Robust Region-of-Interest Determination Based on User Attention Model Through Visual Rhythm Analysis

Chi, Ming-Chieh; Yeh, Chia‐Hung; Chen, Mei-Juan; Hsu, Ching-Ting

doi:10.1109/icccn.2007.4317973

Cited by 24 publications

(15 citation statements)

References 22 publications

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“…In fact, to extract interesting regions from a video, we need to analyze the video and then create a visual attention model [4,5,8,27]. However, poor lighting conditions may make visual cue extraction difficult and then obtain an incorrect visual attention model.…”

Section: System Descriptionmentioning

confidence: 99%

Video quality enhancement based on visual attention model and multi-level exposure correction

Lin

2015

Multimed Tools Appl

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Due to unfavorable environmental conditions such as lack of lighting, poor visual quality in images and videos may make intelligent image/video systems unstable. This means that visual quality enhancement plays an important role in image/video processing, computer vision, and pattern recognition. In this paper, we propose a video quality enhancement scheme based on visual attention model and multi-level exposure correction. To this end, the proposed scheme is composed of four parts: pre-processing, visual attention model generation, multi-level exposure correction, and temporal filtering. To extract more visual cues for visual attention model generation, a pre-processing is used to modify each frame. After preprocessing, facial and non-facial cues are measured to generate visual attention maps of each frame. On the basis of visual attention maps, a multi-level exposure correction algorithm is utilized to adjust the exposure level of each frame and then create several intermediate results. After fusing intermediate results, a synthesized image with good visual quality can be obtained. To avoid flicker effect, a temporal filter is exploited to make the variance of the exposure level small in the temporal domain. To evaluate the performance of the proposed scheme, some images/videos captured by mobile phones and digital cameras are tested. The experimental results show that the proposed scheme can effectively deal with the images/videos with low and high exposure levels. The results also demonstrate that the proposed scheme outperforms some existing methods in terms of visual quality.

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Section: System Descriptionmentioning

confidence: 99%

Video quality enhancement based on visual attention model and multi-level exposure correction

Lin

2015

Multimed Tools Appl

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“…Then, the problems in (4) and (5) can be written as subject to (6) and subject to and (7) respectively. The weighting value is given by (8) where is the th component of the spatially smoothed version of the solution for the previous temporal window. A Gaussian filter is applied to the image representation of the solution for smoothing.…”

Section: Foveated Video Coding Based On Audio-visual Focus Of Atmentioning

confidence: 99%

“…In [7], bottom-up cues and top-down cues such as faces and captions were considered in a scalable visual sensitivity profile generating a hierarchy of saliency maps. Visual rhythm analysis was performed in [8], from which the region-ofinterest (ROI) was outlined and used for foveated video coding in H.264/AVC.…”

mentioning

confidence: 99%

Subjective Quality Evaluation of Foveated Video Coding Using Audio-Visual Focus of Attention

Lee

Simone

Ebrahimi

2011

IEEE J. Sel. Top. Signal Process.

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Abstract-This paper presents a foveated coding method using audio-visual focus of attention and its evaluation through extensive subjective experiments on both standard-definition and high-definition sequences. Regarding a sound-emitting region as the location drawing the human attention, the method applies varying quality levels in an image frame according to the distance of a pixel to the identified sound source. Two experiments are presented to prove the efficiency of the method. Experiment 1 examines the validity and effectiveness of the method in comparison to the constant quality coding for high-quality conditions. In Experiment 2, the method is compared to the fixed bit rate coding for low quality conditions where coding artifacts are noticeable. The results demonstrate that the foveated coding method provides considerable coding gain without significant quality degradation, but uneven distributions of the coding artifacts (blockiness) by the method are often less preferred than the uniform distribution of the artifacts. Additional interesting findings are also discussed, such as content dependence of the performance of the method, the memory effect in multiple viewings, and the difference in the quality perception for frame size variations.Index Terms-Audio-visual focus of attention, content dependence, foveated coding, H.264/AVC, memory effect, quality of experience, subjective quality assessment.

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“…in automatic commercials removal, soccer videos summaries, choosing region of interest on the basis of the user attention analysis and face spoofing detection. [33,34,35] However, it can also be used to detect and count objects crossing a particular frame area, supposing they move in the same direction and with velocity that does not exceed the camera frame rate. [20] This, together with the possibility of processing subsets of many frames simultaneously rather than analyzing the whole sequence frame by frame, constitutes both the core and main advantages of the presented method.…”

Section: Visual Rhythm-based Approachmentioning

confidence: 99%

Computer vision for continuous plankton monitoring

Matuszewski¹

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Visão computacional para o monitoramento contínuo de plânctonEsta versão da dissertação contém as correções e alterações sugeridas pela Comissão Julgadora durante a defesa da versão original do trabalho, realizada em 04/04/2014. Uma cópia da versão original está disponível no Instituto de Matemática e Estatística da Universidade de São Paulo. Microorganismos planctônicos constituem a base da cadeia alimentar marinha e desempenham um grande papel na redução do dióxido de carbono na atmosfera. Além disso, são muito sensíveis a alterações ambientais e permitem perceber (e potencialmente neutralizar) as mesmas mais rapidamente do que em qualquer outro meio. Como tal, não só influenciam a indústria da pesca, mas também são frequentemente utilizados para analisar as mudanças nas zonas costeiras exploradas e a influência destas interferências no ambiente e clima locais. Como consequência, existe uma forte necessidade de desenvolver sistemas altamente eficientes, que permitam observar comunidades planctônicas em grandes escalas de tempo e volume. Isso nos fornece uma melhor compreensão do papel do plâncton no clima global, bem como ajuda a manter o equilíbrio do frágil meio ambiente. Os sensores utilizados normalmente fornecem grandes quantidades de dados que devem ser processados de forma eficiente sem a necessidade do trabalho manual intensivo de especialistas. Um novo sistema de monitoramento de plâncton em grandes volumes é apresentado. Foi desenvolvido e otimizado para o monitoramento contínuo de plâncton; no entanto, pode ser aplicado como uma ferramenta versátil para a análise de fluídos em movimento ou em qualquer aplicação que visa detectar e identificar movimento em fluxo unidirecional. O sistema proposto é composto de três estágios: aquisição de dados, detecção de alvos e suas identificações. O equipamento óptico é utilizado para gravar imagens de pequenas particulas imersas no fluxo de água. A detecção de alvos é realizada pelo método baseado no Ritmo Visual, que acelera significativamente o tempo de processamento e permite um maior fluxo de volume. O método proposto detecta, conta e mede organismos presentes na passagem do fluxo de água em frente ao sensor da câmera. Além disso, o software desenvolvido permite salvar imagens segmentadas de plâncton, que não só reduz consideravelmente o espaço de armazenamento necessário, mas também constitui a entrada para a sua identificação automática. Para garantir o desempenho máximo de até 720 MB/s, o algoritmo foi implementado utilizando CUDA para GPGPU. O método foi testado em um grande conjunto de dados e comparado com a abordagem alternativa de quadro-a-quadro. As imagens obtidas foram utilizadas para construir um classificador que é aplicado na identificação automática de organismos em experimentos de análise de plâncton. Por este motivo desenvolveu-se um software para extração de características. Diversos subconjuntos das 55 características foram testados através de modelos de aprendizagem disponíveis. A melhor exatidão de aproximadamente 92% foi obtida através d...

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Robust Region-of-Interest Determination Based on User Attention Model Through Visual Rhythm Analysis

Cited by 24 publications

References 22 publications

Video quality enhancement based on visual attention model and multi-level exposure correction

Video quality enhancement based on visual attention model and multi-level exposure correction

Subjective Quality Evaluation of Foveated Video Coding Using Audio-Visual Focus of Attention

Computer vision for continuous plankton monitoring

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