Showing papers on "Grayscale published in 2014"

Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

Abstract: This paper presents an optimized watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The singular values of a binary watermark are embedded in singular values of the LL3 sub-band coefficients of the host image by making use of multiple scaling factors (MSFs). The MSFs are optimized using a newly proposed Firefly Algorithm having an objective function which is a linear combination of imperceptibility and robustness. The PSNR values indicate that the visual quality of the signed and attacked images is good. The embedding algorithm is robust against common image processing operations. It is concluded that the embedding and extraction of the proposed algorithm is well optimized, robust and show an improvement over other similar reported methods.

A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition

Abstract: In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local derivative directions from the \(n\) th-order LVP in \((n-1)\) th-order derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.

Object reconstitution using pseudo-inverse for ghost imaging

Abstract: We propose a novel method for object reconstruction of ghost imaging based on Pseudo-Inverse, where the original objects are reconstructed by computing the pseudo-inverse of the matrix constituted by the row vectors of each speckle field. We conduct reconstructions for binary images and gray-scale images. With equal number of measurements, our method presents a satisfying performance on enhancing Peak Signal to Noise Ratio (PSNR) and reducing computing time. Being compared with the other existing methods, its PSNR distinctly exceeds that of the traditional Ghost Imaging (GI) and Differential Ghost Imaging (DGI). In comparison with the Compressive-sensing Ghost Imaging (CGI), the computing time is substantially shortened, and in regard to PSNR our method exceeds CGI on grayscale images and performs as well as CGI visually on binary images. The influence of both the detection noise and the accuracy of measurement matrix on PSNR are also presented.

Image Encryption Algorithm Based on DNA Encoding and Chaotic Maps

Abstract: We propose a new image encryption algorithm based on DNA sequences combined with chaotic maps. This algorithm has two innovations: (1) it diffuses the pixels by transforming the nucleotides into corresponding base pairs a random number of times and (2) it confuses the pixels by a chaotic index based on a chaotic map. For any size of the original grayscale image, the rows and columns are fist exchanged by the arrays generated by a logistic chaotic map. Secondly, each pixel that has been confused is encoded into four nucleotides according to the DNA coding. Thirdly, each nucleotide is transformed into the corresponding base pair a random number of time(s) by a series of iterative computations based on Chebyshev’s chaotic map. Experimental results indicate that the key account of this algorithm is 1.536 × 10127, the correlation coefficient of a 256 × 256 Lena image between, before, and after the encryption processes was 0.0028, and the information entropy of the encrypted image was 7.9854. These simulation results and security analysis show that the proposed algorithm not only has good encryption effect, but also has the ability to repel exhaustive, statistical, differential, and noise attacks.

Variational Exemplar-Based Image Colorization

Abstract: In this paper, we address the problem of recovering a color image from a grayscale one. The input color data comes from a source image considered as a reference image. Reconstructing the missing color of a grayscale pixel is here viewed as the problem of automatically selecting the best color among a set of color candidates while simultaneously ensuring the local spatial coherency of the reconstructed color information. To solve this problem, we propose a variational approach where a specific energy is designed to model the color selection and the spatial constraint problems simultaneously. The contributions of this paper are twofold. First, we introduce a variational formulation modeling the color selection problem under spatial constraints and propose a minimization scheme, which computes a local minima of the defined nonconvex energy. Second, we combine different patch-based features and distances in order to construct a consistent set of possible color candidates. This set is used as input data and our energy minimization automatically selects the best color to transfer for each pixel of the grayscale image. Finally, the experiments illustrate the potentiality of our simple methodology and show that our results are very competitive with respect to the state-of-the-art methods.

Contrast Preserving Decolorization with Perception-Based Quality Metrics

Abstract: Converting color images into grayscale ones suffer from information loss. In the meantime, it is one fundamental tool indispensable for single channel image processing, digital printing, and monotone e-ink display. In this paper, we propose an optimization framework aiming at maximally preserving color contrast. Our main contribution is threefold. First, we employ a bimodal objective function to alleviate the restrictive order constraint for color mapping. Second, we develop an efficient solver that allows for automatic selection of suitable grayscales based on global contrast constraints. Third, we advocate a perceptual-based metric to measure contrast loss, as well as content preservation, in the produced grayscale images. It is among the first attempts in this field to quantitatively evaluate decolorization results.

A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification

Abstract: Previous works about spatial information incorporation into a traditional bag-of-visual-words (BOVW) model mainly consider the spatial arrangement of an image, ignoring the rich textural information in land-use remote-sensing images. Hence, this article presents a 2-D wavelet decomposition (WD)-based BOVW model for land-use scene classification, since the 2-D wavelet decomposition method does well not only in textural feature extraction, but also in the multi-resolution representation of an image, which is favourable for the use of both spatial arrangement and textural information in land-use images. The proposed method exploits the textural structures of an image with colour information transformed into greyscale. Moreover, it works first by decomposing the greyscale image into different sub-images using 2-D discrete wavelet transform (DWT) and then by extracting local features of the greyscale image and all the decomposed images with dense regions in which a given image is evenly sampled by a regular gr...

Seam Carving for Text Line Extraction on Color and Grayscale Historical Manuscripts

Abstract: We propose a novel algorithm for automatic text line extraction on color and grayscale manuscript pages without prior binarization. Our algorithm is based on seam carving to compute separating seams between text lines. Seam carving is likely to produce seams that move through gaps between neighboring lines, if no information about the text geometry is incorporated into the problem. By constraining the optimization procedure inside the region between two consecutive text lines, we can produce robust separating seams that do not cut through word and line components. Extensive experimental evaluations on diverse manuscript pages show that we improve upon the state-of-the-art for grayscale text line extraction.

Iris recognition using image moments and k-means algorithm.

Abstract: This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

Improved color texture descriptors for remote sensing image retrieval

Abstract: Texture features are widely used in image retrieval literature. However, conventional texture features are extracted from grayscale images without taking color information into con- sideration. We present two improved texture descriptors, named color Gabor wavelet texture (CGWT) and color Gabor opponent texture (CGOT), respectively, for the purpose of remote sensing image retrieval. The former consists of unichrome features computed from color chan- nels independently and opponent features computed across different color channels at different scales, while the latter consists of Gabor texture features and opponent features mentioned above. The two representations incorporate discriminative information among color bands, thus describing well the remote sensing images that have multiple objects. Experimental results demonstrate that CGWT yields better performance compared to other state-of-the-art texture features, and CGOT not only improves the retrieval results of some image classes that have unsatisfactory performance using CGWT representation, but also increases the average precision of all queried images further. In addition, a similarity measure function for proposed represen- tation CGOT has been defined to give a convincing evaluation. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1 .JRS.8.083584)

Design of Sobel operator based image edge detection algorithm on FPGA

Abstract: Real-time image processing applications requires processing on large data of pixels in a given timing constraints. Reconfigurable device like FPGAs have emerged as promising solutions for reducing execution times by deploying parallelism techniques in image processing algorithms. Implementation of highly parallel system architecture, parallel access of large internal memory banks and optimization of processing element for applications makes FPGA an ideal device for image processing system. Edge detection is basic tool used in many image processing applications for extracting information from image. Sobel edge detection is gradient based edge detection method used to find edge pixels in image. This paper presents a design of a Sobel edge detection algorithm to find edge pixels in gray scale image. Xilinx ISE Design Suite-14 software platforms is used to design a algorithm using VHDL language. MATLAB software platform is used for obtaining pixel data matrix from gray scale image and vice versa. Xilinx FPGAs of family Vertex-5 are more suitable for image processing work than Spartan-3 and Spartan-6.

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

Abstract: In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS-MRI database which is the magnetic resonance imaging (MRI) database and VIA/I-ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.

An absolute rotary position sensor based on cylindrical coordinate color space transformation

Abstract: In this paper, a novel, very simple and low-cost absolute rotary position sensor is presented. The sensor operation is based on the RGB to cylindrical coordinate color space transformation. A very simple experimental setup based on optical reflective sensors for absolute rotary position sensing in order to demonstrate proof of concept is given. Instead of using complex solution consisting of color sensor and printed color wheel, a much simpler solution that uses three optical reflective sensors and grayscale wheel is proposed. The proposed sensor has linear characteristics with accuracy below ±1°, resolution of 0.1° and linearity with R 2 of 0.99998, within the measurement range from 0° to 360°.

Chalcogenide phase-change thin films used as grayscale photolithography materials.

Abstract: Chalcogenide phase-change thin films are used in many fields, such as optical information storage and solid-state memory. In this work, we present another application of chalcogenide phase-change thin films, i.e., as grayscale photolithgraphy materials. The grayscale patterns can be directly inscribed on the chalcogenide phase-change thin films by a single process through direct laser writing method. In grayscale photolithography, the laser pulse can induce the formation of bump structure, and the bump height and size can be precisely controlled by changing laser energy. Bumps with different height and size present different optical reflection and transmission spectra, leading to the different gray levels. For example, the continuous-tone grayscale images of lifelike bird and cat are successfully inscribed onto Sb2Te3 chalcogenide phase-change thin films using a home-built laser direct writer, where the expression and appearance of the lifelike bird and cat are fully presented. This work provides a way to fabricate complicated grayscale patterns using laser-induced bump structures onto chalcogenide phase-change thin films, different from current techniques such as photolithography, electron beam lithography, and focused ion beam lithography. The ability to form grayscale patterns of chalcogenide phase-change thin films reveals many potential applications in high-resolution optical images for micro/nano image storage, microartworks, and grayscale photomasks.

Devil’s Vortex Phase Structure as Frequency Plane Mask for Image Encryption Using the Fractional Mellin Transform

Abstract: A frequency plane phase mask based on Devil’s vortex structure has been used for image encryption using the fractional Mellin transform. The phase key for decryption is obtained by an iterative phase retrieval algorithm. The proposed scheme has been validated for grayscale secret target images, by numerical simulation. The efficacy of the scheme has been evaluated by computing mean-squared-error between the secret target image and the decrypted image. Sensitivity analysis of the decryption process to variations in various encryption parameters has been carried out. The proposed encryption scheme has been seen to exhibit reasonable robustness against occlusion attack.

Data hiding based on improved exploiting modification direction method and huffman coding

Abstract: The rapid growth of covert activities via communications network brought about an increasing need to provide an efficient method for data hiding to protect secret information from malicious attacks. One of the options is to combine two approaches, namely steganography and compression. However, its performance heavily relies on three major factors, payload, imperceptibility, and robustness, which are always in trade-offs. Thus, this study aims to hide a large amount of secret message inside a grayscale host image without sacrificing its quality and robustness. To realize the goal, a new two-tier data hiding technique is proposed that integrates an improved exploiting modification direction (EMD) method and Huffman coding. First, a secret message of an arbitrary plain text of characters is compressed and transformed into streams of bits; each character is compressed into a maximum of 5 bits per stream. The stream is then divided into two parts of different sizes of 3 and 2 bits. Subsequently, each part is transformed into its decimal value, which serves as a secret code. Second, a cover image is partitioned into groups of 5 pixels based on the original EMD method. Then, an enhancement is introduced by dividing the group into two parts, namely k1 and k2, which consist of 3 and 2 pixels, respectively. Furthermore, several groups are randomly selected for embedding purposes to increase the security. Then, for each selected group, each part is embedded with its corresponding secret code by modifying one grayscale value at most to hide the code in a (2ki + 1)-ary notational system. The process is repeated until a stego-image is eventually produced. Finally, the x2 test, which is considered one of the most severe attacks, is applied against the stego-image to evaluate the performance of the proposed method in terms of its robustness. The test revealed that the proposed method is more robust than both least significant bit embedding and the original EMD. Additionally, in terms of imperceptibility and capacity, the experimental results have also shown that the proposed method outperformed both the well-known methods, namely original EMD and optimized EMD, with a peak signal-to-noise ratio of 55.92 dB and payload of 52,428 bytes.

Fine-tuned grayscale optofluidic maskless lithography for three-dimensional freeform shape microstructure fabrication.

Abstract: This article presents free-floating three-dimensional (3D) microstructure fabrication in a microfluidic channel using direct fine-tuned grayscale image lithography The image is designed as a freeform shape and is composed of gray shades as light-absorbing features Gray shade levels are modulated through multiple reflections of light in a digital micromirror device (DMD) to produce different height formations Whereas conventional photolithography has several limitations in producing grayscale colors on photomask features, our method focuses on a maskless, single-shot process for fabrication of freeform 3D micro-scale shapes The fine-tuned gray image is designed using an 8-bit grayscale color; thus, each pixel is capable of displaying 256 gray shades The pattern of the UV light reflecting on the DMD is transferred to a photocurable resin flowing through a microfluidic channel Here, we demonstrate diverse free-floating 3D microstructure fabrication using fine-tuned grayscale image lithography Additionally, we produce polymeric microstructures with locally embedded gray encoding patterns, such as grayscale-encoded microtags This functional microstructure can be applied to a biophysical detection system combined with 3D microstructures This method would be suitable for fabricating 3D microstructures that have a specific morphology to be used for particular biological or medical applications

Automatic vehicle number plate detection and recognition

Abstract: License plate recognition (LPR) plays a significant role throughout this busy world, owing to the rise in vehicles day by day. Stealing of vehicles, breaking traffic rules, coming into restricted space also are increasing linearly, thus to dam this act registration code recognition is intended. Among the fundamental process steps such as detection of number plate, segmentation of characters and recognition of each characters, segmentation plays an important art, since the accuracy of recognition is based on how perfect the segmentation is done. To avoid problems like unwanted illumination, tilt that degrades the segmentation which in turn affects the recognition accuracy numerous algorithms are developed for this work. This paper presents a strong technique for localisation, segmentation and recognition of the characters within the located plate. Images from still cameras or videos are obtained and regenerated in to grayscale images. Hough lines are determined using Hough transform and therefore the segmentation of grey scale image generated by finding edges for smoothing image is employed to cut back the quantity of connected part and then connected part is calculated. Finally, single character within the registration code is detected. The aim is to indicate that the planned technique achieved high accuracy by optimizing numerous parameters that has higher recognition rate than the standard ways.

Robust Text Detection in Natural Scene Images by Generalized Color-Enhanced Contrasting Extremal Region and Neural Networks

Abstract: This paper presents a robust text detection approach based on generalized color-enhanced contrasting extremal region (CER) and neural networks. Given a color natural scene image, six component-trees are built from its gray scale image, hue and saturation channel images in a perception-based illumination invariant color space, and their inverted images, respectively. From each component-tree, generalized color-enhanced CERs are extracted as character candidates. By using a "divide-and-conquer" strategy, each candidate image patch is labeled reliably by rules as one of five types, namely, Long, Thin, Fill, Square-large and Square-small, and classified as text or non-text by a corresponding neural network, which is trained by an ambiguity-free learning strategy. After pruning non-text components, repeating components in each component-tree are pruned by using color and area information to obtain a component graph, from which candidate text-lines are formed and verified by another set of neural networks. Finally, results from six component-trees are combined, and a post-processing step is used to recover lost characters and split text lines into words as appropriate. Our proposed method achieves 85.72% recall, 87.03% precision, and 86.37% F-score on ICDAR-2013 "Reading Text in Scene Images" test set.

Maximum Entropy for Image Segmentation based on an Adaptive Particle Swarm Optimization

Abstract: Image segmentation is applied widely to image processing and object recognition. Threshold segmentation is a simple and important method in grayscale image segmentation. Information entropy can characterize the grayscale in formation of image and distinguish between the objectives and background. In this paper, we use exponential entropy instead of logarithmic entropy and propose a new multilevel thresholds image segmentation method based on maximum entropy and adaptive Particle Swarm Optimization (APSO). This proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. The APSO takes advantage of the characteristics of particle swarm optimization, through adaptively adjust particles flying speed to improve evolutional process of basic PSO. Standard test images and remote sensing image are segmented in experiment and compared with other related segmentation methods. Experimental results show that the APSO method can quickly converge with high computational efficiency.