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This paper presents a new method for extracting the image features and lineaments related to local extrema of an image or a digital elevation model (DEM) such as ridges and valleys based on the continuous wavelet transform (CWT) of a set of variously illuminated hillshades. The method originates from the principle that a hillshade can exactly reflect the lineaments nearly perpendicular to the illumination direction of the hillshade, but not other ones. The method consists of four steps: (1) preparation of a set of differently illuminated hillshades of the input data, (2) detection of directional edges nearly perpendicular to the illumination direction from each hillshade based on the CWT, (3) a combination of multidirectional edges into an omnidirectional feature image, and (4) identification of lineaments through linkage and linearization of image feature lines. CWT coefficients of each hillshade are used to calculate the gradient and its direction of the hillshade. For each hillshade, directional edge pixels where the gradient direction is parallel to the illumination direction are selectively detected to form accurate and solitary image feature lines related to local extrema of the input data. Directional edges of each hillshade are easily classified into positive and negative edges using the hillshade gradient. As they have similar directions, they are easily linked to form longer line raster objects, which are converted into vector objects, that is, directional lineaments. The multidirectional edges and lineaments given from all the hillshades are combined to form an omnidirectional feature image and a group of omnidirectional lineaments. Its application to real DEMs shows the demonstrated advantages of the proposed method over other methods and the similarity between detected lineaments and fault lines in the study area. [ABSTRACT FROM AUTHOR]

Featured Application: The future applications of the present study are to secure patient information and diagnostic details within medical images, ensuring data integrity and authenticity. The method enhances the security of medical records during storage, transmission, and especially in telemedicine, where images are frequently shared over digital networks. The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a "topological map" of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient's direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system's performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images. [ABSTRACT FROM AUTHOR]

The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

With the widespread application of deep learning, many artificially generated fake images and videos appear on the Internet. However, it is difficult for people to distinguish the real from the fake ones, making the research on detecting and recognizing fake images or videos receive significant attention. Since new forgery techniques can reduce the effectiveness of specific detection methods or even make them ineffective, research on detecting facial depth forgery needs to be continuously developed. To defend against the onslaught of new facial depth forgery methods, we proposed an image gradient-based approach to transform the facial depth forgery detection problem into the recognition and analysis of video frames. Specifically, there are two key components in this approach: (1) we capture images from videos and crop the face section, which dramatically reduces the amount of data; (2) we use the image gradient operator to process the face image that extracts image features for detection and recognition. After these, we have conducted extensive experiments on different facial depth forgery datasets. Experimental results demonstrated that using our image gradient approach could effectively detect facial depth forgery and achieve excellent detection and identification performance. [ABSTRACT FROM AUTHOR]

In order to improve the recognition rate and operational efficiency of the system, a method in which the image features compensation coefficients are optimized by using an improved simulated annealing algorithm is proposed. Firstly, eight computational factors with low computational complexity are given, which can be used to compensate image features. Secondly, the design flow of face recognition algorithm is presented. Thirdly, an improved simulated annealing algorithm is designed to solve the optimal combination of feature compensation coefficients in the face recognition system. Fourthly, the results of the feature compensation coefficients recommended by the improved simulated annealing algorithm are applied to the Efficient Face Recognition Algorithm (EFRA) in this paper, and verified on the simulation platform. Experiments show that the recognition rate can reach 100% when the training images are 6 in ORL. The proposed algorithm also performs well in MU_PIE dataset. [ABSTRACT FROM AUTHOR]

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions. • New filtering method for image velocimetry in rivers handles heterogeneous seeding. • The method enhances surface velocity estimations for more accurate flow measurement. • The methodology was validated in a wide range of environmental and seeding conditions. • This approach minimizes the need for subjective choices during data analysis. [ABSTRACT FROM AUTHOR]

Adaptive video monitoring settings have been extensively deployed in recent years. Smart video monitoring technology enables the acquisition and analysis of movies from various devices, as well as automatic analysis based on knowledge gathering. However, the storage capacity is restricted, and the important frames from the movie cannot be saved. Though, if the movie employs face as keyframes, it creates space and time complexity. To address this issue, the Viola-Jones Algorithm was used to detect faces from extracted keyframes in Video Indexing through Human Face Images using LGFA and the sliding window technique. The image gradient for brightness is created by integrating the sliding windowing method with LGFA, and scanning the input image horizontally takes up 70% of the facial image. As a result, Barcode as an index using the sequence table of the EAN 8 approach converts a video's human face into an EAN-8 linear video indexing barcode and thereby reducing bandwidth, storage space, and time complexity. Regular TV series video datasets, datasets of YouTube faces, and data sets of Hollywood clips were used to evaluate the proposed technique, and shown to be effective for indexing videos based on human faces. [ABSTRACT FROM AUTHOR]

In the field of reversible data hiding in encrypted images (RDH-EI), predict an image effectively and embed a message into the image with lower distortion are two crucial aspects. However, due to the linear regression prediction being sensitive to outliers, it is a challenge to improve the accuracy of predictions. To address this problem, this paper proposes an RDH-EI scheme based on adaptive prediction-error label map. In the prediction stage, an adaptive threshold estimation algorithm based on local complexity is proposed. Then, the pixels selection method based on gradient of image is designed to train the parameters of the prediction model. In the embedding stage, reserve enough space to embed auxiliary information and secret data embedding by flipping the least significant bits (LSBs) to encrypt the original image. In the receiver, based on the prediction- error map, error-free image recovery can be achieved. Extensive experimental results have shown that the proposed method can achieve effective pixel prediction results and get a higher embedding rate (ER). [ABSTRACT FROM AUTHOR]

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity. [ABSTRACT FROM AUTHOR]

To improve the efficiency of traditional face recognition techniques, this paper proposes a novel face recognition algorithm called Image Gradient Feature Compensation (IGFC). Based on the gradients along four directions in an image, a fusion algorithm and a compensation method are implemented to obtain features of the original image. In this study, gradient magnitude maps of a face image are calculated along four directions. Fusion gradients and differential fusion gradients are produced by fusing the four gradient magnitude maps of a face image in multiple ways, and they are used as compensation variables to compensate the appropriate coefficients on the original image and build IGFC feature maps of the original face image. Subsequently, IGFC feature maps are divided into several blocks to calculate the concatenated histogram over all blocks, which is in turn utilized as the feature descriptor for face recognition. Principal component analysis (PCA) is used to cut down the number of dimensions in high-dimensional features, which are recognized by the Support Vector Machine (SVM) classifier. Finally, the proposed IGFC method is superior to traditional methods as suggested by verification studies on YALE, ORL, CMU_PIE, and FERET face databases. When the LibSVM parameter was set to '-s 0 -t 2 -c 16 -g 0.0009765625', the algorithm achieved 100% recognition on Yale and ORL data sets, 92.16% on CMU_PIE data sets, and 74.3% on FERET data sets. The average time for simultaneous completion of the data sets examined was 1.93 s, and the algorithm demonstrated a 70.71% higher running efficiency than the CLBP algorithm. Therefore, the proposed algorithm is highly efficient in feature recognition with excellent computational efficiency. [ABSTRACT FROM AUTHOR]

• Our method detects and removes specular highlight effectively. • Gaussian mixture model clustering is more accurate than k-means clustering. • Confidence strategy makes the calculated unsaturated pixels more realistic. • New exemplar patch matching criterion finds the best exemplar patch quickly. In this paper, a new method based on dichromatic reflection model (DRM) and exemplar patch is proposed to remove highlight pixels for light field images. Firstly, a Gaussian mixture model clustering method with strong generalization performance combined with depth information is used to classify saturated highlight pixels and unsaturated highlight pixels. A confidence strategy based on DRM is proposed to remove unsaturated highlight pixels, and an exemplar patch matching method based on gradient combined with the sum of square of color difference is designed to remove saturated highlight pixels. Meanwhile, a method named SSIME based on information entropy with structural similarity index measure is designed to quantitatively evaluate the effectiveness of proposed method. Experiments show that our proposed method not only effectively detects and removes specular highlights, but also applies to objects with a large range of highlights, which breakthroughs the limitation that large-region specular highlight cannot be removed due to the short baseline of the light field camera. [ABSTRACT FROM AUTHOR]

Aiming at the contradiction of color distortion and image detail enhancement when Retinex algorithm is applied to underwater image enhancement, the adaptive multi-scale Retinex underwater image enhancement algorithm combined with detail information is proposed in this paper. The selection requirements of the scale of convolution function in the enhancement of Retinex algorithm for underwater images containing different detail information are analyzed. Image gradient is used as the adjustment factor to adaptively adjust the weight of multi-scale Retinex operator, which is used to meet the requirements of underwater image with different detail information for contrast enhancement, and effectively alleviate the contradiction between color distortion and detail contrast enhancement in underwater image enhancement. Several groups of experiments show that the algorithm is superior to the traditional multi-scale (MSR) and multi-scale Retinex with color restoration (MSRCR) in removing the blue-green background of underwater image, avoiding color distortion, eliminating non-uniform illumination and image detail enhancement. [ABSTRACT FROM AUTHOR]

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal. [ABSTRACT FROM AUTHOR]

Traditional 3D block matching (BM3D) algorithms are among the best denoising methods at present; however, they exhibit the issue of ringing around image edges, which makes them unable to protect image edges and details. Therefore, this paper proposes an BM3D noise processing algorithm for the diffusion equation to reduce image noise without affecting image details, specifically at the edges. This method first uses anisotropic diffusion (AD) filtering for image preprocessing, and then uses the edge direction instead of horizontal direction to search for similar blocks. The AD model is mainly improved to achieve better edges and detailed processing effects. Firstly, with the improved AD direction, a 5 × 5 edge enhancement operator model is implemented in eight directions, and the corresponding gradient information is obtained. This operator improves the processed image edges to achieve clear contours and good continuity. Next, a new calculation method for the diffusion function, whose coefficient is constructed using a hyperbolic tangent function, is introduced. The proposed method is based on the link between the image gradient and diffusion function, and it is mathematically proven that the diffusion function converges faster than the diffusion function of the model proposed by Perona and Malik. Experimental results indicate that the improved model can effectively retain the image edges and texture details, avoid edge ringing, and provide significant improvements in terms of the subjective visual effects and objective numerical indicators.

Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications.

Patch-based low rank matrix approximation has shown great potential in image denoising. Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising performance. For each local patch in an image, the WNNM method groups nonlocal similar patches by block matching to formulate a low-rank matrix. However, the WNNM often chooses irrelevant patches such that it may lose fine details of the image, resulting in undesirable artifacts in the final reconstruction. In this regards, this paper aims to provide a denoising algorithm which further improves the performance of the WNNM method. For this purpose, we develop a new nonlocal similarity measure by exploiting both pixel intensities and gradients and present a filter that enhances edge information in a patch to improve the performance of low rank approximation. The experimental results on widely used test images demonstrate that the proposed denoising algorithm performs better than other state-of-the-art denoising algorithms in terms of PSNR and SSIM indices as well as visual quality.

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.

Accurate estimation of eye-related information is important for many applications such as gaze estimation, face alignment, driver drowsiness detection, etc. Earlier works fail to estimate eye information in low-resolution images captured by a regular camera or webcam. This paper is aimed at developing an Iris Center (IC) and Eye Corner (EC) localization method in low-resolution facial images with an application of gaze estimation. A three-stage method is proposed for IC and EC localization. In the first stage, a circular gradient-intensity-based operator is proposed for rough ICs estimation and a CNN model is used in the second stage to find true ICs. In the third stage, Explicit Shape Regression (ESR) method is used for EC localization where initialization is done taking the ICs as a reference point to the mean eye contour shape model. The proposed IC localization method is evaluated on BioID and Gi4E database and it shows better accuracy compare to some of the state-of-the-art methods. This method further evaluated for gaze estimation based on IC and EC which does not require any prior calibrations unlike earlier infrared illumination-based gaze trackers. Here, the experiment for gaze estimation is performed in our proposed NITSGoP database that prepared under indoor conditions with complex background and uneven illuminations. The experimental results suggest that the proposed method can be used for gaze estimation with better accuracy both in still images and videos. [ABSTRACT FROM AUTHOR]

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.

Eye center localization is considered a crucial step for many human–computer interaction (HCI) real-time applications. Detecting the center of eye (COE), accurately and in real time, is very challenging due to the wide variation of poses, eye appearance and specular reflection, especially in low-resolution images. In this paper, an accurate real-time detection algorithm of the COE is proposed. The proposed approach depends on the image gradient to detect the COE. The computational complexity is minimized and the accuracy is improved by down sampling the face resolution and applying a rough-to-fine algorithms, to reduce the search area, in accordance with the Eye Region Of Interest (EROI) and the number of COE candidates, tested by the proposed algorithm. Also, the detection algorithm is applied on a limited number of pixels that represent the iris boundary of the COE candidates. The Look Up Tables (LUTs) are implemented to, initially, store the invariant elements of the proposed image gradient-based algorithm, to reduce the detection time. Before applying the proposed COE detection approach, a modified specular reflection method is used to improve the detection accuracy. The performance of the proposed algorithm has been evaluated by applying it to three benchmark databases: the BIOID, GI4E and Talking Face video datasets, at different face resolutions. Experimental results revealed that the accuracy of the proposed algorithm is up to 91.68% and 96.7% for BIOID and GI4E datasets, respectively, while the minimum achieved average detection time is 2.7 ms. The promising results highlight the potential of the proposed algorithm to be used in some eye gaze-based real-time applications. Comparing the proposed method with the most state-of-the-art approaches showed that the system outperforms most of them and has a comparable performance with the others, in terms of the COE localization accuracy and detection speed. [ABSTRACT FROM AUTHOR]

• Very first fuzzy based noise model in image processing. • Straight forward principle compared to indirect noise models based on image histogram. • Useful for quality control in MR imaging. • Useful for parameter optimization of filters used for image denoising. Noise is a critical factor which destroys the visual clarity of Magnetic Resonance (MR) images. In many of the denoising schemes used on MR images, the depth of denoising is decided based on the strength of noise and their operational parameters are tuned in proportion to the Standard Deviation (SD) of noise. Most of the state-of-the-art noise models estimate noise statistics indirectly from the standard probability density function of choice, fitted on the image histogram. A mathematical model to estimate the noise statistics in Magnetic Resonance (MR) images, from the image fuzziness is proposed in this work. Noise significantly affects the randomness of gray levels, gradient and fuzziness of the image. Based on this principle, a direct method for computing the noise variance is proposed in this paper. Noise variance of the image is directly estimated from the fuzziness of the noisy image by using the polynomial model. The fuzzy membership values at each pixel are set in proportion to the normalized local gradient. On phantom studies, quadratic index of fuzziness is observed to be well-correlated with standard deviation of noise with a correlation of 0.9532 ± 0.3315. The proposed polynomial model exhibited a goodness of fit, r = 0.9996. The model is found to be superior to the existing models with regard to Root Mean Squared Error (RMSE). On simulation studies on MR phantom, noise variance is found to be well-correlated with image fuzziness. The proposed model exhibited high goodness of fit. The model is found to be superior to the noise models available in literature in terms of Root Mean Square Error (RMSE). The principle of the proposed fuzziness based noise model is straightforward compared to indirect noise models which make use of image histogram. [ABSTRACT FROM AUTHOR]

Melanoma tumor can cause a serious life threatening problem in humans, if left untreated for a long time without early diagnosis. For early diagnosis of melanoma, it is more significant to develop novel methods based on biophysics analyses, molecular targets recognitions, and new image analysis criteria. In this article, anatomical region segmentation and diameter identification is proposed to detect melanoma from dermoscopic images. Four main steps of the proposed system are as follows: In the first step, the preprocessing is performed to smooth the melanoma extraction process. The region segmentation is done in the second step using watershed segmentation and Sobel operator. In the third step, the postprocessing procedures like as morphological open, canny edge detection also applied to improve the region of interest. Finally, the melanoma region is identified using color symmetry features. The proposed method is tested with two data sets to prove the performance proposed method. The proposed method achieved an accuracy of 95.31% and specificity of 98.3%, which is better than other methods. Experimental results show that the effectiveness of the proposed method and illustrate viability of real‐time clinical applications. [ABSTRACT FROM AUTHOR]

The method of infrared small target detection is a crucial technology for infrared early-warning tasks, infrared imaging guidance, and large field of view target monitoring, and it is very important for certain early-warning tasks. In this paper, we propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as "noise" in infrared images and transforms small target detection tasks into denoising problems. In addition, we use the perceptual loss to solve the problem of background texture feature loss in the encoding process, and propose the structural loss to make up for the perceptual loss defect in which small targets appear. We compare ten methods on six sequences and one single-frame dataset. Experimental results show that our method obtains the highest SCRG value on four sequences and the highest BSF value on six sequences. From the ROC curve, we can see that our method achieves the best results in all test sets. [ABSTRACT FROM AUTHOR]

The goal of blind image forensics is to determine authenticity and origin of an image without using an explicitly embedded security scheme. Most existing forensic methods can roughly be grouped into statistical and physics-based approaches. Statistical methods can oftentimes be fully automated, and achieve impressive results on current state-of-the-art benchmarks. Physics-based methods explain image inconsistencies using an analytic model, and are more robust to common image processing operations such as resizing or recompression. In this work, we propose a physics-based forensic descriptor to characterize 2-D lighting environments of objects. The key idea is that the integral over a gradient field of an object indicates the direction of incident light in the image plane. In contrast to prior 2-D lighting methods, the proposed method is remarkably robust to changes in object color and variations in user input, as it operates on the whole object area instead of object contours. Furthermore, we show that the proposed method is unaffected by image resizing or compression, which makes it possible to analyze images that are impossible to analyze with current state-of-the-art statistical methods. [ABSTRACT FROM AUTHOR]

In current adaptive steganography study, the distortion function is used to describe pixel modification distortion cost. Distortion cost can describe the influence of pixel modification to the cover. And it plays an important actor in adaptive stegaography. Based on previous researches, in this paper, an Anisotropic Distortion cost Update strategy (ADU strategy) is proposed to adaptively update the initial distortion cost (obtained by HILL.etc) to describe the interaction between multiple embedding modifications more accurately. Based on steanographic security theorty, it is proved that clustering modification strategy can improve steganographic security through theoretical derivation. And through study on steganalysis features, it is analyzed and proved that the determination of optimal modification method of the central pixel is anisotropic and much more complicated. Following these two proofs, the image gradient and thermal conductivity are used to quantify the anisotropy of 4 neighboring pixels. Then the optimal modification method of the central pixel is calculated. Finally, the distortion cost of the central pixel can be updated based on the optimal modification method. Experiments with the optimal embedding simulator show that the ADU strategy has better performance when the scaling factor is 2; the proposed ADU strategy can effectively improve the steganography schemes, especially for steganalytic performance with maxSRMd2 features. [ABSTRACT FROM AUTHOR]

This paper suggests an image gradient based method that determines the optimal image size for convolutional neural network (CNN)-based diagnosis of fluid-film rotorbearing systems. As distinct patterns improve the diagnosis performance, a criterion is defined to measure the intensity of patterns in an image. The proposed criterion is derived by segmenting an image by the size of the CNN filter and evaluating each segment through the use of image gradient analysis. Vibration signals from a testbed are used to demonstrate the proposed method. First, the signals are transformed into vibration images by using an omnidirectional regeneration technique. Then, vibration images of four different health states are analyzed using the suggested criterion. The analyzed results are compared to the performance of CNN based diagnosis. The results indicate that the proposed criterion can determine the optimal size range of the vibration image that gives the best performance for CNN-based diagnosis. [ABSTRACT FROM AUTHOR]

Evolutionary facial composites are created using interactive genetic algorithms based on user selections. This approach is grounded in perceptive studies, and is superior to feature-based systems. A method is presented for creating facial composites in which faces are encoded with shape information, the coordinates of a predefined landmark points, and the image gradient, which represents face information more precisely than image luminance. The new method is accompanied by a Poisson integration process that presents the user with candidate faces. Two user tests, one using composite creators and the other external evaluators, show that the new method produces higher rated composites that are better recognised. [ABSTRACT FROM AUTHOR]

In this paper, an eye tracking methodology is presented. It represents a commercial camera based system for detection of eye pupil and its trajectory and classification of eye movements that led to the formation of assessed eye path. Methodology was examined on 10 subjects, who performed specified series of eye movements in two recording conditions: with and without simultaneous head motions. Image segmentation was performed and coordinates of eye pupil were found for each consecutive video frame. Modified velocity threshold method for eye movement classification was performed. Classification accuracy was evaluated with two parameters: Sensitivity (S) and Positive predictive value (PP), and compared for both recording conditions. In the absence of head motions, a high accuracy result was obtained (S=96.25%; PP=100%). A less accurate result was obtained in the second case (S=87.50%; PP=88.61%). However, it was shown that proposed methodology can provide good results for commercial eye tracking regardless of the recording conditions.

Understanding the ancient script can provide rich details of a civilization, like its cultural, political and social scenarios. Brahmi, an ancient mother script, has been a key to the development of many modern Indian scripts like Gurmukhi, Devanagari, Bangla etc. Inferring ancient writings can be a tedious job and moreover, if executed manually, it requires several language experts. The current paper presents a recognition system for Brahmi characters using linear Support Vector machine classifier. Gradient information of the character images pixels is extracted, and histogram of the gradients is stored as a feature vector for each character image. Character dataset includes both handwritten character images and images from the internet. Linear SVM classifier is trained on the feature set of 24 images of each character. The proposed recognition system is performed with an accuracy of 91.6% to recognize the Brahmi characters from the test images. [ABSTRACT FROM AUTHOR]