rules.
Video portal for a media space of structured video streams

The necessity for a video portal which supports an access to a specific video or its part from a huge video media space consisting of a large number of structured video streams is increasing. The video portal for a media space can be defined as a system which helps us to access videos through various kinds of media segments such as image, text, audio and video segments. In order to construct the video portal, we deal with structured video with metadata. Our system defines the structure of video in terms of document type definition (DTD), and describes the metadata of video with XML according to the DTD. We propose the video portal for structured video streams for the sports domain, and verify its usefulness based on a prototype system's performance.
Performance Analysis of Anonymous Communication System 3-Mode Net

This paper analyzes the performance of 3-Mode Net (3MN), which is a new anonymous communication system proposed. In particular, we give the probability distributions of the number of relay nodes as well as the number of encryption required for communications. The expectations and variances of these two numbers are also given. These results enable us to grasp the influence of the probabilities of mode selections in 3MN. Numerical examples are also presented to illustrate these results.
Extracting actors, actions and events from sports video -a fundamental approach to story tracking

To effectively deal with the vast amount of videos, we need to construct a content-based representation for each video. As a step towards this goal, this paper proposes a method to automatically generate the semantical annotations for a sports video by integrating the text(c1osed-caption) and image stream. we first segment the text data and extract segments which are meaningful to grasp the story of the video, then extract the actors, the actions and the events of each scene which are useful for information retrieval by using the linguistic cues and the domain knowledge. We also segment the image stream so that each segment can associate with each text segment extracted above by using the image cues. Finally we can annotate the video by associating the text segments with the image segments. Some experimental results are presented and discussed in this paper.
Discriminating Intended Human Objects in Consumer Videos

In a consumer video, there are not only intended objects, which are intentionally captured by the camcorder user, but also unintended objects, which are accidentally framed-in. Since the intended objects are essential to present what the camcorder user wants to express in the video, discriminating the intended objects from the unintended objects are beneficial for many applications, e.g., video summarization, privacy protection, and so forth. In this paper, focusing on human objects, we propose a method for discriminating the intended human objects from the unintended human objects. We evaluated the proposed method using 10 videos captured by 3 camcorder users. The results demonstrate that the proposed method successfully discriminates the intended human objects with 0.45 of recall and 0.80 of precision.
Recognition of social dancing from auditory and visual information

We discuss the recognition of social dancing from a real image sequence and an acoustic signal of music. Since social dancing involves somewhat complicated movements by multiple humans, it is difficult to recover a detailed description of each dancer's posture by the conventional approach based on matching of an articulated human body model to each frame of an image sequence. Using the results of the recognition process for annotating a video of social dancing, we focus on the bare minimum of motion description. We show that the motion description can be acquired from a real image sequence and an acoustic signal by simple familiar techniques of computer vision and music information processing.
Dynamic feature detection using virtual correction and camera oscillations

Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of a static environment. In this paper, we relax the severe assumption of a static scene to allow for the detection and deletion of dynamic points. A new "virtual correction" method is introduced which serves to detect the dynamic points by checking the re-projection error of the points before and after the virtual measurement update. It can also recover the erroneously excluded useful features, particularly the distant points which may be deleted because of the change in its position after new measurement observation. Deliberate camera oscillations are also used to improve the VSLAM accuracy and the camera observability. The simulation results showed the effectiveness of the virtual correction when combined with camera oscillation in recovering the misclassified features and detecting the dynamic features even in difficult scenarios.
Extracting Real-World Observations from Microblog

Since a large number of users of various social networking services post what they observe around themselves, what is going on around the world can be known in real time by extracting such real-world observations. Especially, observations covering miscellaneous areas of interest are posted to Twitter as short text messages. Our goal is to extract such observations to better understand the current situations of the real world. Since the observations at specific locations are often described with words representing the observed locations or events, the locations or events all over the world can be discovered by finding such local words which are uniquely used at specific locations or times. Then, parts of their observations can be obtained by using the local words to define the semantics of each local word. Finally, the observations which do not contain the local words but are related to the locations or events can be extracted based on their semantic relevancy to each local word.
Generating of sketch map drawing from vectorized image

This paper presents a method of generating a sketch drawing from geographical map images with vector representation. This method consists of two stages: generation of a road network from a vectorized image and generation of a sketch map drawing based on the road network. The road network is fundamental data for any applications, represented as a graph structure that is augmented with related attributes about roads and crossings. The characteristic of this method is to introduce a parameter, called roughness, in order to control sketch map generation. Because of the roughness, we can obtain a variety of sketch map drawings according to user's requirements. We experimentally verified that the proposed method is effective and that the roughness is valid as a psychological measure.<<ETX>>
Steganography using Sensor Noise and Linear Prediction Synthesis Filter

This paper presents a new approach utilizing the sensor's pattern noise and linear prediction synthesis filter for steganography. The pattern noise is extracted from the images using denoising filter (for example wavelet-based filter). Then the approach introduces the linear prediction synthesis filter, whose parameters are derived from the extracted noise. After being filtered by such a filter, the secret message can be embedded by adapting the characteristics of the sensor's pattern noise. As a result, the embedding process violate little of the natural image statistics, and hence the detectability of steganalytic method is noticeably decreased. The experimental results prove the effectiveness of the new approach.
Intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video

This paper presents intermodal collaboration: a strategy for semantic content analysis for broadcasted sports video. The broadcasted video can be viewed as a set of multimodal streams such as visual, auditory, text (closed caption) and graphics streams. Collaborative analysis for the multimodal streams is achieved based on temporal dependency between their streams, in order to improve the reliability and efficiency for semantic content analysis such as extracting highlight scenes from sports video and automatically generating annotations of specific scenes. A couple of case studies are shown to experimentally confirm the effectiveness of intermodal collaboration.
A discrete wavelet transform based recoverable image processing for privacy protection

This paper presents a novel scheme of a recoverable image processing for privacy protection in real-time video surveillance system. The privacy information is embedded into the video using information hiding. Thus, the original privacy information can be recoverable with secrete key if necessary. In the proposed system, the privacy information is defined as information of objects that consist of detailed data to recover the original image of objects. The scheme is based on discrete wavelet transform (DWT) which is used for generating privacy-protected low resolution image, as well as the high resolution data including privacy information. An amplitude modulo modulation based information hiding scheme is used to hide the privacy information. Experimental results have shown that the proposed system can reduce the amount of the privacy information significantly, and allows the privacy information to be revealed after being embedded in real time.
Extracting characters and character lines in multi-agent scheme

In this paper, we present COCE (COordinative Character Extractor), a new method for extracting printed Japanese characters from an unformatted document image. This research aims to exploit knowledge independent of the layouts. COCE is based on a multiagent scheme where each agent is assigned to a single character line and tries to extract characters by making use of the knowledge about features of a character line as well as shapes and arrangement of characters. Moreover, the agents communicate with each other to keep consistency between their tasks. We have favourable results for the effectiveness of this method.
Extracting Context Information from Microblog Based on Analysis of Online Reviews

Recommender systems automatically determine suitable items for users. Although preferences or context of users have been widely utilized in order to evaluate the suitability of the items for users, the surrounding context have little been considered. Focusing on that many ordinary human beings voluntarily report their observations of the current situation of the world to microblogs, this paper proposes a recommender system which not only recommends suitable restaurants to users based on their preferences and context but also provides the surrounding context information reported to microblogs which will further affect the users' restaurant selection behaviors. In particular, considering that such influential surrounding context information in microblogs includes keywords related to restaurant assessment, we propose a method for automatically determining the keywords to extract the context information by analyzing online reviews, which have been gathered also from ordinary human beings over a long period of time. The experiments by using Twitter as microblogs and Tabelog, a popular online restaurant review site in Japan, to obtain online reviews, indicated that the influential context information can be extracted from Twitter with the highest recall of 93.3% by using the area-related keywords. Additionally using the restaurant-related keywords was effective in removing irrelevant information obtaining the precision of 15.9%.
Detecting intended human objects in human-captured videos

When people take videos, they always want to capture intended objects, which are essential for presenting what they want to express in their videos, and to share the intended objects with others. The concept of intended objects provide a novel perspective for video content analysis, and detecting intended objects may be beneficial for wide range of applications such as video understanding and semantics interpretation, video summarization, video adaptation, video privacy protection, and so on. In this paper, we focus on a particular type of intended objects, i.e., intended human objects, and an interesting method is developed for detecting intended human objects automatically from human-captured videos. We also investigate the correlation between intended human objects and visual attention. Our experimental results indicate that our method can successfully detect the intended human objects.
Automatic personal preference acquisition from TV viewer’s behaviors

The demand for information services considering personal preferences is increasing. In this paper, we propose a system for automatically acquiring personal preferences from TV viewerpsilas behaviors. Our system firstly extracts intervals of interest and estimates the interest degree for each extracted interval based on the temporal patterns in facial changes by Hidden Markov Models (HMMs). Then, the viewer profile is created by associating the interest degrees with the content information described in the metadata of the watched program. Experimental results have shown that the proposed methods are able to correctly estimate interest degrees for extracted intervals with a precision rate of 73.1% and a recall rate of 68.8%, and that the created viewer profiles are comparable to the actual preferences of each viewer.
Anonymous communication system using probabilistic choice of actions and multiple loopbacks

This paper proposes a new anonymous communication system using probabilistic choice of actions and multiple loopbacks. Our system can provide both sender anonymity and receiver anonymity. Our system also decreases the computation load of each relay node, because there exist no encryption and decryption processes in our system. Applying an analysis method in an anonymous communication system called 3-Mode Net, we evaluate the number of relay nodes and sender anonymity.
Motion estimation and detection of complex object by analyzing resampled movements of parts

A moving object that has many complex moving parts is very hard to detect and its motion is not easy to estimate. We present a new technique for motion estimation and detection of moving complex objects by analyzing the resampled motions of parts of the objects. A Kalman filter is used to track all resampled movements and the tracked routes are classified into groups that share the same fundamental movements. Our simulations show that recall of motion estimation and detection is approximately 0.8, while the computation drops exponentially.
LMI-based stability condition for 2D discrete systems described by the Fornasini-Marchesini second model

This paper presents a stability criterion for 2D discrete systems described by the Fornasini-Marchesini second model. The method presented in this paper is based on linear matrix inequalities (LMI), and hence, it is computationally tractable. In deriving the method, finite-order Fourier series approximation of the solution for frequency-dependent LMI (FDLMI), and the properties of quadratic form representation of finite-order Fourier series play key roles. From the view point of the proposed method, the existing LMI-based condition can be regarded as the one which is obtained by Fourier series approximation of order zero, and thus, it is expected that the proposed method leads to less conservative results. This is illustrated by a numerical example.