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Shape from focus technique can be used in the computer monocular vision, which is widely applied in the smart transportation. In this study, we proposed a novel directional statistics based focus measure for shape from focus computation. We first compute the standard deviation σ and the mean value μ in the directional neighborhood. Then use the σ/μ as the focus measure to estimate the shape. The proposed...
We consider the task of multi-view subspace learning which integrates multi-view information to learn a unified representation for multimedia data. In real-world scenarios, we encounter views with high diversities of semantic levels. Neglecting the problem of semantic inconsistency, existing graph-based methods directly convert heterogeneous information into local affinity matrices to conduct a fusion...
Point sets generated by image-based 3D reconstruction techniques are often much noisier than those obtained using active techniques like laser scanning. Therefore, they pose greater challenges to the subsequent surface reconstruction (meshing) stage. We present a simple and effective method for removing noise and outliers from such point sets. Our algorithm uses the input images and corresponding...
Over the last decade, a lot of research has been done on sound event classification. But a main problem with sound event classification is that the performance sharply degrades in the presence of noise. As spectrogram-based image features and denoising auto encoder reportedly have superior performance in noisy conditions, this paper proposes a new robust feature called denoising auto encoder image...
In this paper we describe a straightforward, yet effective method of recovering angles from a set of tomographic projections when the view-angles are completely unknown. Existing works on this problem have consistently assumed availability of projections from a large number of angles as well as made assumptions on the underlying distribution of angles to aid reconstruction. We make no such assumptions,...
When collecting samples via crowd-sourcing for semi-supervised learning, often labels that designate events of interest are assigned unreliably, resulting in label noise. In this paper, we propose a robust method for graph-based image classifier learning given noisy labels, leveraging on recent advances in graph signal processing. In particular, we formulate a graph-signal restoration problem, where...
Image in painting is the process of removing selected object and restoring dead pixel from an image based on the background information. Various method have been proposed to tackle the in painting problem where they need related information from other images and use only neighboring data to recover the lost part of image. To overcome this, an efficient in painting technique called Robust Non-Local...
In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum likelihood (ML) estimation based CRT when the remainder noises may not necessarily have the same variances. The proposed algorithm only needs to search for the solution among L elements, where L is the number of remainders. We compare the performances...
Blind reconstruction or deconvolution, is the process of restoring an observed image without explicit knowledge of the imaging system's point spread function (PSF). Images produced from an imaging system, for example confocal laser scanning or widefield optical microscope, are noisy and invariably blurred. For robust scientific interpretation and analysis of a typical image obtained in this way, it...
We present a method for recovering fast and robustly the 3D shape of inextensible and smooth surfaces from a monocular image. We propose a weighted iterative least squares approach to minimize the reprojection error between 2D-3D point correspondences preserving the 3D lengths. In addition, a local 3D smoothness constraint for each mesh vertex is proposed to increase the robustness to noisy correspondences...
Shape-from-focus (SFF) methods involve recovering the topography of an observed object surface by axially maximising (along the optical axis) the sharpness information from a focus measurement throughout a sequence of numerous images acquired by optical sectioning with a limited depth-of-field imaging system. Nevertheless, some noisy data necessarily introduced by imaging equipments during the acquisition...
In this paper, the method of missing data imputation based on the emergent field of compressive sensing for the front end of a speaker identification system in noisy conditions is investigated. Firstly, noisy speech signals are transformed into Gammatone spectrum by using cochlear filtering; then, unreliable spectral components are reconstructed given an incomplete set of reliable ones; finally, speaker...
Image inpainting is the art of recovering the original image from images which are generally incomplete due to various factors, including degradation due to ageing, damage due to wear and tear and missing image details due to occlusion. In such situations, there is a need to predict the missing image information without introducing undesirable artifacts. Original contribution in this direction is...
In this paper, we address the computational complexity issue in Sparse Representation based Classification (SRC). In SRC, it is time consuming to find a global sparse representation. To remedy this deficiency, we propose a Local Sparse Representation based Classification (LSRC) scheme, which performs sparse decomposition in local neighborhood. In LSRC, instead of solving the l1-norm constrained least...
This paper presents a view-invariant approach to gait recognition in multi-camera scenarios exploiting a joint spatio-temporal data representation and analysis. First, multi-view information is employed to generate a 3D voxel reconstruction of the scene under study. The analyzed subject is tracked and its centroid and orientation allow recentering and aligning the volume associated to it, thus obtaining...
Feature-based methods have found increasing use in many applications such as object recognition, 3D reconstruction and mosaicing. In this paper, we focus on the problem of matching such features. While a histogram-of-gradients type methods such as SIFT, GLOH and Shape Context are currently popular, several papers have suggested using orders of pixels rather than raw intensities and shown improved...
We present a multi-level partition of unity algebraic set surfaces (MPU-APSS) for surface reconstruction which can be represented by either a projection or in an implicit form. A algebraic point set surface (APSS) defines a smooth surface from a set of unorganized points using local moving least-squares (MLS) fitting of algebraic spheres. However, due to the local nature, APSS does not work well for...
Recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Compressive sensing systems are not immune to noise, which is always present in practical acquisition systems. In this paper we propose robust methods for sampling and reconstructing sparse signals in the presence of impulsive noise. Analysis of the proposed methods...
The compressed sensing (CS) paradigm unifies sensing and compression of sparse signals in a simple linear measurement step. Reconstruction of the signal from the CS measurements relies on the knowledge of the measurement matrix used for sensing. Generation of the pseudo-random sensing matrix utilizing a cryptographic key, offers a natural method for encrypting the signal during CS. This CS based encryption...
This paper provides a technique for measuring camera translation relatively w.r.t. the scene from two images. We demonstrate that the amount of the translation can be reliably measured for general as well as planar scenes by the most frequent apical angle, the angle under which the camera centers are seen from the perspective of the reconstructed scene points. Simulated experiments show that the dominant...
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