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Recently, Template matching approach has been widely used to locate faces with various pose, illumination and clutter background. Normalized Cross-correlation (NCC) is an effective and simple measurement method to compute the similarity matching between the stored faces templates and the rectangular blocks of the input image to locate the face position. However, localization error occurs very often...
This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance...
In this paper a novel logarithmic edge detection algorithm is presented. The algorithm is an extended and modified version of PLIP Sobel edge detection algorithm. Six new kernels are suggested to achieve a higher level of independence from scene illumination and provide obvious distinction between edge and non-edge pixels. We present experimental results for this method, and compare results of the...
Effective foreground detection under sudden illumination change is an active research topic. However, most existing background subtraction approaches, which are intensity based, fail to handle this situation. In this paper, we propose a novel background modeling method that overcomes this limitation by relying on statistical models which use pixel phase instead of intensities. We first extract the...
This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy...
In this paper, we propose a new appearance based approach for palmprint recognition, which combines Kernel Spectral Regression Discriminant Analysis (KSRDA) method and HOG representation. KSRDA is the kernel version of SRDA which has lower computation complexity than Linear Discriminant Analysis (LDA). Meanwhile, HOG representation isn't sensitive to changes of illumination, and has the robustness...
In this paper we present a planar fiducial marker system to be used with color cameras. Our system provides precise and robust full 3D pose estimation of the markers with superior accuracy when compared with many fiducial systems, while color information is used to provide more than 65,000 distinct markers. In contrast with most color-based fiducial frameworks, ours requires no prior classification...
Facial analysis based on local regions/blocks usually outperforms holistic approaches because it is less sensitive to local deformations and occlusions. Moreover, modeling local features enables us to avoid the problem of high dimensionality of feature space. In this paper, we model the local face blocks with Gabor features and project them into a discriminant identity space. The similarity score...
In this paper, we propose a novel illumination-normalization method. By using the combination of the Kernel Principal Component Analysis (KPCA) and Pre-image technology, this method can restore the frontal-illuminated face image from a single non-frontal-illuminated face image. In this method, a frontal-illumination subspace is first learned by KPCA. For each input face image, we project its large-scale...
A logarithmic CMOS image sensor is proposed with a readout circuit that allows the direct calculation of a weighted average of pixels in a column. The kernel weights are controlled through bias voltages in a set of variable-gain current mirrors. A detailed description of the circuit topology and design is given. The circuitry was simulated using 1.2°m CMOS technology. The simulation shows that inaccuracies...
In this paper, the fuzzy flood fill mean shift algorithm is introduced. This algorithm is developed for the methodology of robust segmentation by improving the mean shift algorithm through the fuzzy kernels and the flood fill technique, instead of those based on the spatial bandwidth. Due to this exchange, the flood fill mean shift involves only one parameter, the range bandwidth, which is less sensitive...
Winder et al. have recently shown the superiority of the DAISY descriptor in comparison to other widely extended descriptors such as SIFT and SURF. Motivated by those results, we present a novel algorithm that extracts viewpoint and illumination invariant keypoints and describes them with a particular implementation of a DAISY-like layout. We demonstrate how to efficiently compute the scale-space...
Background modeling plays an important role in video surveillance, yet in complex scenes it is still a challenging problem. Among many difficulties, problems caused by illumination variations and dynamic backgrounds are the key aspects. In this work, we develop an efficient background subtraction framework to tackle these problems. First, we propose a scale invariant local ternary pattern operator,...
Camera shake during photography is a common problem which causes images to get blurred. Here we choose a specific problem in which the image is a barcode and the motion can be modeled as a convolution. We design a blind deconvolution algorithm to remove the translatory motion from a blurred barcode image. Based on the bimodal characteristics of barcode image histograms, we construct a simple target...
In this paper, we propose to show that a particular fuzzy extension of mathematical morphology coincides with a non-additive extension of linear filtering based on convolution kernels thus bridging the two approaches.
The paper proposes an adaptive classification mechanism designed for structured light system to improve quality of reconstructed models. We observed that the conventional albedo-based thresholding fails when the lighting condition is not carefully considered. To address this problem, an adaptive model is proposed. The core idea is to adjust decision boundary during extraction of sequence of binary-coded...
Background modeling and subtraction is a core component of many vision based systems. By far the most popular background models are per-pixel models, in which each pixel is considered independently. Such models fail to handle dynamic backgrounds and noise. In this paper, we present a solution to this problem by proposing a novel and computationally simple spatio-temporal background model. We extend...
The filter flow problem is to compute a space-variant linear filter that transforms one image into another. This framework encompasses a broad range of transformations including stereo, optical flow, lighting changes, blur, and combinations of these effects. Parametric models such as affine motion, vignetting, and radial distortion can also be modeled within the same framework. All such transformations...
Content-based multimedia database indexing and retrieval tasks require automatic extraction of descriptive features that are relevant to the subject materials i.e., images, video etc. The typical low-level features that are extracted in images and video include measures of color, texture, or shape. Although these features can easily be obtained, they do not give a precise idea of the image content...
Segmenting images into distinct material types is a very useful capability. Most work in image segmentation addresses the case where only a single image is available. Some methods improve on this by collecting HDR or multispectral images. However, it is also possible to use the reflectance properties of the materials to obtain better results. By acquiring many images of an object under different lighting...
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