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Proceedings ArticleDOI

Development of methodology for extraction of depth for 2D-to-3D conversion

01 Feb 2017-pp 1-5
TL;DR: Fusion of four monocular cues as-Motion cue, Linear perspectivecue, Aerial perspective cue and defocus are proposed for depth estimation using single camera to get a continuous depth map.
Abstract: This paper presents semi-automatic method hybrid depth map generation using fusion of monocular cues. Depth Estimation is generally done using stereoscopic cameras. It is a difficult task to estimate depth from single view camera. In this paper, fusion of four monocular cues as-Motion cue, Linear perspectivecue, Aerial perspective cue and defocus are proposed for depth estimation using single camera. Bilateral filter is used to get a continuous depth map. The results show that the present system for estimation of depth based on monocular cues achieves better performance.
Citations
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Journal ArticleDOI
TL;DR: In this paper , the authors look at the notion of augmented reality and its application in interior design and show how the furniture that best matches their home decor, such as the color of a wall, etc., improves and boosts market value.
Abstract: People nowadays are very interested in new technology and its capabilities. Augmented and virtual reality are two of the most popular topics right now. The Augmented Reality merges the virtual and physical worlds. This approach will be applied to interior design in this case that enhance the view of customers. The customer is looking for a platform that combines the majority of the functions with improved visibility. In interior design, augmented reality provides the buyer with the delight of purchasing their assets. The furniture that best matches their home decor, such as the colour of a wall, etc., improves and boosts market value. This paper will look at the notion of Augmented Reality and its application. We will also know the process of overlapping virtual data in a real-time environment of a 2D or 3D virtual image.
Journal ArticleDOI
TL;DR: In this article , the authors proposed a trust-based system in which every node obtains a trust value based on their activities and the mote that is least trusted network will be marked as malicious and get isolated from the network.
Abstract: The Internet of Things systems are prone to the attacks as they have ad-hoc and finite resource structure. Internet of Things-based mechanisms can be utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains vulnerability against huge number of attackers and adversaries namely cybercriminals, government, etc. The major goal of PA is to steal the sensitive information such as numbers of credit card numbers, state of data, credential of commercial account and information related to health, by hacking the Internet of Things devices. The hello flood attack is one of malicious activity of IoT which affect network performance to great extent. This attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service within the network. This research work suggests a trust-based system in which every node obtains a trust value based on their activities. The mote that is least trusted Network will be marked as malicious and get isolated from the network. Network Simulator-2 is applied to deploy the suggested scheme and various metrics such as throughput, packet loss, energy consumption and delay are considered to analyse the results.
References
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Journal ArticleDOI
TL;DR: This work considers the problem of estimating detailed 3D structure from a single still image of an unstructured environment and uses a Markov random field (MRF) to infer a set of "plane parameters" that capture both the 3D location and 3D orientation of the patch.
Abstract: We consider the problem of estimating detailed 3D structure from a single still image of an unstructured environment. Our goal is to create 3D models that are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov random field (MRF) to infer a set of "plane parametersrdquo that capture both the 3D location and 3D orientation of the patch. The MRF, trained via supervised learning, models both image depth cues as well as the relationships between different parts of the image. Other than assuming that the environment is made up of a number of small planes, our model makes no explicit assumptions about the structure of the scene; this enables the algorithm to capture much more detailed 3D structure than does prior art and also give a much richer experience in the 3D flythroughs created using image-based rendering, even for scenes with significant nonvertical structure. Using this approach, we have created qualitatively correct 3D models for 64.9 percent of 588 images downloaded from the Internet. We have also extended our model to produce large-scale 3D models from a few images.

1,522 citations

Proceedings ArticleDOI
20 Jun 2009
TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel.
Abstract: In this paper, we propose a simple but effective image prior - dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal.

847 citations

Journal ArticleDOI
01 Jul 2005
TL;DR: This paper presents a fully automatic method for creating a 3D model from a single photograph made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration.
Abstract: This paper presents a fully automatic method for creating a 3D model from a single photograph. The model is made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration. Our main insight is that instead of attempting to recover precise geometry, we statistically model geometric classes defined by their orientations in the scene. Our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. Because of the inherent ambiguity of the problem and the statistical nature of the approach, the algorithm is not expected to work on every image. However. it performs surprisingly well for a wide range of scenes taken from a typical person's photo album.

730 citations

Journal ArticleDOI
TL;DR: Local scale control is shown to be important for the estimation of blur in complex images, where the potential for interference between nearby edges of very different blur scale requires that estimates be made at the minimum reliable scale.
Abstract: We show that knowledge of sensor properties and operator norms can be exploited to define a unique, locally computable minimum reliable scale for local estimation at each point in the image. This method for local scale control is applied to the problem of detecting and localizing edges in images with shallow depth of field and shadows. We show that edges spanning a broad range of blur scales and contrasts can be recovered accurately by a single system with no input parameters other than the second moment of the sensor noise. A natural dividend of this approach is a measure of the thickness of contours which can be used to estimate focal and penumbral blur. Local scale control is shown to be important for the estimation of blur in complex images, where the potential for interference between nearby edges of very different blur scale requires that estimates be made at the minimum reliable scale.

575 citations

Journal ArticleDOI
TL;DR: This paper presents a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations, and demonstrates the effectiveness of this method in providing a reliable estimation of the defocus map.

370 citations