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In this work we propose a novel variational method that we intend to use for estimating non-rigid texture deformation. The method is able to capture variation in gray scale images with respect to the geometry of its features. Accurate localization of features in the presence of unknown deformations is a crucial property for texture characterization. Our experimental evaluations demonstrate that accounting...
This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) Forward Looking Infrared (FLIR) imagery. The proposed approach called kernel wavelet-RX algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high dimensional feature space (possibly infinite) via a certain nonlinear mapping function...
We address the problem of license plate detection in video surveillance systems. The Adaboost based approach, known for relative ease of implementation, makes use of discriminative features such as edges or Haar-like features. In this paper, we propose a novel detection algorithm based on local structure patterns for license plate detection. The proposed algorithm includes post-processing methods...
A novel detection and tracking system for bagged cargo is proposed in this paper. First, a boosted cascade classifier based on Haar features is designed to recognize and locate the motion region together with frame difference. Second, a block region grow method is proposed to avoid the illumination and shadow interference in the frame difference image. Finally, template matching and mean shift are...
Gabor wavelet transform is one of the most effective feature extraction techniques for textures. As the Gabor wavelets are believed to be rather consistent to the response of Human Vision System (HVS), and many successful examples are reported in the areas of texture analysis. However, computational complexity of the feature extraction is still high even for computers nowadays, especially large sized...
Field Programmable Gate Array (FPGA) is an effective device to realize real-time parallel processing of vast amounts of video data because of the fine-grain reconfigurable structures. This paper presents a kind of parallel processing construction of Sobel edge detection enhancement algorithm, which can quickly get the result of one pixel in only one clock periods. The algorithm is designed with a...
Extracting distinctive scale invariant features from images of the same scene or object is very important in many computer vision applications, and there has been significant research into the scale invariant feature detectors and descriptors. Some of these methods have emphasized on computational speed and accuracy, so that they can enable lots of real-time applications with reduced computational...
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...
Supervised Learning (SL) is a machine learning research area which aims at developing techniques able to take advantage from labeled training samples to make decisions over unseen examples. Recently, a lot of tools have been presented in order to perform machine learning in a more straightforward and transparent manner. However, one problem that is increasingly present in most of the SL problems being...
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...
This paper addresses the development of an automatic segmentation technique for detecting cell nuclei. The technique uses a new approach for segmenting nuclei in images taken from tissues with colon carcinoma. The segmentation problems encountered in these images and solved by the proposed technique are related to the non-uniform illumination on the background, out-of-focus nuclei, the physical structure...
In this paper we present a scene exploration method for the identification of interest regions in unknown indoor environments and the position estimation of the objects located in those regions. Our method consists of two stages: First, we generate a saliency map of the scene based on the spectral residual of three color channels and interest points are detected in this map. Second, we propose and...
The use of region shape descriptors was investigated for categorisation of textile design images. Images were segmented using MRF pixel labelling and the shapes of regions obtained were described with generic Fourier descriptors. Each image was represented as a bag of shapes. A simple yet competitive classification scheme based on nearest neighbour class-based matching was used. Classification performance...
In this paper, we introduce a new sharpening method which guarantees colour constancy and resolves the problem of equi-luminance colours. The algorithm is similar to unsharp masking in that the gradients are calculated at different scales by blurring the original with a variable size kernel. The main difference is in the blurring stage where we calculate the average of an n × n neighborhood by projecting...
We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macro-blocks of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks will be solved by estimating the proper covariance...
We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification. Local co-occurrences of visual words effectively characterize the spatial alignment of objects' components. The visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid quantization error in assigning the words...
The goal of this paper is the development of a novel approach for the problem of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces (RKHS). The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated semi parametric Representer Theorem. Examples verify that in the presence of gaussian noise the proposed method performs relatively well compared...
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...
This paper extends a recent image-dependent regularisation approach introduced in aiming at edge-preserving smoothing. For that purpose, geodesic distances equipped with a Riemannian metric need to be estimated in local neighbourhoods. By deriving an appropriate metric from the gradient structure tensor, the associated geodesic paths are constrained to follow salient features in images. Following,...
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...
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