Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments.

In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.

The SAMS: Smartphone Addiction Management System and verification.

While the popularity of smartphones has given enormous convenience to our lives, their pathological use has created a new mental health concern among the community. Hence, intensive research is being conducted on the etiology and treatment of the condition. However, the traditional clinical approach based surveys and interviews has serious limitations: health professionals cannot perform continual assessment and intervention for the affected group and the subjectivity of assessment is questionable. To cope with these limitations, a comprehensive ICT (Information and Communications Technology) system called SAMS (Smartphone Addiction Management System) is developed for objective assessment and intervention. The SAMS system consists of an Android smartphone application and a web application server. The SAMS client monitors the user's application usage together with GPS location and Internet access location, and transmits the data to the SAMS server. The SAMS server stores the usage data and performs key statistical data analysis and usage intervention according to the clinicians' decision. To verify the reliability and efficacy of the developed system, a comparison study with survey-based screening with the K-SAS (Korean Smartphone Addiction Scale) as well as self-field trials is performed. The comparison study is done using usage data from 14 users who are 19 to 50 year old adults that left at least 1 week usage logs and completed the survey questionnaires. The field trial fully verified the accuracy of the time, location, and Internet access information in the usage measurement and the reliability of the system operation over more than 2 weeks. The comparison study showed that daily use count has a strong correlation with K-SAS scores, whereas daily use times do not strongly correlate for potentially addicted users. The correlation coefficients of count and times with total K-SAS score are CC = 0.62 and CC =0.07, respectively, and the t-test analysis for the contrast group of potential addicts and the values for the non-addicts were p = 0.047 and p = 0.507, respectively.

Comparing the Self-Report and Measured Smartphone Usage of College Students: A Pilot Study.

OBJECTIVE: Nowadays smartphone overuse has become a social and medical concern. For the diagnosis and treatment, clinicians use the self-report information, but the report data often does not match actual usage pattern. The paper examines the similarity and variance in smartphone usage patterns between the measured data and self-reported data. METHODS: Together with the self-reported data, the real usage log data is collected from 35 college students in a metropolitan region of Northeast Asia, using Android smartphone monitoring application developed by the authors. RESULTS: The unconscious users underestimate their usage time by 40%, in spite of 15% more use in the actual usage. Messengers are most-used application regardless of their self-report, and significant preference to SNS applications was observed in addict group. The actual hourly pattern is consistent with the reported one. College students use more in the afternoon, when they have more free time and cannot use PCs. No significant difference in hourly pattern is observed between the measured and self-report. CONCLUSION: The result shows there are significant cognitive bias in actual usage patterns exists in self report of smartphone addictions. Clinicians are recommended to utilize measurement tools in diagnosis and treatment of smartphone overusing subjects.

Comparison of risk and protective factors associated with smartphone addiction and Internet addiction.

BACKGROUND AND AIMS: Smartphone addiction is a recent concern that has resulted from the dramatic increase in worldwide smartphone use. This study assessed the risk and protective factors associated with smartphone addiction in college students and compared these factors to those linked to Internet addiction. METHODS: College students (N = 448) in South Korea completed the Smartphone Addiction Scale, the Young's Internet Addiction Test, the Alcohol Use Disorders Identification Test, the Beck Depression Inventory I, the State-Trait Anxiety Inventory (Trait Version), the Character Strengths Test, and the Connor-Davidson Resilience Scale. The data were analyzed using multiple linear regression analyses. RESULTS: The risk factors for smartphone addiction were female gender, Internet use, alcohol use, and anxiety, while the protective factors were depression and temperance. In contrast, the risk factors for Internet addiction were male gender, smartphone use, anxiety, and wisdom/knowledge, while the protective factor was courage. Discussion These differences may result from unique features of smartphones, such as high availability and primary use as a tool for interpersonal relationships. CONCLUSIONS: Our findings will aid clinicians in distinguishing between predictive factors for smartphone and Internet addiction and can consequently be utilized in the prevention and treatment of smartphone addiction.

Latent class analysis on internet and smartphone addiction in college students.

PURPOSE: This study aimed to classify distinct subgroups of people who use both smartphone and the internet based on addiction severity levels. Additionally, how the classified groups differed in terms of sex and psychosocial traits was examined. METHODS: A total of 448 university students (178 males and 270 females) in Korea participated. The participants were given a set of questionnaires examining the severity of their internet and smartphone addictions, their mood, their anxiety, and their personality. Latent class analysis and ANOVA (analysis of variance) were the statistical methods used. RESULTS: Significant differences between males and females were found for most of the variables (all <0.05). Specifically, in terms of internet usage, males were more addicted than females (P<0.05); however, regarding smartphone, this pattern was reversed (P<0.001). Due to these observed differences, classifications of the subjects into subgroups based on internet and smartphone addiction were performed separately for each sex. Each sex showed clear patterns with the three-class model based on likelihood level of internet and smartphone addiction (P<0.001). A common trend for psychosocial trait factors was found for both sexes: anxiety levels and neurotic personality traits increased with addiction severity levels (all P<0.001). However, Lie dimension was inversely related to the addiction severity levels (all P<0.01). CONCLUSION: Through the latent classification process, this study identified three distinct internet and smartphone user groups in each sex. Moreover, psychosocial traits that differed in terms of addiction severity levels were also examined. It is expected that these results should aid the understanding of traits of internet and smartphone addiction and facilitate further study in this field.