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The well-known bilateral filter is used to smooth noisy images while keeping their edges. This filter is commonly used with Gaussian kernel functions without real justification. The choice of the kernel functions has a major effect on the filter behavior. We propose to use exponential kernels with L1 distances instead of Gaussian ones. We derive Stein's Unbiased Risk Estimate to find the optimal parameters...
Hyperspectral imaging has proven useful in a diverse range of applications in agriculture, diagnostic medicine, and surveillance to name a few. However, conventional hyperspectral images (HSIs) tend to be noisy due to limited light in individual bands; thus making denoising necessary. Previous methods for HSI de-noising have viewed the entire HSI as a general 3D volume or focused on processing the...
Most of the state-of-the-art algorithms of restoring single blurred image are sensitive to image noise and artifacts. Our idea is to learn an adaptive filter for blind deconvolution to remedy this problem. We use this auxiliary filter to progressively suppress image noise in early stage of kernel estimation, leading to a robust kernel estimation algorithm. Our approach can naturally handle image noise...
Symbol retrieval for technical documents is still a hot challenge in the document analysis community. In this paper we propose another way to spot symbols. A pixel-based template operator which is an adaptation of the hit-or-miss transform is defined. This operator is robust to translation, rotation and reflection. Experimental results on a real application show the efficiency of our approach.
In this paper, we address the problem of representing objects using contours for the purpose of recognition. We propose a novel segmentation method for integrating a new contour matching energy into level set based segmentation schemes. The contour matching energy is represented by major components of Elliptic Fourier shape descriptors and serves as a shape prior to guide the curve evolution. The...
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a novel human action recognition approach which learns weights for features is proposed, where each feature is assigned a weight for human action representation. These weights are learned...
This paper proposes a new edge descriptor named centripetal-SIFT edge descriptor. A method for symmetric object detection is presented based on symmetry and centripetal-SIFT descriptor in real unsegmented images. Our method includes three main steps: 1) The dominant symmetry axis is located based on SIFT feature point; 2) Image edge points are extracted in scale space and described by the proposed...
Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick cooccurrence matrices and local binary patterns...
Sparse representation based classification (SRC) has been widely used for face recognition (FR). Although SRC algorithm is also adopted in human action recognition, the evaluations of different regular terms have not been given. In this paper, we will discuss and evaluate the role of different regular terms of SRC in human action recognition, after that, we propose human action recognition algorithm...
In this paper we present a methodology for nonlocal processing of 3D colored point clouds using regularization of functions defined on weighted graphs. To adapt it to nonlocal processing of 3D data, a new definition of patches for 3D point clouds is introduced and used for nonlocal filtering of 3D data such as colored point clouds. Results illustrate the benefits of our nonlocal approach to filter...
We present a sparse representation-based method for detecting adventitious lung sounds in low-quality auscultation signals. Since the noise cannot be represented sparsely by any bases, we can extract clear breath sounds and adventitious sounds from noisy electronic auscultation signals via the sparse representation. Using these clear sound components, we measure the level of abnormality, and robustly...
This paper proposes a robust detection method for circular objects in noisy and inhomogeneous contrast image. This method detects circular objects not by the difference in image intensities between the object interior and its surrounding, but by the separability and uniformity of the image intensity distributions as calculated by Bhattacharyya Coefficient. The proposed method can detect obscure and...
We propose a new Semi-Lagrangian scheme for the area-preserving Mean Curvature flow. The model uses a level set framework to propagate a closed planar curve. The corresponding flow has been proposed by Sapiro & Tannenbaum [9]. The scheme has the advantage to allow large time step still maintaining a good accuracy. We apply the algorithm to recover the original shape of a synthetic image to which...
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...
Concentric circles (C2Tag's) are planar markers which offer great advantages for detection and tracking. As the circular point-pair (CPP) is the geometric information encoded by a C2Tag, this work is focused on factorization techniques for Structure-and-Motion from multiple CPP images. Gathering all of them in a measurement matrix, two issues are addressed: how to scale the existing entries and how...
We propose a novel trajectory clustering algorithm which is suitable for online processing of pedestrian or vehicle trajectories computed with a vision-based tracker. Our approach does not require defining distances between trajectories, and can thus process broken trajectories which are inevitable in most cases when object trackers are applied to real world video footage. Clusters are defined as...
Edge detection is an important image processing technique used in face recognition. In this paper we propose an edge detection algorithm suitable for extracting edge maps from facial images under noisy conditions. We employ several improvised techniques like composite gradient variation measures, weighted wide convolution kernels and dual maxima detection. This helps in removing spurious edge points...
In the emerging field of adaptive biometrics, systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due to limitations associated with existing adaptation schemes. This paper proposes a dual-staged template adaptation scheme that allows...
We introduce an adaptation framework based on scale space filtering for making thinning algorithms robust against noise in sketch images. The framework takes a sketch image as input, produces a set of Gaussian blurred images of the input sketch and uses a thinning algorithm to produce thinned versions of the blurred images. The algorithm's output is then the thinned image with the best performance...
Most conventional smoothing and denoising methods for color images deal with each color channel independently, which results in discolorations due to unbalancing the relation between the color components. In this paper, we propose a smoothing algorithm to reduce discolorations based on ”color-lines”. Our iterative algorithm consists of a local color decomposition step by color-line vectors and an...
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