284 research outputs found
Mean-shift background image modelling
Background modelling is widely used in computer vision for the detection of foreground objects in a frame sequence. The more accurate the background model, the more correct is the detection of the foreground objects. In this paper, we present an approach to background modelling based on a mean-shift procedure. The mean shift vector convergence properties enable the system to achieve reliable background modelling. In addition, histogram-based computation and the new concept of local basins of attraction allow us to meet the stringent real-time requirements of video processing. Β©2004 IEEE
Face and Body gesture recognition for a vision-based multimodal analyser
users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. In this paper, we present a survey of research conducted on face and body gesture and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly in this paper, we present a framework for a vision-based multimodal analyzer that combines face and body gesture and further discuss relevant issues
Face and body gesture analysis for multimodal HCI
Humans use their faces, hands and body as an integral part of their communication with others. For the computer to interact intelligently with human users, computers should be able to recognize emotions, by analyzing the human's affective state, physiology and behavior. Multimodal interfaces allow humans to interact with machines through multiple modalities such as speech, facial expression, gesture, and gaze. In this paper, we present an overview of research conducted on face and body gesture analysis and recognition. In order to make human-computer interfaces truly natural, we need to develop technology that tracks human movement, body behavior and facial expression, and interprets these movements in an affective way. Accordingly, in this paper we present a vision-based framework that combines face and body gesture for multimodal HCI. Β© Springer-Verlag Berlin Heidelberg 2004
Recommended from our members
Core-periphery or decentralized? Topological shifts of specialized information on Twitter
In this paper we investigate shifts in Twitter network topology resulting from the type of information being shared. We identified communities matching areas of agricultural expertise and measured the core-periphery centralization of network formations resulting from users sharing generic versus specialized information. We found that centralization increases when specialized information is shared and that the network adopts decentralized formations as conversations become more generic. The results are consistent with classical diffusion models positing that specialized information comes with greater centralization, but they also show that users favor decentralized formations, which can foster community cohesion, when spreading specialized information is secondary
An edge-based approach for robust foreground detection
Foreground segmentation is an essential task in many image processing applications and a commonly used approach to obtain foreground objects from the background. Many techniques exist, but due to shadows and changes in illumination the segmentation of foreground objects from the background remains challenging. In this paper, we present a powerful framework for detections of moving objects in real-time video processing applications under various lighting changes. The novel approach is based on a combination of edge detection and recursive smoothing techniques.We use edge dependencies as statistical features of foreground and background regions and define the foreground as regions containing moving edges. The background is described by short- and long-term estimates. Experiments prove the robustness of our method in the presence of lighting changes in sequences compared to other widely used background subtraction techniques
Behavioural and neural markers of tactile sensory processing in infants at elevated likelihood of autism spectrum disorder and/or attention deficit hyperactivity disorder.
BACKGROUNDS: Atypicalities in tactile processing are reported in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) but it remains unknown if they precede and associate with the traits of these disorders emerging in childhood. We investigated behavioural and neural markers of tactile sensory processing in infants at elevated likelihood of ASD and/or ADHD compared to infants at typical likelihood of the disorders. Further, we assessed the specificity of associations between infant markers and later ASD or ADHD traits. METHODS: Ninety-one 10-month-old infants participated in the study (n = 44 infants at elevated likelihood of ASD; n = 20 infants at elevated likelihood of ADHD; n = 9 infants at elevated likelihood of ASD and ADHD; n = 18 infants at typical likelihood of the disorders). Behavioural and EEG responses to pairs of tactile stimuli were experimentally recorded and concurrent parental reports of tactile responsiveness were collected. ASD and ADHD traits were measured at 24βmonths through standardized assessment (ADOS-2) and parental report (ECBQ), respectively. RESULTS: There was no effect of infants' likelihood status on behavioural markers of tactile sensory processing. Conversely, increased ASD likelihood associated with reduced neural repetition suppression to tactile input. Reduced neural repetition suppression at 10βmonths significantly predicted ASD (but not ADHD) traits at 24βmonths across the entire sample. Elevated tactile sensory seeking at 10βmonths moderated the relationship between early reduced neural repetition suppression and later ASD traits. CONCLUSIONS: Reduced tactile neural repetition suppression is an early marker of later ASD traits in infants at elevated likelihood of ASD or ADHD, suggesting that a common pathway to later ASD traits exists despite different familial backgrounds. Elevated tactile sensory seeking may act as a protective factor, mitigating the relationship between early tactile neural repetition suppression and later ASD traits
ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΠ°Π±ΠΎΡΡ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ Π² ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ. (Π§Π°ΡΡΡ 2)
Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia was not conceived as a source of original information, but as a gateway to secondary sources: according to Wikipediaβs guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readersβ interactions with citations. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0,29% overall; 0,56% on desktop; 0,13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources, and references about life events (births, deaths, marriages, etc.) are particularly popular. Taken together, our findings deepen our understanding of Wikipediaβs role in a global information economy where reliability is ever less certain, and source attribution ever more vital.Β ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΡΠ°ΠΌΡΡ
ΠΏΠΎΡΠ΅ΡΠ°Π΅ΠΌΡΡ
ΡΠ°ΠΉΡΠΎΠ² Π² ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠ΅ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΠ½Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄Π»Ρ ΠΌΠ½ΠΎΠ³ΠΈΡ
ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΠ½ΡΠΈΠΊΠ»ΠΎΠΏΠ΅Π΄ΠΈΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ Π·Π°Π΄ΡΠΌΡΠ²Π°Π»Π°ΡΡ Π½Π΅ ΠΊΠ°ΠΊ ΠΈΡΡΠΎΡΠ½ΠΈΠΊ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΠΎΠΉ (ΠΎΠΊΠΎΠ½ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠΉ) Π½Π°ΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, Π°, ΡΠΊΠΎΡΠ΅Π΅, ΠΊΠ°ΠΊ Π²ΠΎΡΠΎΡΠ° ΠΊ Π±ΠΎΠ»Π΅Π΅ Π³Π»ΡΠ±ΠΎΠΊΠΈΠΌ ΠΈ ΡΠΎΡΠ½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌ. Π ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π±Π°Π·ΠΎΠ²ΡΠΌΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°ΠΌΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ ΡΠ°ΠΊΡΡ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΠΏΠΎΠ΄ΠΊΡΠ΅ΠΏΠ»Π΅Π½Ρ Π½Π°Π΄ΡΠΆΠ½ΡΠΌΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΡΠ°ΠΆΠ°ΡΡ ΠΏΠΎΠ»Π½ΡΠΉ ΡΠΏΠ΅ΠΊΡΡ Π²ΡΠ΅Ρ
ΠΌΠ½Π΅Π½ΠΈΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠ΅. Π₯ΠΎΡΡ ΡΠΈΡΠ°ΡΡ Π»Π΅ΠΆΠ°Ρ Π² ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ, ΠΏΠΎΠΊΠ° ΠΌΠ°Π»ΠΎ ΡΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎ ΠΎ ΡΠΎΠΌ, ΠΊΠ°ΠΊ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΡΠ°Π±ΠΎΡΠ°ΡΡ Ρ Π½ΠΈΠΌΠΈ. Π§ΡΠΎΠ±Ρ Π·Π°ΠΊΡΡΡΡ ΡΡΠΎΡ ΠΏΡΠΎΠ±Π΅Π», ΠΌΡ ΡΠΎΠ·Π΄Π°Π»ΠΈ ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΈΠ΅ (ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅) ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ Π΄Π»Ρ Π²Π΅Π΄Π΅Π½ΠΈΡ Π·Π°ΠΏΠΈΡΠ΅ΠΉ (ΠΆΡΡΠ½Π°Π»ΠΎΠ²) Π²ΡΠ΅Ρ
Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΉ ΡΠΎ ΡΡΡΠ»ΠΊΠ°ΠΌΠΈ, ΠΈΠ΄ΡΡΠΈΠΌΠΈ ΠΈΠ· Π°Π½Π³Π»ΠΎΡΠ·ΡΡΠ½ΡΡ
ΡΡΠ°ΡΠ΅ΠΉ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ Π½Π° ΡΠΈΡΠΈΡΡΠ΅ΠΌΡΠ΅ ΡΡΡΠ»ΠΊΠΈ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΡΠ°, ΠΈ ΠΏΡΠΎΠ²Π΅Π»ΠΈ ΠΏΠ΅ΡΠ²ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠΈΡΠ°ΡΠ΅Π»Π΅ΠΉ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ, ΡΡΠΎ Π² ΡΠ΅Π»ΠΎΠΌ Π²ΠΎΠ²Π»Π΅ΡΡΠ½Π½ΠΎΡΡΡ Π² ΡΠΈΡΠ°ΡΡ Π½ΠΈΠ·ΠΊΠ°Ρ. ΠΠΊΠΎΠ»ΠΎ 300 ΠΏΡΠΎΡΠΌΠΎΡΡΠΎΠ² ΡΡΡΠ°Π½ΠΈΡ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ ΠΊ Π²Ρ
ΠΎΠ΄Ρ Π½Π° ΠΎΠ΄Π½Ρ ΡΡΡΠ»ΠΊΡ β ΡΡΠΎ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ Π²ΡΠ΅Π³ΠΎ 0,29%; Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ 0,56% ΠΏΡΠΈ ΡΠ°Π±ΠΎΡΠ΅ Ρ Π½Π°ΡΡΠΎΠ»ΡΠ½ΡΠΌ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠΎΠΌ (Π½Π° ΡΠ°Π±ΠΎΡΠ΅ΠΌ ΡΡΠΎΠ»Π΅) ΠΈ 0,13% ΠΏΡΠΈ ΡΠ°Π±ΠΎΡΠ΅ Π½Π° ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ²Π°Ρ
. Π‘ΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π°ΠΌΠΈ ΠΏΠΎ ΡΡΡΠ»ΠΊΠ΅, ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Ρ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΡΡ ΡΠ°ΡΠ΅ Π½Π° Π±ΠΎΠ»Π΅Π΅ ΠΊΠΎΡΠΎΡΠΊΠΈΡ
ΡΡΡΠ°Π½ΠΈΡΠ°Ρ
ΠΈ Π½Π° ΡΡΡΠ°Π½ΠΈΡΠ°Ρ
ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π½ΠΈΠ·ΠΊΠΎΠ³ΠΎ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°. ΠΡΡ
ΠΎΠ΄Ρ ΠΈΠ· ΡΡΠΎΠ³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠΈΡΡ, ΡΡΠΎ ΡΡΡΠ»ΠΊΠΈ ΡΠ°ΡΠ΅ Π²ΡΠ΅Π³ΠΎ ΡΡΠ΅Π±ΡΡΡΡΡ, ΠΊΠΎΠ³Π΄Π° ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΡ Π½Π΅ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ, ΠΊΠΎΡΠΎΡΡΡ ΠΈΡΠ΅Ρ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΠΌΡ ΠΎΠ±ΡΠ°ΡΠΈΠ»ΠΈ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅, ΡΡΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΎΡΠΊΡΡΡΠΎΠ³ΠΎ Π΄ΠΎΡΡΡΠΏΠ° ΠΈ ΡΡΡΠ»ΠΊΠΈ ΠΎ ΠΆΠΈΠ·Π½Π΅Π½Π½ΡΡ
ΡΠΎΠ±ΡΡΠΈΡΡ
(ΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ, ΡΠΌΠ΅ΡΡΠΈ, Π±ΡΠ°ΠΊΠΈ ΠΈ Ρ.Π΄.) ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ ΠΏΠΎΠΏΡΠ»ΡΡΠ½Ρ. Π‘ΠΎΠ±ΡΠ°Π½Π½ΡΠ΅ Π²ΠΎΠ΅Π΄ΠΈΠ½ΠΎ, Π½Π°ΡΠΈ Π²ΡΠ²ΠΎΠ΄Ρ ΡΠ³Π»ΡΠ±Π»ΡΡΡ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠΎΠ»ΠΈ ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ Π² Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅, Π³Π΄Π΅ Π½Π°Π΄ΡΠΆΠ½ΠΎΡΡΡ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π²ΡΡ ΠΌΠ΅Π½Π΅Π΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ½Π½ΠΎΠΉ, Π° Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π²ΡΡ Π±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΡΠΌ. Π‘ΠΏΡΠ°Π²ΠΎΡΠ½ΡΠΉ ΡΠΎΡΠΌΠ°Ρ ACM Π΄Π»Ρ ΡΡΡΠ»ΠΎΠΊ: Π’ΠΈΡΠΈΠ°Π½ΠΎ ΠΠΈΠΊΠ°ΡΠ΄ΠΈ, ΠΠΈΡΠΈΠ°ΠΌ Π Π΅Π΄ΠΈ, ΠΠΆΠΎΠ²Π°Π½Π½ΠΈ ΠΠΎΠ»Π°Π²ΠΈΡΡΠ° ΠΈ Π ΠΎΠ±Π΅ΡΡ ΠΠ΅ΡΡ. 2020.ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Ρ ΡΠΈΡΠ°ΡΠ°ΠΌΠΈ Π² ΠΠΈΠΊΠΈΠΏΠ΅Π΄ΠΈΠΈ. Π ΡΡΡΠ΄Π°Ρ
: ΠΠ΅Π±-ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΡ 2020 (WWWβ20), 20β24 Π°ΠΏΡΠ΅Π»Ρ 2020 Π³ΠΎΠ΄Π°, Π’Π°ΠΉΠ±ΡΠΉ, Π’Π°ΠΉ-Π²Π°Π½Ρ. ACM, ΠΡΡ-ΠΠΎΡΠΊ, ΡΡΠ°Ρ ΠΡΡ-ΠΠΎΡΠΊ, Π‘Π¨Π, 12 Ρ. https://doi.org/10.1145/3366423.3380300
- β¦