Region of interest extraction and virtual camera control based on panoramic video capturing

Sun, Xiuhong; Foote, Jonathan; Kimber, Don; Manjunath, B. S.

doi:10.1109/tmm.2005.854388

Cited by 59 publications

(4 citation statements)

References 30 publications

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“…The ROIs could be obtained by automatic detection algorithm [11][12][13]. The algorithm of this paper can be easily integrated with those algorithms.…”

Section: Resultsmentioning

confidence: 99%

Adaptive background skipping algorithm for region-of-interest scalable video coding

Liu

Hu²,

Han³

2008

2008 11th IEEE Singapore International Conference on Communication Systems

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In this paper, we propose an adaptive Region of Interest (ROI) algorithm for scalable video coding. ROIs are encoded in every temporal layer, while background (non-ROI) is skipped selectively in enhancement layers. The background skipping decision is based on an adaptive estimation of motion activity. And the saving bits could be used to enhance the ROI spatial quality. The overall perceptual quality can be significantly improved since human vision tends to pay more attention to ROIs. The experimental results indicate the proposed scheme has better visual quality compared with conventional temporal scalable scheme. Keywords-scalable video coding, region of interest, background skippingI.

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“…The ROIs could be obtained by automatic detection algorithm [11][12][13]. The algorithm of this paper can be easily integrated with those algorithms.…”

Section: Resultsmentioning

confidence: 99%

Adaptive background skipping algorithm for region-of-interest scalable video coding

Liu

Hu²,

Han³

2008

2008 11th IEEE Singapore International Conference on Communication Systems

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show abstract

“…Depending on the application, one or several, manual or automatic ROIs are extracted in the original image. Among the automatic ROI extraction techniques [16] [17] [18], the histogram-based statistical methods are very popular due to their computational efficiency. In applications where the background can be detected in the very low or very high brightness areas, simple histogram rules can be used for fast ROI extraction.…”

Section: Image Preprocessingmentioning

confidence: 99%

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Vlachos,

Dermatas

2024

JCC

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Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

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“…Using a panoramic texture for an immersive feeling is also a researched topic [Jenkin et al 1998;Chen 1995], also in different applications areas (e.g., lecture videos [Yokoi and Fujiyoshi 2005;Sun et al 2005;Ahmed and Eades 2005]). Some works also describe manually controlled virtual cameras generated from panoramic videos with emphasis on human-centered interfaces [Foote et al 2013], network performance [Mavlankar and Girod 2010] and over-all system aspects [Gaddam et al 2014b].…”

Section: Related Workmentioning

confidence: 99%

The Cameraman Operating My Virtual Camera is Artificial

Gaddam

Langseth

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

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In this article, we argue that the energy spent in designing autonomous camera control systems is not spent in vain. We present a real-time virtual camera system that can create smooth camera motion. Similar systems are frequently benchmarked with the human operator as the best possible reference; however, we avoid a priori assumptions in our evaluations. Our main question is simply whether we can design algorithms to steer a virtual camera that can compete with the user experience for recordings from an expert operator with several years of experience? In this respect, we present two low-complexity servoing methods that are explored in two user studies. The results from the user studies give a promising answer to the question pursued. Furthermore, all components of the system meet the real-time requirements on commodity hardware. The growing capabilities of both hardware and network in mobile devices give us hope that this system can be deployed to mobile users in the near future. Moreover, the design of the presented system takes into account that services to concurrent users must be supported. ACM Reference Format:Vamsidhar Reddy Gaddam, Ragnhild Eg, Ragnar Langseth, Carsten Griwodz, and Pål Halvorsen. 2015. The cameraman operating my virtual camera is artificial: Can the machine be as good as a human? ACM Trans. Multimedia Comput. Commun.

show abstract

Region of interest extraction and virtual camera control based on panoramic video capturing

Cited by 59 publications

References 30 publications

Adaptive background skipping algorithm for region-of-interest scalable video coding

Adaptive background skipping algorithm for region-of-interest scalable video coding

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

The Cameraman Operating My Virtual Camera is Artificial

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