Variable resolution teleconferencing

Basu, Anup; Sullivan, Alan; Wiebe, Kevin James

doi:10.1109/icsmc.1993.390703

Cited by 30 publications

(14 citation statements)

References 10 publications

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“…In future work we will focus on automatic estimation of the value of parameter β used in our algorithm. We will also investigate applications in foveation [14,15,16,17,18,19], model based coding [20,21,22], and medical imaging [23,24,25].…”

Section: Discussionmentioning

confidence: 99%

Highlighting objects of interest in an image by integrating saliency and depth

Mukherjee

Cheng

Basu

2016

2016 IEEE International Conference on Image Processing (ICIP)

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Stereo images have been captured primarily for 3D reconstruction in the past. However, the depth information acquired from stereo can also be used along with saliency to highlight certain objects in a scene. This approach can be used to make still images more interesting to look at, and highlight objects of interest in the scene. We introduce this novel direction in this paper, and discuss the theoretical framework behind the approach. Even though we use depth from stereo in this work, our approach is applicable to depth data acquired from any sensor modality. Experimental results on both indoor and outdoor scenes demonstrate the benefits of our algorithm.Index Terms-Focus/Defocus Processing, Depth from Stereo, Segmentation INTRODUCTIONDepth-of-Field (DoF) is an essential component in producing photorealistic effects during image capture. DoF enhances images not only by giving people the "feeling" of depth information but also allows them to focus on the important regions. DoF makes images look more natural. It is essential in making the focus points of the image stand out, by emphasizing the foreground and de-emphasizing the background [1]. While taking a photo with an optical camera, we can vary the size of the aperture to set the DoF or "zone of focus" for the photo. The points within the DoF appear to be focused, while other points far away from the focal plane are blurred. Photos with a small zone of focus are said to have a "shallow" DoF. Thus, to highlight objects of interest in a photo, we should select a shallow DoF containing only these objects, also known as "Region-of-Interest" (ROI) or "foreground." However, there is often a requirement to render shallow DoF effects during post-processing, such as during photo retouching by a professional photographer in his or her studio, or during cinematographic editing sessions. Furthermore, in the areas of virtual reality and video games, real-time DoF effects are also important aspects in enhancing the visual effects. Thus, simulating DoF effects have become an important research topic in the field of computer vision.Most DoF rendering algorithms post-process single-view images, and can be roughly categorized as object-space-based and image-space-based methods. Lee's method [2] performs image blurring via non-linear interpolation of mipmap images generated from a pinhole image. Multilayer approaches like our proposed method split an image into layers based on pixel depths. In others, hidden image areas are approximated by color extrapolation to solve the partial visibility problem via Fourier transform, pyramid image processing, anisotropic diffusion, splatting, rectangle spreading, and so on [1].Saliency is widely used to investigate visual attention. The biologically inspired method by Itti [3] determines image saliency using Difference-of-Gaussians. Itti's method was later extended with graph-based normalization to build the visual saliency model [4]. Other methods use frequency domain processing, or combine global contrast and spatial relationship ...

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Section: Discussionmentioning

confidence: 99%

Highlighting objects of interest in an image by integrating saliency and depth

Mukherjee

Cheng

Basu

2016

2016 IEEE International Conference on Image Processing (ICIP)

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“…For engineering purposes, it is not necessary to adhere to the logmap (2). Thus, we use the term "logmap" loosely and use it to refer to transformations that have the log-polar favor.…”

Section: Logmap and Foveation Operatormentioning

confidence: 98%

“…Since a foveated image carries much less information compared to the original uniform image, it can be encoded by fewer number of bytes. This observation has been exploited by visualization systems where transmission of images to the display device is a bottleneck, for example, in video conferencing [2,3,11]. Foveation also plays a role in visualization systems where the image rendering process is not fast enough to meet the real-time requirement, for example in volume visualization [16] and flight simulation [12,31].…”

Section: Applications Of Foveationmentioning

confidence: 99%

Wavelet Foveation

Chang

Mallat

Yap

2000

Applied and Computational Harmonic Analysis

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A foveated image is a nonuniform resolution image whose resolution is highest at a point (fovea) but falls off away from the fovea. It can be obtained from a uniform image through a space-variant smoothing process, where the width of the smoothing function is small near the fovea and gradually expanding as the distance from the fovea increases. We treat this process as an integral operator and analyze its kernel. This kernel is dominated by its diagonal in the wavelet bases and thus permits a fast algorithm for foveating images. In addition, the transformed kernel takes a simple form which can be easily computed using a look-up table. This is useful, since in applications the fovea changes rapidly. We describe an application of our approximation algorithm in image visualization over the Internet.

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“…By weighting the value of the smallest salience and the smallest distance from the point to the object, a value is generated that defines which output of the pyramid will be used to compose the multifoveated image. Basu et al [44][45][46] seek to compress visual information using the Cartesian Variable Resolution (CVR) technique. They propose two methodologies, so that during the insertion of more than one region of interest, it would be possible to define when to reduce the resolution around the fovea as a compensation, or if additional information should be retained by reducing the compression ratio.…”

Section: Related Workmentioning

confidence: 99%

Dynamic multifoveated structure for real-time vision tasks in robotic systems

Medeiros

Gomes

Clua

et al. 2019

J Real-Time Image Proc

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Foveation is a technique that allows real-time image processing by drastically reducing the amount of visual data without loosing essential information around some focused area. When a robot needs to pay attention at two or more regions of the image at the same time, e.g., for tracking two or more objects, multifoveation is necessary. In this case, computing features twice in the intersections between the different foveated structures, which could linearly increase the processing time, must be avoided. To solve this redundancy removal problem, we propose two algorithms. The first one is based on the previous calculation of redundant blocks and the second one is based on a pixel-by-pixel processing at execution time. Experimental results show a gain in processing time for the block-based model in comparison with the pixel-by-pixel and also of both in comparison with other approaches that sequentially calculate various single foveated images. Robotics vision and other tasks related to dynamic visual attention, as recognition, real-time surveillance, video transmission, and image rendering, are examples of applications that can rely on and strongly benefit from such model.

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Variable resolution teleconferencing

Cited by 30 publications

References 10 publications

Highlighting objects of interest in an image by integrating saliency and depth

Highlighting objects of interest in an image by integrating saliency and depth

Wavelet Foveation

Dynamic multifoveated structure for real-time vision tasks in robotic systems

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