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The rapid development of three-dimensional (3D) imaging techniques has significantly increased the demand for high resolution (HR) depth video and images. Significant pixel deficiencies and too much noise can be seen in depth images especially taken from Kinect cameras. For this reason, usability in several computer vision applications is restricted. In the acquisition of HR depth images, in traditional...
The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the...
Nowadays, people might need super resolution to obtain high quality images. Super resolution algorithm enhances high frequency information (texture or edges) to improve the image quality. We can do more things with super resolution, such as road surveillance system. The image quality would be degraded by illumination, angle, distance, and other conditions, and it will result in failing to recognize...
Understanding a scene provided by very high resolution (VHR) satellite imagery has become a more and more challenging problem. In this paper, we propose a new method for scene classification based on saliency computing of patches sampling from the VHR images. Sparse principal component analysis (sPCA) is then adopted to select the corresponding informative salient patches for image scene representation...
This paper proposes image super-resolution techniques with multi-channel convolutional neural networks (CNN). In the proposed method, output pixels are classified into four groups depending on their positions. Those groups are generated from separate channels of the CNN. Finally, they are synthesized into a 2−2 magnified image. This architecture can enlarge images directly without bicubic interpolation...
In this study, the automated matching of 2.5 m resolution Göktürk-2 panchromatic stereo images has been addressed. From an operational perspective, it seems unlikely to produce the epipolar images from Göktürk-2 stereo datasets at a sub-pixel level due to several reasons. Therefore, SIFT-flow method that does not require any user input and that has ability to perform matching through the stereo data...
Image Super-resolution (SR) reconstruction techniques based on sparse representation have attracted ever-increasing attentions in recent years, where the choice of over-complete dictionary is of prime important for reconstruction quality. However, most of the image SR methods based on sparse representation fail to consider the discrimination and the redundance of the dictionaries, which lead to obvious...
This paper proposes a novel neural network learning the essential mapping function between the low resolution and high resolution image for Image superresolution problem. In our approach, patch recurrence property of small patches in natural image are utilized as a prior to train the network. An autoencoder neutral network is designed to reconstruct the high resolution patches. The constraint that...
In this paper we propose a new high-quality and efficient single image super-resolution model that extends exploit the self-similarity property. The similarity of frequency error compensation between the high-resolution patch and low-resolution model can modeled as a optimization problem. Based on the in-place patch similarity, the optimization model is further simplified to alleviate the computing...
In this paper, we introduce a single image super resolution based on non-parametric local information. The basic idea of the proposed method is to use a property, which is inferred by relations between input and its lower resolution images, of an unknown high resolution image. The experimental results show the efficiency of the proposed algorithm compared to a state-of-the-art method. Besides, the...
This paper proposes a method for single image enlargement with linear weighting techniques and kernel estimation. The aims of our technique are to reduce the distance of the pixel value too far especially for interpolation pixel. Contribution from the closest pixels to the interpolation point is unchanged meanwhile the farthest pixel contribution will be estimated. There are four pixel contributions...
Building a photorealistic, 3D model of an object or a complete scene from image-based methods is a fundamental problem in computer vision, and has many applications in robotic perception, navigation, exploration and mapping. In this paper, we extend current state-of-the-art in the computation of depth maps by presenting an accurate and computationally efficient iterative hierarchical algorithm for...
Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. Recently, full-resolution salient maps that retain well-defined boundaries have attracted attention. In these maps, boundaries are preserved by retaining substantially more frequency content from the original image than older techniques. However, if the salient...
In this work, we developed a technique for face recognition using the idea of multiresolution face recognition. The multiresolution subbands are generated by using discrete wavelet transform (DWT). We then apply scale invariant feature transform (SIFT) to extract the salient feature descriptors at each subband using the resulting low frequency subband of the image. The descriptors are used to perform...
This paper presents a novel object-oriented stereo matching on multi-scale superpixels to generate a low-resolution depth map. It overcomes the classic downsampling methods' disadvantages, such as boundary blurring, outlier enlargement and foreground objects merging to background, etc. The approach we exploited is to segment the image in three scales' superpixels from dense to sparse ones according...
In this paper we introduce a novel technique that restores video frames by using several high quality stills taken of the same static scene. Our method is relied on the robustness of the local feature points. We first select several seed frames by employing a powerful detector/descriptor between video frames and reference photographs. Next, based on this information good local feature points are tracked...
We develop a model for face recognition that describes the image as a sum of signal and noise components. We describe each component as a weighted combination of basis functions. In this paper we investigate the effect of the degree of localization of these basis functions: each might describe the whole image (describe global pixel covariance) or only a small part of the face (describe only local...
In this paper a very fast graphics processing unit implementation of a local, census-correlation-based stereo matching algorithm is presented. In comparison to absolute or squared difference correlation techniques, the census transform is computational more expensive which led to the motivation of a GPU-based implementation. Due to the parallel architecture of modern graphics cards, complex algorithms...
We propose an environment modelling method using high-resolution spherical stereo colour imaging. We capture indoor or outdoor scenes with line scanning by a rotating spherical camera and recover depth information from a stereo image pair using correspondence matching and spherical cross-slits stereo geometry. The existing single spherical imaging technique is extended to stereo geometry and a hierarchical...
Underwater is a complex structured environment because of inhomogeneous light absorption and light scattering by the environment. These factors make 3D reconstruction in underwater more challenging. In the literature, only calibrated cameras and predefined camera motions are allowed for 3D reconstruction. In this study, 3D representation of underwater scenes are reconstructed from uncalibrated video...
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