Showing papers on "Grayscale published in 2013"
TL;DR: A very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD), which produces state-of-the-art results on grayscale as well as color images.
Abstract: In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented.
339 citations
TL;DR: A new image watermarking scheme based on the Redundant Discrete Wavelet Transform (RDWT) and the Singular Value Decomposition (SVD) that showed a high level of robustness not only against the image processing attacks but also against the geometrical attacks which are considered as difficult attacks to resist.
Abstract: Copyright protection and proof of ownership are two of the main important applications of the digital image watermarking. The challenges faced by researchers interested in digital image watermarking applications lie in the creation of new algorithms to serve those applications and to be resistant to most types of attacks, especially the geometrical attacks. Robustness, high imperceptibility, security, and large capacity are four essential requirements in any watermarking scheme. This paper presents a new image watermarking scheme based on the Redundant Discrete Wavelet Transform (RDWT) and the Singular Value Decomposition (SVD). The gray scale image watermark was embedded directly in the singular values of the RDWT sub-bands of the host image. The scheme achieved a large capacity due to the redundancy in the RDWT domain and at the same time preserved high imperceptibility due to SVD properties. Embedding the watermarking pixel's values without any modification inside the wavelet coefficient of the host image overcomes the security issue. Furthermore, the experimental results of the proposed scheme showed a high level of robustness not only against the image processing attacks but also against the geometrical attacks which are considered as difficult attacks to resist.
228 citations
TL;DR: A new NR contrast based grayscale image contrast measure: Root Mean Enhancement (RME); aNR color RME contrast measure CRME which explores the three dimensional contrast relationships of the RGB color channels; and a NR color quality measure Color Quality Enhancement (CQE) which is based on the linear combination of colorfulness, sharpness and contrast.
Abstract: No-reference (NR) image quality assessment is essential in evaluating the performance of image enhancement and retrieval algorithms. Much effort has been made in recent years to develop objective NR grayscale and color image quality metrics that correlate with perceived quality evaluations. Unfortunately, only limited success has been achieved and most existing NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This paper present: a) a new NR contrast based grayscale image contrast measure: Root Mean Enhancement (RME); b) a NR color RME contrast measure CRME which explores the three dimensional contrast relationships of the RGB color channels; c) a NR color quality measure Color Quality Enhancement (CQE) which is based on the linear combination of colorfulness, sharpness and contrast. Computer simulations demonstrate that each measure has its own advantages: the CRME measure is fast and suitable for real time processing of low contrast images; the CQE measure can be used for a wider variety of distorted images. The effectiveness of the presented measures is demonstrated by using the TID2008 database. Experimental results also show strong correlations between the presented measures and Mean Opinion Score (MOS)1.
174 citations
TL;DR: A new way to incorporate spatial information between neighboring pixels into the Gaussian mixture model based on Markov random field (MRF) to demonstrate its robustness, accuracy, and effectiveness, compared with other mixture models.
Abstract: In this paper, a new mixture model for image segmentation is presented. We propose a new way to incorporate spatial information between neighboring pixels into the Gaussian mixture model based on Markov random field (MRF). In comparison to other mixture models that are complex and computationally expensive, the proposed method is fast and easy to implement. In mixture models based on MRF, the M-step of the expectation-maximization (EM) algorithm cannot be directly applied to the prior distribution ${\pi_{ij}}$ for maximization of the log-likelihood with respect to the corresponding parameters. Compared with these models, our proposed method directly applies the EM algorithm to optimize the parameters, which makes it much simpler. Experimental results obtained by employing the proposed method on many synthetic and real-world grayscale and colored images demonstrate its robustness, accuracy, and effectiveness, compared with other mixture models.
150 citations
TL;DR: This work found that activity in V1 allowed predicting the memory color of color-diagnostic objects presented in grayscale in naive participants performing a motion task, and suggested that prior knowledge is projected from midlevel visual regions onto primary visual cortex, consistent with predictive coding theory.
115 citations
TL;DR: A hybrid method of principal component analysis and local binary pattern (LBP) is introduced, which can classify different expressions more effectively and can get higher recognition rate than the traditional recognition methods.
115 citations
TL;DR: A fast vertical edge detection algorithm (VEDA) based on the contrast between the grayscale values, which enhances the speed of the CLPD method and is compared to the Sobel operator in terms of accuracy, algorithm complexity, and processing time.
Abstract: This paper proposes a fast method for car-license-plate detection (CLPD) and presents three main contributions. The first contribution is that we propose a fast vertical edge detection algorithm (VEDA) based on the contrast between the grayscale values, which enhances the speed of the CLPD method. After binarizing the input image using adaptive thresholding (AT), an unwanted-line elimination algorithm (ULEA) is proposed to enhance the image, and then, the VEDA is applied. The second contribution is that our proposed CLPD method processes very-low-resolution images taken by a web camera. After the vertical edges have been detected by the VEDA, the desired plate details based on color information are highlighted. Then, the candidate region based on statistical and logical operations will be extracted. Finally, an LP is detected. The third contribution is that we compare the VEDA to the Sobel operator in terms of accuracy, algorithm complexity, and processing time. The results show accurate edge detection performance and faster processing than Sobel by five to nine times. In terms of complexity, a big-O-notation module is used and the following result is obtained: The VEDA has less complexity by K2 times, whereas K2 represents the mask size of Sobel. Results show that the computation time of the CLPD method is 47.7 ms, which meets the real-time requirements.
112 citations
TL;DR: An image-difference framework that comprises image normalization, feature extraction, and feature combination is presented that shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
Abstract: Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
110 citations
TL;DR: This paper proposes a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline and demonstrates the effectiveness of the proposed multilevelgrayscale + depth fusion scheme.
Abstract: Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.
107 citations
TL;DR: A non-expanded block-based progressive visual secret sharing scheme with noise-like and meaningful shares with several advantages over other related methods, including one that is more suitable for grayscale and color secret images.
93 citations
TL;DR: This paper used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination andSpectral information in this newclass of textures contributes only to form texture.
Abstract: Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.
TL;DR: Investigation of the effect of scanning resolution and reconstruction settings such as noise reduction and 32-bit to 8-bit mapping interval on the 3D X-ray CT imaging of soil structure and the impact on the performance of thresholding methods suggests that the acquisition and reconstruction parameters investigated significantly affect the quality of soil images, and the subsequent thresholding process.
Posted Content•
TL;DR: Two graph-based algorithms for multiclass segmentation of high-dimensional data on graphs using a graph adaptation of the classical numerical Merriman-Bence-Osher (MBO) scheme, which alternates between diffusion and thresholding are presented.
Abstract: We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the functional's double-well potential modified to handle the multiclass case. The first algorithm minimizes the functional using a convex splitting numerical scheme. The second algorithm is a uses a graph adaptation of the classical numerical Merriman-Bence-Osher (MBO) scheme, which alternates between diffusion and thresholding. We demonstrate the performance of both algorithms experimentally on synthetic data, grayscale and color images, and several benchmark data sets such as MNIST, COIL and WebKB. We also make use of fast numerical solvers for finding the eigenvectors and eigenvalues of the graph Laplacian, and take advantage of the sparsity of the matrix. Experiments indicate that the results are competitive with or better than the current state-of-the-art multiclass segmentation algorithms.
03 Sep 2013
TL;DR: This paper presents a novel multi-cue framework for scene segmentation, involving a combination of appearance (grayscale images) and depth cues (dense stereo vision) to create a small set of meaningful free-form region hypotheses for object location and extent.
Abstract: This paper presents a novel multi-cue framework for scene segmentation, involving a combination of appearance (grayscale images) and depth cues (dense stereo vision). An efficient 3D environment model is utilized to create a small set of meaningful free-form region hypotheses for object location and extent. Those regions are subsequently categorized into several object classes using an extended multi-cue bag-of-features pipeline. For that, we augment grayscale bag-of-features by bag-of-depth-features operating on dense disparity maps, as well as height pooling to incorporate a 3D geometric ordering into our region descriptor.
TL;DR: This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulse model.
Abstract: Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulsemodel. We also include the description of themaximum a posteriori (MAP) estimator for each model as well as a summary of general optimization procedures that are typically used to solve the TV problem.
TL;DR: A novel and robust method for automatic vehicle detection using aerial images over highway was presented and the correctness, completeness, and quality rates of the proposed vehicle detection method were about 98%, 93%, and 92%, respectively.
Abstract: A robust and efficient vehicle detection method from high resolution aerial image is still challenging. In this paper, a novel and robust method for automatic vehicle detection using aerial images over highway was presented. In the method, a GIS road vector map was used to constrain the vehicle detection system to the highway networks. After the morphological structure element was identified, we utilized the grayscale opening transformation and grayscale top-hat transformation to identify hypothesis vehicles in the light or white background, and used the grayscale closing transformation and grayscale bot-hat transformation to identify the hypothesis vehicles in the black or dark background. Then, targets with large size or covering a large area were sieved from the hypothesis vehicles using an area threshold that is much larger than a typical vehicle. Targets, whose width is narrower than the diameter of structure element utilized in the grayscale morphological transformation, were smoothed out from the hypothesis vehicles using binary morphological opening transformation. Finally, the hypothesis vehicles detected in both cases were overlaid. It should be noted that in the detection system, a vehicle could be detected twice by the two approaches. The two identical hypothesis vehicles should be amalgamated into a single one for accuracy assessment subsequently. We tested our system on seventeen highway scenes of aerial images with a spatial resolution of 0.15 × 0.15 m. The experimental results showed that the correctness, completeness, and quality rates of the proposed vehicle detection method were about 98%, 93%, and 92%, respectively. Thus, our proposed approach is robust and efficient to detect vehicles of highway using high resolution aerial images.
TL;DR: A new Adaptive Thresholding algorithm that can segment single leaves in a leaf image extracted randomly from an online system is proposed that has an advantage when segmenting complicated leaf images that contain overlapping laminas and have an uneven gray scale in the leaf region itself.
TL;DR: Experimental results show that this algorithm can achieve higher embedding capacity and imperceptible distortion and performance comparisons are provided to demonstrate the feasibility of the proposed algorithm in reversible data hiding.
TL;DR: A triple color image encryption scheme based on chaos is designed, it's suitable for encrypting images in batches, and has been performed to test the validity of the scheme.
TL;DR: Simulation results and security analysis verify the feasibility and effectiveness of this method, and the proposed iterative fractional Fourier transform algorithm has faster convergent speed.
Abstract: A single-channel color image encryption is proposed based on iterative fractional Fourier transform and two-coupled logistic map. Firstly, a gray scale image is constituted with three channels of the color image, and permuted by a sequence of chaotic pairs which is generated by two-coupled logistic map. Firstly, the permutation image is decomposed into three components again. Secondly, the first two components are encrypted into a single one based on iterative fractional Fourier transform. Similarly, the interim image and third component are encrypted into the final gray scale ciphertext with stationary white noise distribution, which has camouflage property to some extent. In the process of encryption and description, chaotic permutation makes the resulting image nonlinear and disorder both in spatial domain and frequency domain, and the proposed iterative fractional Fourier transform algorithm has faster convergent speed. Additionally, the encryption scheme enlarges the key space of the cryptosystem. Simulation results and security analysis verify the feasibility and effectiveness of this method.
TL;DR: A novel watermarking scheme is proposed by embedding a binary watermark into gray-scale images using a hybrid GA-BPN intelligent network, which is robust against selected attacks and is well optimized.
TL;DR: A new, operator‐independent thresholding technique based on the analysis of the intraclass grayscale variance of the unclassified voxels is proposed, which performs noticeably better than Otsu's method, and is robust enough to variations in image contrast and soil structure.
Abstract: Recent advances in imaging techniques offer the possibility of visualizing the three-dimensional structure of soils at very fine scales. To make use of such information, a thresholding process is commonly implemented to separate the image into solid particles and pores. Despite the multitude of thresholding algorithms available, their performance is being challenged by the complexity of the soil structure. Experience shows that, to improve thresholding performance, existing methods require significant input from a skilled operator, making the thresholding subjective. In this context, this article proposes a new, operator-independent thresholding technique based on the analysis of the intraclass grayscale variance. The method extends the well-established Otsu technique, by applying first a preclassification of the voxels corresponding to the solid phase. Then, a threshold value is determined through minimization of the intraclass variance of the unclassified voxels. The method was implemented globally, then locally for a range of window sizes, with the optimal window size selected as that for which the standardized grayscale variances of the two voxel populations are equal. Results on the three-dimensional soil images investigated suggest that the proposed method performs noticeably better than Otsu’s method, and in particular is robust enough to variations in image contrast and soil structure. Tested on a synthetic image, the new method produces a misclassification of only 2% of voxels, compared to 4.9% with Otsu’s method. These results suggest that the proposed method can be very useful in the analysis of images of a variety of heterogeneous media, including soils.
TL;DR: This paper focuses on the simple description of the theory and on the implementation of Cham- bolle's projection algorithm for minimizing the total variation of a grayscale image and adapts the algorithm to the vectorial total variation for color images.
Abstract: Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f = u + �, andis supposed to be at each pixel inde- pendently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the bound- aries of the objects. This paper focuses on the simple description of the theory and on the implementation of Cham- bolle's projection algorithm for minimizing the total variation of a grayscale image. Further- more, we adapt the algorithm to the vectorial total variation for color images. The implementa- tion is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.
Journal Article•
01 Jan 2013-International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy
TL;DR: The simulated results show that AVD can be used as an alternative to the classical inertia of moment (IM) computing and directions do matter while GLCM processing on image pattern and it is shown that Sobel edge-detector operator along with G LCM may be used to predict the surface texture quantitatively.
Abstract: Texture is literally defined as consistency of a substance or a surface. Technically, it is the pattern of information or arrangement of structure found in an image. Texture is a crucial characteristic of many image type and textural features have a plethora of application viz., image processing, remote sensing, content-based imaged retrieval and so on. There are various ways of extracting these features and the most common way is by using a gray-level cooccurrence matrix (GLCM). GLCM contains second order statistical information of neighbouring pixels of an image. In the present work, a detailed study on a sample image (8 bit gray scale image) pattern is carried out with an aim to develop a methodology that acts as a non destructive and contactless way of describing the surface texture. The study involves the use of a contemporary method, known as absolute value of differences (AVD) when the information of the image is not present in higher frequency domain. The simulated results show that AVD can be used as an alternative to the classical inertia of moment (IM) computing and directions do matter while GLCM processing on image pattern. Moreover it is shown that Sobel edge-detector operator along with GLCM may be used to predict the surface texture quantitatively.
TL;DR: This paper presents an efficient spatial domain based image hiding scheme, using Particle Swarm Optimization (PSO), used to find the best pixel locations in a gray scale cover image where the secret gray scale image pixel data can be embedded.
02 Jun 2013
TL;DR: A generic convex energy functional that is suitable for both grayscale and vector-valued images and proves convexity and develops a dual definition for the proposed energy, which gives rise to an efficient and parallelizable minimization algorithm.
Abstract: We introduce a generic convex energy functional that is suitable for both grayscale and vector-valued images. Our functional is based on the eigenvalues of the structure tensor, therefore it penalizes image variation at every point by taking into account the information from its neighborhood. It generalizes several existing variational penalties, such as the Total Variation and vectorial extensions of it. By introducing the concept of patch-based Jacobian operator, we derive an equivalent formulation of the proposed regularizer that is based on the Schatten norm of this operator. Using this new formulation, we prove convexity and develop a dual definition for the proposed energy, which gives rise to an efficient and parallelizable minimization algorithm. Moreover, we establish a connection between the minimization of the proposed convex regularizer and a generic type of nonlinear anisotropic diffusion that is driven by a spatially regularized and adaptive diffusion tensor. Finally, we perform extensive experiments with image denoising and deblurring for grayscale and color images. The results show the effectiveness of the proposed approach as well as its improved performance compared to Total Variation and existing vectorial extensions of it.
TL;DR: A new algorithm for efficient computation of morphological operations for gray images and the specific hardware based on a new recursive morphological decomposition method of 8-convex structuring elements by only causal two-pixelstructuring elements (2PSE).
Abstract: This paper presents a new algorithm for efficient computation of morphological operations for gray images and the specific hardware. The method is based on a new recursive morphological decomposition method of 8-convex structuring elements by only causal two-pixel structuring elements (2PSE). Whatever the element size, erosion or/and dilation can then be performed during a unique raster-like image scan involving a fixed reduced analysis neighborhood. The resulting process offers low computation complexity combined with easy description of the element form. The dedicated hardware is generic and fully regular, built from elementary interconnected stages. It has been synthesized into an FPGA and achieves high-frequency performances for any shape and size of structuring element.
TL;DR: In the process of encryption and decryption, chaotic permutation and diffusion makes the resultant image nonlinear and disorder both in spatial domain and frequency domain, and the proposed phase iterative algorithm has faster convergent speed.
TL;DR: Two segmentation-free algorithms for automating the calibration process of digital and analog measuring instruments without built-in communication interface are proposed based on template matching with normalized cross correlation for reading the display digits and the second uses radial projections and Bresenham algorithm to determine the pointer position in analog instruments.
01 Jan 2013
TL;DR: In this article, a detailed study on a sample image (8 bit gray scale image) pattern is carried out with an aim to develop a methodology that acts as a non destructive and contactless way of describing the surface texture.
Abstract: 2 ABSTRACT: Texture is literally defined as consistency of a substance or a surface. Technically, it is the pattern of information or arrangement of structure found in an image. Texture is a crucial characteristic of many image type and textural features have a plethora of application viz., image processing, remote sensing, content-based imaged retrieval and so on. There are various ways of extracting these features and the most common way is by using a gray-level co- occurrence matrix (GLCM). GLCM contains second order statistical information of neighbouring pixels of an image. In the present work, a detailed study on a sample image (8 bit gray scale image) pattern is carried out with an aim to develop a methodology that acts as a non destructive and contactless way of describing the surface texture. The study involves the use of a contemporary method, known as absolute value of differences (AVD) when the information of the image is not present in higher frequency domain. The simulated results show that AVD can be used as an alternative to the classical inertia of moment (IM) computing and directions do matter while GLCM processing on image pattern. Moreover it is shown that Sobel edge-detector operator along with GLCM may be used to predict the surface texture quantitatively.