Input Color Image Research Articles

Securing the regions comprising information in a document image with novel key selection technique – a parallel approach

Securing the information contents in an image with increase in speed is a challenging issue even today. In this paper, a parallel approach to secure information contents of important regions in a document image is proposed. The novelty of the approach are to identify intelligently the regions comprising information is to be secured, generating and selecting the key sequence. The proposed method comprises of four stages. In the first stage, rules of cellular automata are selected randomly to generate the key sequence. In the second stage, key sequences are selected on the basis of sarpabandha which depicts pattern of the movement of snake. The input color image is decomposed into non overlapping blocks of uniform size which are processed in parallel. Using Grey Level Co-occurrence Matrix (GLCM), entropy feature is extracted for each block and based on the feature value classification of blocks are carried out in the third stage. In the last stage encryption operation is performed for each plane of input color image separately. Red, green and blue color planes of input color image are fused with the key sequences to perform the encryption. Cipher is feedback to enhance the diffusion of information. The proposed methodology is analyzed in terms of parameters peak signal to noise ratio, entropy, structural similarity index and correlation coefficient. In the proposed work, identification of regions and encryption both are processed in parallel to reduce the time of computation.

A novel approach-foot tumour detection algorithm using watershed and thresholding based segmentation.

Detection of foot tumour has always been a little tedious as it is generally confounded with other diseases of the foot. Hence, the method of detecting foot tumour is of utmost importance. Though image processing techniques have replaced the original biopsy method, these classical methods are still attributed to a few drawbacks. In this paper we have proposed an efficient methodology which extracts the image and converts an input color image to YcBcRc components and each component of YcBcRc component are extracted separately to detect the exact size and location of tumour region in the foot using threshold and watershed segmentation algorithms by applying morphological operators. After the scanned MRI is enhanced using segmentation, the tumour can be detected effectively.

Asymmetric encryption algorithm for colour images based on fractional Hartley transform

ABSTRACTThis paper presents a new scheme for encryption of single-channel colour images. The scheme uses amplitude- and phase-truncation approach to introduce non-linearity for enhanced security. Further, the colour image encryption is performed in the fractional Hartley domain, which is relatively less investigated. The encryption starts with an affine transform of each channel of the input colour image. Thereafter, one of the channels is considered as the input amplitude image while the other two are used as phase masks, one in the spatial and the other in the frequency domain. A detailed analysis of the scheme’s sensitivity to various encryption parameters has been carried out. In addition, security analysis of the scheme against attacks establishes the scheme’s robustness. The combined use of the affine transform and phase-truncation approach for colour image encryption in the fractional Hartley domain is attempted for the first time in this study. It is shown that the proposed scheme resists the special attack.

UTILIZATION OF IMAGE AND SIGNAL PROCESSING TECHNIQUES FOR ASSESSMENT OF BUILT HERITAGE CONDITION

Historical buildings represent invaluable heritage from the past and therefore their protection is a very important task. Assessment of their condition must not cause damage accumulation, thus the least possible volume removed from the structure is essential. As many historical buildings in the Czech Republic are built using sandstone that can be considered as a typical heterogeneous system, statistical signal processing is a promising approach for determination of the representative volume element (RVE) dimensions. Such calculations can be carried out on the domain of logical arrays representing binary images of the materials microstructure. This paper deals with processing of image data obtained using SEM-BSE and high resolution flatbed scanner for determination of RVE dimensions. Advanced image processing techniques are employed and results from calculation using grayscale data are presented and compared with results calculated on the basis of color input images.

Salient object segmentation based on depth-aware image layering

This paper proposes an efficient salient object segmentation method via depth-aware image layering. First, based on the multiscale region segmentation results of an input color image, the depth consistency integration is utilized to generate the image pre-segmentation result. Then, under the guidance of the depth histogram division, the pre-segmented regions are divided into several different layers to differentiate salient object regions and background regions. Finally, an adaptive sample update and selection method based on layered image regions is used to select appropriate training samples for salient object segmentation. The depth information of the image is fully utilized in each step of the entire framework. Experimental results on two public datasets demonstrate that the proposed method achieves the better performance than the state-of-the-art depth-aware salient object segmentation methods.

Optimal visual cryptographic scheme with multiple share creation for multimedia applications

The criminal activity of hackers to retrieve the highly secured information on multimedia application generates major security concerned problems towards the human life. Basically, the visual cryptographic scheme generates numerous unreasonable image shares with specific information. The secret information hidden in the image can be retrieved by accumulating all the shares. The basic idea that relays on this cryptographic scheme is the encryption of image which hides the secret into m numerous image shares. By this process, it turns to be complex for the hackers to retrieve an original data of the image. Moreover, to increase the confidentiality and privacy of the original image there is in demand to make use of the efficient encryption algorithm. In this paper, the proposed visual cryptographic scheme is divided into three phases namely, (a) Separation of color bands, (b) Generation of numerous shares and (c) Optimal Encryption and Decryption. For optimal encryption and decryption of the image, we propose an OGWO based ECC (Elliptic Curve Cryptographic) approach. Initially, the input color image is separated into three color bands such as R, G and B. Subsequently, multiple image shares are generated based on the pixel measures. After this process, the shares of the image are partitioned into blocks and decrypt the original image using the optimal key generated by the OGWO (Oppositional Gray Wolf Optimization) algorithm. From the experimental analysis, we inferred that for the decrypted image PSNR achieved is 59.012, 0.11 for MSE and the CC is 1, without experiencing any deviations in the original image.

CONTRAST ENHANCEMENT FOR COLOR IMAGES USING AN ADJUSTABLE CONTRAST STRETCHING TECHNIQUE

With the growing demand for high-quality color images, efficient yet low-complexity methods are increasingly needed for better visualization. Unfortunately, the low-contrast is one prevalent effect that degrades color images due to various unavoidable limitations. Hence, a new adjustable contrast stretching technique is proposed in this article to improve the contrast of color images. The processing scheme of the proposed technique is relatively simple. It starts by converting the input color image to grayscale. Then, it automatically computes two contrast tuning parameters depending on the pre-determined grayscale image. Finally, it improves the contrast of the degraded color image using an amended version of an existing contrast stretching technique. Accordingly, its input is a color image and a contrast adjustment parameter δ, while its output is a contrast-adjusted color image. The proposed technique is tested by conducting intensive experiments on real-degraded images, and it is compared with four well-known contrast enhancement techniques. In addition, the proposed and comparative techniques are evaluated based on three eminent no-reference image quality assessment metrics. From the performance analysis of the achieved experiments and comparisons, the proposed technique provided satisfying performances and outperformed the comparative techniques in terms of recorded accuracy and perceived quality.

Robust Color Image Hashing Using Quaternion Polar Complex Exponential Transform for Image Authentication

Image hashing is one of the multimedia protection techniques. In this paper, a new method for robust image hashing based on quaternion polar complex exponential transform (QPCET) is proposed. The proposed method targets two goals. The first goal is the robustness against geometric and common signal processing attacks. The second one is authenticating color images without conversion which keeps their color information. In the proposed method, the input color image is normalized by the bicubic interpolation and then the interpolated image passes to Gaussian low-pass filter. QPCET moments are used to extract features. Finally, the hash value is calculated using the extracted features. On the sender side, a secret key is utilized to increase the protection of the hash value before transmitting it. The hash value is attached with the transmitted color image. On the receiver side, the authenticity of the received image is checked by decrypting the hash value. Euclidean distance is used to check the similarity between different hashes. Results of the conducted experiments prove the robustness of proposed hash against different geometric and signal processing attacks. Also, it preserves the content of the transmitted color image. Hashing different images has a very low collision probability which ensure the suitability of the proposed method for image authentication. Comparison with the existing methods ensures the superiority of the proposed method.

Effect of Decolorized Images In Scene Classification Using Deep Convolution Features

Scene classification is considered as an imperative issue for computer vision and has got extensive consideration in the recent past. Due to recent developments in high performance computing units such as GPUs, popularly known deep learning algorithm namely, convolutional neural networks (CNNs), exploits huge datasets to give powerful models. The paper proposes the use of transfer learning technique, by which a pre-trained model known as Places-CNN is used to generate feature vectors for each scene image of the dataset. The scene-classification experiments are conducted on the Oliva Torralba (OT) scene dataset, which consists of eight outdoor scene categories. The features were extracted from the fully connected layer of the pre-trained Places CNN architecture. The deep features were extracted from the input color images and the grayscale images converted using two different techniques based on singular value decomposition (SVD). The results obtained from classification experiments show that, models trained on SVD-Decolorized and Modified-SVD decolorized images give comparable performance to the input color images. Unlike the color images, which use three planes (RGB) of information, the grayscale images use only one plane of information. The grayscale images were able to retain the required shape and texture information from the original RGB images and, thus sufficient to categorize the classes of scene images.

WpmDecolor: weighted projection maximum solver for contrast-preserving decolorization

This paper presents a novel semi-reference inspired color-to-gray conversion model for faithfully preserving the contrast details of the color image, essentially differs from most of the no-reference and reference approaches. In the proposed model, on the basic assumption that a good gray conversion should make the conveyed gradient values (i.e., contrast) to be maximal, we present a projection maximum function to model the decolorization procedure. We further incorporate weights of the original gradients into the maximum function. The Gaussian weighted factor consisting of the gradients of each channel of the input color image is employed to better reflect the degree of preserving feature discriminability and color ordering in color-to-gray conversion. The projected gradient descent and discrete searching techniques are developed to solve the proposed model with and without nonnegative constraint, respectively. Extensive experimental evaluations on two existing datasets, containing abundant colors and patterns, show that the proposed method outperforms the state-of-the-art methods quantitatively and qualitatively.

Automatic color image contrast enhancement using Gaussian mixture modeling, piecewise linear transformation, and monotone piecewise cubic interpolant

Conventional contrast enhancement algorithms use complicated nonlinear mapping functions with parameters specified manually. In this paper, a parameter-free piecewise linear transformation and monotone piecewise cubic interpolation are used for color image contrast enhancement. The Gaussian mixture model is used to divide luminance histogram of input color image into multiple sub-histograms. Each sub-histogram is mapped linearly or nonlinearly to a portion of available output dynamic range proportional to its extent and the number of its pixels. The proposed algorithm has more controlled contrast enhancement than conventional algorithms to preserve natural outlook and local details in the input color image. This is done by considering brightness histogram preservation of input image by proposed algorithm, due to considering extent of each sub-histogram for allocating a portion of output dynamic range to it. Experimental results show that the proposed algorithm produces better or comparable contrast-enhanced images with natural outlook than several conventional algorithms on various types of images.

An adaptive RGB colour enhancement formulation for logarithmic image processing-based algorithms

This paper presents an effective colour enhancement framework for statistical and logarithmic image processing (LIP)-based enhancement algorithms. The proposed approach utilizes the fusion of partial, multiple computed luminance channels with colour image channel statistics obtained from the input colour image for adaptive colour enhancement. The proposed scheme does not modify the image intensity channel, avoiding colour fading typically observed in colour images processed with conventional algorithms. The colour enhancement scheme compensates for the weaknesses of greyscale-based contrast enhancement and illumination normalization algorithms by focusing on preserving/restoring or enhancing colour. The proposed system avoids the conversion to complex, non-linear colour spaces such as HSI and HSV while producing similar results without manual adjustment of parameters. Additionally, an adaptive scheme for detection of images with unbalanced colour and uneven illumination is combined with the proposed system. Results show that the proposed scheme augments colour results of greyscale-based contrast enhancement algorithms and is relatively less complex compared to most algorithms in the literature.

Image coding based on classified vector quantisation using edge orientation patterns

Vector quantisation (VQ) shows a good performance for image coding with high-compression ratios. However, there are many difficulties for image coding with VQ, especially the edge degradation and high-computational complexity. To resolve these two problems, the authors propose a new coding method based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their edge orientations. For colour image coding, 27 codebooks (nine for each colour component) are pre-designed based on a series training images. In the encoding stage, an input colour image is decomposed into Y, Cb, and Cr components, and each component image is divided into non-overlapping 4 × 4 blocks. For each block, eight edge orientation templates of size 4 × 4 are performed to determine its edge orientation. According to the edge orientation, each block is compressed by using the corresponding codebook. Essentially, the authors’ scheme is a kind of classified VC (CVQ). Simulation results show that, their EOP-based CVQ can largely improve the compression efficiency as well as speeding up the encoding process and it is sufficient to establish effectiveness of the authors’ algorithm as compared with the existing techniques.

PERANCANGAN APLIKASI PENYISIPAN CITRA BERWARNA PADA QR CODE DENGAN METODE HALFTONE

The project is made in the form of a software application that accepts input color image to be processed into Halftone QR-Code. Halftone QR-Code is a composite image of the QR-Code and halftone image that can be recognized by QR-Code Scanner. QR-Code image generated using the Library Massagingtoolkit-QRCode with alphanumeric text input degan various versions QR-Code in accordance with a lot of character input. The input image is processed into a halftone image with methods Ordered Dithering variation dither matrix 4 x 4 and 8 x 8. The image of the QR-Code and halftone images will be processed into QR-Code Halftone Pattern Assignment method. Output generated by the application Halftone QR-Code tested with QR-Code ZXing Library and QRAFTER application on Iphone 6s Plus and QR. The average rate of overall success with the test scenario ZXing QR-Code Library by 94%. The average rate of overall success with the test scenario QRAFTER application on Iphone 6s Plus from the laptop screen and hardcopy respectively by 100%.

Log-Euclidean Metrics for Contrast Preserving Decolorization.

This paper presents a novel Log-Euclidean metric inspired color-to-gray conversion model for faithfully preserving the contrast details of color image, which differs from the traditional Euclidean metric approaches. In the proposed model, motivated by the fact that Log-Euclidean metric has promising invariance properties such as inversion invariant and similarity invariant, we present a Log-Euclidean metric-based maximum function to model the decolorization procedure. The Gaussian-like penalty function consisting of the Log-Euclidean metric between gradients of the input color image and transformed grayscale image is incorporated to better reflect the degree of preserving feature discriminability and color ordering in color-to-gray conversion. A discrete searching algorithm is employed to solve the proposed model with linear parametric and non-negative constraints. Extensive evaluation experiments show that the proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.

Color removal method considering perceived lightness

A novel color removal method for digital color images is proposed. In this method, differences of colors in an input color image are reflected effectively, and the perceived lightness coming from the input color image is preserved as much as possible in order to realize the natural color-to-gray conversion. The effectiveness and the validity of the proposed method are verified by some experiments.

GREAT: a gradient-based color-sampling scheme for Retinex.

Modeling the local color spatial distribution is a crucial step for the algorithms of the Milano Retinex family. Here we present GREAT, a novel, noise-free Milano Retinex implementation based on an image-aware spatial color sampling. For each channel of a color input image, GREAT computes a 2D set of edges whose magnitude exceeds a pre-defined threshold. Then GREAT re-scales the channel intensity of each image pixel, called target, by the average of the intensities of the selected edges weighted by a function of their positions, gradient magnitudes, and intensities relative to the target. In this way, GREAT enhances the input image, adjusting its brightness, contrast and dynamic range. The use of the edges as pixels relevant to color filtering is justified by the importance that edges play in human color sensation. The name GREAT comes from the expression "Gradient RElevAnce for ReTinex," which refers to the threshold-based definition of a gradient relevance map for edge selection and thus for image color filtering.

Unsupervised hierarchical image segmentation through fuzzy entropy maximization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimal thresholds when performing multilevel gray image segmentation. Nevertheless, existing fuzzy c-partition entropy approaches generally have two limitations, i.e., partition number c needs to be manually tuned for different input and the methods can process grayscale images only. To address these two limitations, an unsupervised multilevel segmentation algorithm is presented in this paper. The core step of our algorithm is a bi-level segmentation operator, which uses binary graph cuts to maximize both fuzzy 2-partition entropy and segmentation smoothness. By iteratively performing this bi-level segmentation operator, multilevel image segmentation is achieved in a hierarchical manner: Starting from the input color image, our algorithm first picks the color channel that can best segment the image into two labels, and then iteratively selects channels to further split each labels until convergence. The experimental results demonstrate the presented hierarchical segmentation scheme can efficiently segment both grayscale and color images. Quantitative evaluations over classic gray images and the Berkeley Segmentation Database show that our method is comparable to the state-of-the-art multi-scale segmentation methods, yet has the advantage of being unsupervised, efficient, and easy to implement.

Image retrieval based on histograms of EOPs and VQ indices

A new feature for image retrieval based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their EOs. Each input colour image is decomposed into Y, Cb and Cr components, and each component image is divided into non-overlapping 4 × 4 blocks. For each block, eight EO templates of size 4 × 4 are performed to determine its EO. On the other hand, vector quantisation (VQ) is an efficient clustering and classification technique for high-dimensional spaces, and thus VQ is also used to extract the texture features. The retrieval simulation results show that the EOP-histogram-based features combined with the traditional VQ-index-histogram-based features can largely improve the recall and precision performance.

Flow-guided warping for image-based shape manipulation

We present an interactive method that manipulates perceived object shape from a single input color image thanks to a warping technique implemented on the GPU. The key idea is to give the illusion of shape sharpening or rounding by exaggerating orientation patterns in the image that are strongly correlated to surface curvature. We build on a growing literature in both human and computer vision showing the importance of orientation patterns in the communication of shape, which we complement with mathematical relationships and a statistical image analysis revealing that structure tensors are indeed strongly correlated to surface shape features. We then rely on these correlations to introduce a flow-guided image warping algorithm, which in effect exaggerates orientation patterns involved in shape perception. We evaluate our technique by 1) comparing it to ground truth shape deformations, and 2) performing two perceptual experiments to assess its effects. Our algorithm produces convincing shape manipulation results on synthetic images and photographs, for various materials and lighting environments.