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J. Imaging, Volume 3, Issue 3 (September 2017)

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Open AccessArticle RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement
J. Imaging 2017, 3(3), 24; doi:10.3390/jimaging3030024
Received: 1 June 2017 / Revised: 27 June 2017 / Accepted: 27 June 2017 / Published: 1 July 2017
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Abstract
A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute
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A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute of grayscale values, the naive application of the methods for grayscale images to color images often results in unsatisfactory consequences. Conventional hue-preserving color image enhancement methods utilize histogram equalization (HE) for enhancing the contrast. However, they cannot always enhance the saturation simultaneously. In this paper, we propose a histogram specification (HS) method for enhancing the saturation in hue-preserving color image enhancement. The proposed method computes the target histogram for HS on the basis of the geometry of RGB (rad, green and blue) color space, whose shape is a cube with a unit side length. Therefore, the proposed method includes no parameters to be set by users. Experimental results show that the proposed method achieves higher color saturation than recent parameter-free methods for hue-preserving color image enhancement. As a result, the proposed method can be used for an alternative method of HE in hue-preserving color image enhancement. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessArticle Automatic Recognition of Speed Limits on Speed-Limit Signs by Using Machine Learning
J. Imaging 2017, 3(3), 25; doi:10.3390/jimaging3030025
Received: 30 May 2017 / Revised: 30 June 2017 / Accepted: 1 July 2017 / Published: 5 July 2017
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Abstract
This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local
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This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using Hue, Saturation and Value (HSV) color spaces for extracting the speed limit numbers on the identified speed-limit signs, and finally (3) a method for recognition of the extracted numbers using a neural network. The method of traffic sign recognition previously proposed by the author consisted of extracting geometric shapes from the sign and recognizing them based on their aspect ratios. This method cannot be used for the numbers on speed-limit signs because the numbers all have the same aspect ratios. In a study that proposed recognition of speed limit numbers using an Eigen space method, a method using only color information was used to detect speed-limit signs from images of scenery. Because this method used only color information for detection, precise color information settings and processing to exclude everything other than the signs are necessary in an environment where many colors similar to the speed-limit signs exist, and further study of the method for sign detection is needed. This study focuses on considering the following three points. (1) Make it possible to detect only the speed-limit sign in an image of scenery using a single process focusing on the local patterns of speed limit signs. (2) Make it possible to separate and extract the two-digit numbers on a speed-limit sign in cases when the two-digit numbers are incorrectly extracted as a single area due to the light environment. (3) Make it possible to identify the numbers using a neural network by focusing on three feature quantities. This study also used the proposed method with still images in order to validate it. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessArticle A PDE-Free Variational Method for Multi-Phase Image Segmentation Based on Multiscale Sparse Representations
J. Imaging 2017, 3(3), 26; doi:10.3390/jimaging3030026
Received: 30 December 2016 / Revised: 8 May 2017 / Accepted: 19 June 2017 / Published: 13 July 2017
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Abstract
We introduce a variational model for multi-phase image segmentation that uses a multiscale sparse representation frame (wavelets or other) in a modified diffuse interface context. The segmentation model we present differs from other state-of-the-art models in several ways. The diffusive nature of the
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We introduce a variational model for multi-phase image segmentation that uses a multiscale sparse representation frame (wavelets or other) in a modified diffuse interface context. The segmentation model we present differs from other state-of-the-art models in several ways. The diffusive nature of the method originates from the sparse representations and thus propagates information in a different manner comparing to any existing PDE models, allowing one to combine the advantages of non-local information processing with sharp edges in the output. The regularizing part of the model is based on the wavelet Ginzburg–Landau (WGL) functional, and the fidelity part consists of two terms: one ensures the mean square proximity of the output to the original image; the other takes care of preserving the main edge set. Multiple numerical experiments show that the model is robust to noise yet can preserve the edge information. This method outperforms the algorithms from other classes in cases of images with significant presence of noise or highly uneven illumination Full article
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Open AccessArticle Terahertz Application for Non-Destructive Inspection of Coated Al Electrical Conductive Wires
J. Imaging 2017, 3(3), 27; doi:10.3390/jimaging3030027
Received: 30 April 2017 / Revised: 10 July 2017 / Accepted: 11 July 2017 / Published: 14 July 2017
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Abstract
At present, one of the main inspection methods of electric wires is visual inspection. The development of a novel non-destructive inspection technology is required because of various problems, such as water invasion by the removal of insulators. Since terahertz (THz) waves have high
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At present, one of the main inspection methods of electric wires is visual inspection. The development of a novel non-destructive inspection technology is required because of various problems, such as water invasion by the removal of insulators. Since terahertz (THz) waves have high transparency to nonpolar substances such as coatings of conductive wire, electric conductive wires are extremely suitable for THz non-destructive inspection. In this research, in order to investigate the quantitative possibility of detecting the defects on aluminum electric wire, THz wave reflection imaging measurement was performed for artificially disconnected wires. It is shown that quantitative detection is possible for the disconnect status of the aluminum electric wire by using THz waves. Full article
(This article belongs to the Special Issue THz and MMW Imaging)
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Open AccessArticle Assessment of Geometric Distortion in Six Clinical Scanners Using a 3D-Printed Grid Phantom
J. Imaging 2017, 3(3), 28; doi:10.3390/jimaging3030028
Received: 26 May 2017 / Revised: 27 June 2017 / Accepted: 15 July 2017 / Published: 18 July 2017
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Abstract
A cost-effective regularly structured three-dimensional (3D) printed grid phantom was developed to enable the quantification of machine-related magnetic resonance (MR) distortion. This phantom contains reference features, “point-like” objects, or vertices, which resulted from the intersection of mesh edges in 3D space. 3D distortions
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A cost-effective regularly structured three-dimensional (3D) printed grid phantom was developed to enable the quantification of machine-related magnetic resonance (MR) distortion. This phantom contains reference features, “point-like” objects, or vertices, which resulted from the intersection of mesh edges in 3D space. 3D distortions maps were computed by comparing the locations of corresponding features in both MR and computer tomography (CT) data sets using normalized cross correlation. Results are reported for six MRI scanners at both 1.5 T and 3.0 T field strengths within our institution. Mean Euclidean distance error for all MR volumes in this study, was less than 2 mm. The maximum detected error for the six scanners ranged from 2.4 mm to 6.9 mm. The conclusions in this study agree well with previous studies that indicated that MRI is quite accurate near the centre of the field but is more spatially inaccurate toward the edges of the magnetic field. Full article
(This article belongs to the Special Issue Magnetic Resonance Imaging)
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Open AccessArticle Pattern Reconstructability in Fully Parallel Thinning
J. Imaging 2017, 3(3), 29; doi:10.3390/jimaging3030029
Received: 16 June 2017 / Revised: 13 July 2017 / Accepted: 15 July 2017 / Published: 19 July 2017
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Abstract
It is a challenging topic to perform pattern reconstruction from a unit-width skeleton, which is obtained by a parallel thinning algorithm. The bias skeleton yielded by a fully-parallel thinning algorithm, which usually results from the so-called hidden deletable points, will result in the
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It is a challenging topic to perform pattern reconstruction from a unit-width skeleton, which is obtained by a parallel thinning algorithm. The bias skeleton yielded by a fully-parallel thinning algorithm, which usually results from the so-called hidden deletable points, will result in the difficulty of pattern reconstruction. In order to make a fully-parallel thinning algorithm pattern reconstructable, a newly-defined reconstructable skeletal pixel (RSP) including a thinning flag, iteration count, as well as reconstructable structure is proposed and applied for thinning iteration to obtain a skeleton table representing the resultant thin line. Based on the iteration count and reconstructable structure associated with each skeletal pixel in the skeleton table, the pattern can be reconstructed by means of the dilating and uniting operations. Embedding a conventional fully-parallel thinning algorithm into the proposed approach, the pattern may be over-reconstructed due to the influence of a biased skeleton. A simple process of removing hidden deletable points (RHDP) in the thinning iteration is thus presented to reduce the effect of the biased skeleton. Three well-known fully-parallel thinning algorithms are used for experiments. The performances investigated by the measurement of reconstructability (MR), the number of iterations (NI), as well as the measurement of skeleton deviation (MSD) confirm the feasibility of the proposed pattern reconstruction approach with the assistance of the RHDP process. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition)
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Open AccessArticle Using SEBAL to Investigate How Variations in Climate Impact on Crop Evapotranspiration
J. Imaging 2017, 3(3), 30; doi:10.3390/jimaging3030030
Received: 27 April 2017 / Revised: 3 July 2017 / Accepted: 5 July 2017 / Published: 20 July 2017
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Abstract
Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural processes. Deficit or excessive irrigation could create either crop health-related problems or water over-consumption, respectively. The latter could lead to groundwater depletion and
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Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural processes. Deficit or excessive irrigation could create either crop health-related problems or water over-consumption, respectively. The latter could lead to groundwater depletion and deterioration of its quality through deep percolation of agrichemical residuals. In this context, and under the current conditions where Cyprus is facing effects of possible climate changes, the purpose of this study seeks to estimate the needed crop water requirements of the past (1995–2004) and the corresponding ones of the present (2005–2015) in order to test if there were any significant changes regarding the crop water requirements of the most water-intensive trees in Cyprus. The Mediterranean region has been identified as the region that will suffer the most from variations of climate. Thus the paper refers to effects of these variations on crop evapotranspiration (ETc) using remotely-sensed data from Landsat TM/ETM+/OLI employing a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL). Though the general feeling is that of changes on climate will consequently affect ETc, our results indicate that there is no significant effect of climate variation on crop evapotranspiration, despite the fact that some climatic factors have changed. Applying Student’s t-test, the mean values for the most water-intensive trees in Cyprus of the 1994–2004 decade have shown no statistical difference from the mean values of 2005–2015 for all the cases, concluding that the climate change taking place in the past decades in Cyprus have either not affected the crop evapotranspiration or the crops have managed to adapt to the new environmental conditions through time. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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Open AccessArticle Robust Parameter Design of Derivative Optimization Methods for Image Acquisition Using a Color Mixer
J. Imaging 2017, 3(3), 31; doi:10.3390/jimaging3030031
Received: 27 May 2017 / Revised: 3 July 2017 / Accepted: 15 July 2017 / Published: 21 July 2017
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Abstract
A tuning method was proposed for automatic lighting (auto-lighting) algorithms derived from the steepest descent and conjugate gradient methods. The auto-lighting algorithms maximize the image quality of industrial machine vision by adjusting multiple-color light emitting diodes (LEDs)—usually called color mixers. Searching for the
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A tuning method was proposed for automatic lighting (auto-lighting) algorithms derived from the steepest descent and conjugate gradient methods. The auto-lighting algorithms maximize the image quality of industrial machine vision by adjusting multiple-color light emitting diodes (LEDs)—usually called color mixers. Searching for the driving condition for achieving maximum sharpness influences image quality. In most inspection systems, a single-color light source is used, and an equal step search (ESS) is employed to determine the maximum image quality. However, in the case of multiple color LEDs, the number of iterations becomes large, which is time-consuming. Hence, the steepest descent (STD) and conjugate gradient methods (CJG) were applied to reduce the searching time for achieving maximum image quality. The relationship between lighting and image quality is multi-dimensional, non-linear, and difficult to describe using mathematical equations. Hence, the Taguchi method is actually the only method that can determine the parameters of auto-lighting algorithms. The algorithm parameters were determined using orthogonal arrays, and the candidate parameters were selected by increasing the sharpness and decreasing the iterations of the algorithm, which were dependent on the searching time. The contribution of parameters was investigated using ANOVA. After conducting retests using the selected parameters, the image quality was almost the same as that in the best-case parameters with a smaller number of iterations. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessArticle Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration
J. Imaging 2017, 3(3), 32; doi:10.3390/jimaging3030032
Received: 8 May 2017 / Revised: 19 July 2017 / Accepted: 20 July 2017 / Published: 22 July 2017
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Abstract
The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s)
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The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s) to retrieve the remaining two translation parameters. Such a mechanism may accumulate the error of the six rotation and scale parameters toward the two translation parameters. In this study, we propose the incorporation of a direct method to estimate all eight transformation parameters of LBTM simultaneously using least-squares adjustment. The improved LBTM method was compared with the original LBTM through using one synthetic dataset and three experimental datasets for satellite image 2D registration and 3D rectification. The experimental results demonstrated that the improved LBTM converges to a steady solution with two to three ground control points (GCPs) and five ground control lines (GCLs), whereas the original LBTM requires at least 10 GCLs to yield a stable solution. Full article
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Open AccessArticle Improving CNN-Based Texture Classification by Color Balancing
J. Imaging 2017, 3(3), 33; doi:10.3390/jimaging3030033
Received: 29 June 2017 / Revised: 17 July 2017 / Accepted: 21 July 2017 / Published: 27 July 2017
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Abstract
Texture classification has a long history in computer vision. In the last decade, the strong affirmation of deep learning techniques in general, and of convolutional neural networks (CNN) in particular, has allowed for a drastic improvement in the accuracy of texture recognition systems.
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Texture classification has a long history in computer vision. In the last decade, the strong affirmation of deep learning techniques in general, and of convolutional neural networks (CNN) in particular, has allowed for a drastic improvement in the accuracy of texture recognition systems. However, their performance may be dampened by the fact that texture images are often characterized by color distributions that are unusual with respect to those seen by the networks during their training. In this paper we will show how suitable color balancing models allow for a significant improvement in the accuracy in recognizing textures for many CNN architectures. The feasibility of our approach is demonstrated by the experimental results obtained on the RawFooT dataset, which includes texture images acquired under several different lighting conditions. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessFeature PaperArticle Image Fragile Watermarking through Quaternion Linear Transform in Secret Space
J. Imaging 2017, 3(3), 34; doi:10.3390/jimaging3030034
Received: 14 June 2017 / Revised: 4 August 2017 / Accepted: 5 August 2017 / Published: 11 August 2017
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Abstract
In this paper, we apply the quaternion framework for color images to a fragile watermarking algorithm with the objective of multimedia integrity protection (Quaternion Karhunen-Loève Transform Fragile Watermarking (QKLT-FW)). The use of quaternions to represent pixels allows to consider the color information in
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In this paper, we apply the quaternion framework for color images to a fragile watermarking algorithm with the objective of multimedia integrity protection (Quaternion Karhunen-Loève Transform Fragile Watermarking (QKLT-FW)). The use of quaternions to represent pixels allows to consider the color information in a holistic and integrated fashion. We stress out that, by taking advantage of the host image quaternion representation, we extract complex features that are able to improve the embedding and verification of fragile watermarks. The algorithm, based on the Quaternion Karhunen-Loève Transform (QKLT), embeds a binary watermark into some QKLT coefficients representing a host image in a secret frequency space: the QKLT basis images are computed from a secret color image used as a symmetric key. A computational intelligence technique (i.e., genetic algorithm) is employed to modify the host image pixels in such a way that the watermark is contained in the protected image. The sensitivity to image modifications is then tested, showing very good performance. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessArticle Color Consistency and Local Contrast Enhancement for a Mobile Image-Based Change Detection System
J. Imaging 2017, 3(3), 35; doi:10.3390/jimaging3030035
Received: 30 June 2017 / Revised: 31 July 2017 / Accepted: 8 August 2017 / Published: 23 August 2017
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Abstract
Mobile change detection systems allow for acquiring image sequences on a route of interest at different time points and display changes on a monitor. For the display of color images, a processing approach is required to enhance details, to reduce lightness/color inconsistencies along
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Mobile change detection systems allow for acquiring image sequences on a route of interest at different time points and display changes on a monitor. For the display of color images, a processing approach is required to enhance details, to reduce lightness/color inconsistencies along each image sequence as well as between corresponding image sequences due to the different illumination conditions, and to determine colors with natural appearance. We have developed a real-time local/global color processing approach for local contrast enhancement and lightness/color consistency, which processes images of the different sequences independently. Our approach combines the center/surround Retinex model and the Gray World hypothesis using a nonlinear color processing function. We propose an extended gain/offset scheme for Retinex to reduce the halo effect on shadow boundaries, and we employ stacked integral images (SII) for efficient Gaussian convolution. By applying the gain/offset function before the color processing function, we avoid color inversion issues, compared to the original scheme. Our combined Retinex/Gray World approach has been successfully applied to pairs of image sequences acquired on outdoor routes for change detection, and an experimental comparison with previous Retinex-based approaches has been carried out. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessFeature PaperArticle Improved Color Mapping Methods for Multiband Nighttime Image Fusion
J. Imaging 2017, 3(3), 36; doi:10.3390/jimaging3030036
Received: 30 June 2017 / Revised: 18 August 2017 / Accepted: 24 August 2017 / Published: 28 August 2017
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Abstract
Previously, we presented two color mapping methods for the application of daytime colors to fused nighttime (e.g., intensified and longwave infrared or thermal (LWIR)) imagery. These mappings not only impart a natural daylight color appearance to multiband nighttime images but also enhance their
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Previously, we presented two color mapping methods for the application of daytime colors to fused nighttime (e.g., intensified and longwave infrared or thermal (LWIR)) imagery. These mappings not only impart a natural daylight color appearance to multiband nighttime images but also enhance their contrast and the visibility of otherwise obscured details. As a result, it has been shown that these colorizing methods lead to an increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness. A crucial step in the proposed coloring process is the choice of a suitable color mapping scheme. When both daytime color images and multiband sensor images of the same scene are available, the color mapping can be derived from matching image samples (i.e., by relating color values to sensor output signal intensities in a sample-based approach). When no exact matching reference images are available, the color transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image. In the current study, we investigated new color fusion schemes that combine the advantages of both methods (i.e., the efficiency and color constancy of the sample-based method with the ability of the statistical method to use the image of a different but somewhat similar scene as a reference image), using the correspondence between multiband sensor values and daytime colors (sample-based method) in a smooth transformation (statistical method). We designed and evaluated three new fusion schemes that focus on (i) a closer match with the daytime luminances; (ii) an improved saliency of hot targets; and (iii) an improved discriminability of materials. We performed both qualitative and quantitative analyses to assess the weak and strong points of all methods. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessArticle Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors
J. Imaging 2017, 3(3), 37; doi:10.3390/jimaging3030037
Received: 21 March 2017 / Revised: 28 August 2017 / Accepted: 29 August 2017 / Published: 5 September 2017
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Abstract
The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety
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The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. Faces can have many variations in appearance (aging, facial expression, illumination, inaccurate alignment and pose) which continue to cause poor ability to recognize identity. The purpose of our research work is to provide an approach that contributes to resolve face identification issues with large variations of parameters such as pose, illumination, and expression. For provable outcomes, we combined two algorithms: (a) robustness local binary pattern (LBP), used for facial feature extractions; (b) k-nearest neighbor (K-NN) for image classifications. Our experiment has been conducted on the CMU PIE (Carnegie Mellon University Pose, Illumination, and Expression) face database and the LFW (Labeled Faces in the Wild) dataset. The proposed identification system shows higher performance, and also provides successful face similarity measures focus on feature extractions. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition)
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Open AccessArticle Histogram-Based Color Transfer for Image Stitching
J. Imaging 2017, 3(3), 38; doi:10.3390/jimaging3030038
Received: 5 July 2017 / Revised: 5 September 2017 / Accepted: 6 September 2017 / Published: 9 September 2017
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Abstract
Color inconsistency often exists between the images to be stitched and will reduce the visual quality of the stitching results. Color transfer plays an important role in image stitching. This kind of technique can produce corrected images which are color consistent. This paper
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Color inconsistency often exists between the images to be stitched and will reduce the visual quality of the stitching results. Color transfer plays an important role in image stitching. This kind of technique can produce corrected images which are color consistent. This paper presents a color transfer approach via histogram specification and global mapping. The proposed algorithm can make images share the same color style and obtain color consistency. There are four main steps in this algorithm. Firstly, overlapping regions between a reference image and a test image are obtained. Secondly, an exact histogram specification is conducted for the overlapping region in the test image using the histogram of the overlapping region in the reference image. Thirdly, a global mapping function is obtained by minimizing color differences with an iterative method. Lastly, the global mapping function is applied to the whole test image for producing a color-corrected image. Both the synthetic dataset and real dataset are tested. The experiments demonstrate that the proposed algorithm outperforms the compared methods both quantitatively and qualitatively. Full article
(This article belongs to the Special Issue Color Image Processing)
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Open AccessArticle Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping
J. Imaging 2017, 3(3), 39; doi:10.3390/jimaging3030039
Received: 31 July 2017 / Revised: 31 August 2017 / Accepted: 7 September 2017 / Published: 18 September 2017
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Abstract
Accurate high-resolution three-dimensional (3D) models are essential for a non-invasive analysis of phenotypic characteristics of plants. Previous limitations in 3D computer vision algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present an image-based 3D
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Accurate high-resolution three-dimensional (3D) models are essential for a non-invasive analysis of phenotypic characteristics of plants. Previous limitations in 3D computer vision algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present an image-based 3D plant reconstruction system that can be achieved by using a single camera and a rotation stand. Our method is based on the structure from motion method, with a SIFT image feature descriptor. In order to improve the quality of the 3D models, we segmented the plant objects based on the PlantCV platform. We also deducted the optimal number of images needed for reconstructing a high-quality model. Experiments showed that an accurate 3D model of the plant was successfully could be reconstructed by our approach. This 3D surface model reconstruction system provides a simple and accurate computational platform for non-destructive, plant phenotyping. Full article
(This article belongs to the Special Issue 2D, 3D and 4D Imaging for Plant Phenotyping)
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Open AccessErratum Erratum: Ville V. Lehtola, et al. Radial Distortion from Epipolar Constraint for Rectilinear Cameras. J. Imaging 2017, 3, 8
J. Imaging 2017, 3(3), 23; doi:10.3390/jimaging3030023
Received: 23 June 2017 / Revised: 23 June 2017 / Accepted: 23 June 2017 / Published: 23 June 2017
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Abstract
Due to a mistake during the production process, the J. Imaging Editorial Office and the authors wish to make this correction to the paper written by Lehtola et al. [1]. [...]
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