Vertical Images Research Articles

GisGCN: A Visual Graph-Based Framework to Match Geographical Areas through Time

Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding.

A Novel Self-Correction Method for Linear Displacement Measurement Based on 2-D Synthesis Mechanism

With increased range, the accuracy of longer-range linear displacement measurement is difficult to guarantee. According to the analysis, it is inevitable that there will be a certain margin between the direction of the calibration grating and the direction of the reading head, which will impact the longer-range linear displacement measurement. In order to eliminate this measurement error, a self-correction method based on 2-D synthesis is herein proposed. First, we describe the principle of absolute linear displacement measurement based on image recognition. Then, the error model is established, which caused by the included angle between the calibration grating and the reading head. Third, a method is proposed for obtaining the vertical offset of the calibration grating by using the vertical image sensor. Finally, we reach an error self-correction method based on 2-D synthesis. In order to test the feasibility of the proposed method, a linear displacement measuring device with a range of 200 mm was developed. After experiment, the maximum absolute error was reduced from 4.34 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.03~\mu \text{m}$ </tex-math></inline-formula> with the proposed error correction in the long range of 200 mm. The proposed algorithm does not depend on the assembly and installation position, and can realize self-correction of errors. So the method could lay the groundwork for improving larger range linear displacement measurement technology.

Investigation of 3D models acquired with UAV oblique images

The concept of oblique image acquisition entered the literature long after the overhead photogrammetric acquisition techniques. The oblique image acquisition technique is applied to model the edge surfaces of buildings that cannot be obtained with vertical (nadir) image acquisition and eliminate the problems that arise in the orthorectification of high-rise buildings in urban areas. Oblique image acquisition by Unmanned Aerial Vehicles (UAV) independent of aerial photogrammetry is considered possible with unique flight plans. How convenient oblique image acquisition with the help of UAVs is and how it affects the final product is a popular topic studied by researchers. In this study, oblique image acquisition was performed using a rotary-wing UAV (Parrot ANAFI) whose camera can move 180°. With the help of the 3D models obtained, the building was drawn in vector and evaluated to scale. As a result, it has been understood that almost all structural deficiencies in the final products can be eliminated by oblique images. However, it has been revealed that there is still a need for other methods for structures that do not have patterns, such as flat walls.

Estimations of the Passing Height of Approaching Objects.

Limited optical cues associated with ball flight were inadequate to estimate the vertical passing distance of approaching balls. These results suggest that these optical cues either must be integrated with contextual and kinematic cues or must be of larger amplitude to contribute to estimates of vertical passing distance. To intercept or avoid approaching objects, individuals must estimate both when and where the object will arrive. The purpose of this experiment was to determine whether individuals could estimate the vertical passing height of a ball approaching at different linear speeds when vertical angular retinal image velocity and cues for time to contact were minimized. Twenty participants stood 40 feet from a pitching machine that projected tennis balls toward observers at six random speeds from 56 to 80 mph. The flight of the balls was stopped after 9 feet. The actual passing height ranged from about 35 (lowest speed) to 136 cm (highest speed). Observers indicated the height at which they expected the balls to arrive. Overall, the height estimates increased as ball speed increased (means, 121 ± 13 cm [lowest speed] and 131 ± 10 cm [highest speed]). However, only at the higher speeds were the absolute height estimates close to the actual height of the ball. At the higher ball speeds, estimates for participants with some experience in baseball or softball were more accurate (86.4% correct at the highest speed) than estimates for participants with no experience. Overall, estimates of vertical passing distance were inaccurate particularly at the lower speeds. Underestimates of vertical drop at lower speeds may have resulted from overestimates of ball speeds. At short exposure durations, optical cues associated with ball flight were inadequate for predictions of vertical passing distance at all speeds for the no-experience group and at lower speeds for the experienced group.

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R2 was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R2 = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight.

The vertical image projection method for migration stretch compensation

True amplitude inversion is often carried out without taking into account migration distortions to the wavelet. Seismic migration leaves a dip-dependent effect on the wavelet that can cause significant inaccuracies in the inverted impedances obtained from conventional inversion approaches based on 1D vertical convolutional modeling. Neglecting this effect causes misleading inversion results and leakage of dipping noise and migration artifacts from the higher frequency bands to the lower frequencies. I have observed that, despite the dip dependency of this effect, low-dip and flat events may also suffer if they are contaminated with cross-cutting noise, steep migration artifacts, and smiles. I adopt an efficient, effective, and reversible data preconditioning approach that accounts for the dip dependency of the wavelet and is applied to migrated images prior to inversion. My proposed method consists of integrating data with respect to the total wavenumber followed by the differentiation with respect to the vertical wavenumber. This process is equivalent to applying a deterministic dip-consistent preconditioning that projects the data from the total wavenumber to the vertical wavenumber axis. This preconditioning can be applied to pre- and poststack data as well as to amplitude-variation-with-offset attributes such as intercept and gradient before inversion. The vertical image projection methodology that I adopt here reduces the impact of migration artifacts such as cross-cutting noise and migration smiles and improves inverted impedances in synthetic and real data examples. In particular, I indicate that neglecting the proposed preconditioning leads to anomalously higher impedance values along the steeply dipping structures.

Deep learning models for screening of high myopia using optical coherence tomography

This study aimed to validate and evaluate deep learning (DL) models for screening of high myopia using spectral-domain optical coherence tomography (OCT). This retrospective cross-sectional study included 690 eyes in 492 patients with OCT images and axial length measurement. Eyes were divided into three groups based on axial length: a “normal group,” a “high myopia group,” and an “other retinal disease” group. The researchers trained and validated three DL models to classify the three groups based on horizontal and vertical OCT images of the 600 eyes. For evaluation, OCT images of 90 eyes were used. Diagnostic agreements of human doctors and DL models were analyzed. The area under the receiver operating characteristic curve of the three DL models was evaluated. Absolute agreement of retina specialists was 99.11% (range: 97.78–100%). Absolute agreement of the DL models with multiple-column model was 100.0% (ResNet 50), 90.0% (Inception V3), and 72.22% (VGG 16). Areas under the receiver operating characteristic curves of the DL models with multiple-column model were 0.99 (ResNet 50), 0.97 (Inception V3), and 0.86 (VGG 16). The DL model based on ResNet 50 showed comparable diagnostic performance with retinal specialists. The DL model using OCT images demonstrated reliable diagnostic performance to identify high myopia.

ODTiD: Optic Nerve Head SD-OCT Image Dataset.

IntroductionOptic disc tilt (ODT) or tilted optic disc is a common finding in the general population. It is due to anomalous development caused by the malclosure of the embryonic optic fissure. ODT is commonly associated with high myopia as well as other conditions. In recent days, the common method to image the optic disc (OD) is by optical coherence tomography (OCT). To the best of our knowledge, there are no datasets of ODT available in the public domain. This dataset aims to make open access raw ODT OCT images to test out new image processing segmentation algorithms.MethodsThis dataset of ODT images contains both horizontal and vertical cross-sectional images obtained using spectral-domain optical coherence tomography (SD-OCT, Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA). The optic disc cube 200×200 program was used and all the images are aligned with the center of the optic nerve head. This dataset includes images from both clinically normal (20 eyes) and myopic subjects (101 eyes).ResultsThe dataset consists of clear (121) and manually marked (121) images resulting in a total of 242 images. The age distribution for all subjects combined is 27.24 ± 9.28 (range, 11.0–69.0) years. For normal subjects mean ± SD age distribution is 32.40 ± 17.23 years. Similarly, the myopia age distribution is 26.22 ± 6.37 years. Ground truth images, ie, manually segmented by a clinical expert are provided along with other meta-data includes age, gender, laterality, refractive error classification, spherical equivalent (SE), best-corrected visual acuity (BCVA), intraocular pressure (IOP), and axial length (AXL).ConclusionThis open, public database is online at the ICPSR website of the University of Michigan. The dataset can be used to test and validate newly developed automated segmentation algorithms.

Depth Estimation from Light Field Geometry Using Convolutional Neural Networks.

Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the balance between computational time and the accuracy of depth estimation is still worth exploring. The geometry in light field imaging is the basis of depth estimation, and the abundant light-field data provides convenience for applying deep learning algorithms. The Epipolar Plane Image (EPI) generated from the light-field data has a line texture containing geometric information. The slope of the line is proportional to the depth of the corresponding object. Considering the light field depth estimation as a spatial density prediction task, we design a convolutional neural network (ESTNet) to estimate the accurate depth quickly. Inspired by the strong image feature extraction ability of convolutional neural networks, especially for texture images, we propose to generate EPI synthetic images from light field data as the input of ESTNet to improve the effect of feature extraction and depth estimation. The architecture of ESTNet is characterized by three input streams, encoding-decoding structure, and skipconnections. The three input streams receive horizontal EPI synthetic image (EPIh), vertical EPI synthetic image (EPIv), and central view image (CV), respectively. EPIh and EPIv contain rich texture and depth cues, while CV provides pixel position association information. ESTNet consists of two stages: encoding and decoding. The encoding stage includes several convolution modules, and correspondingly, the decoding stage embodies some transposed convolution modules. In addition to the forward propagation of the network ESTNet, some skip-connections are added between the convolution module and the corresponding transposed convolution module to fuse the shallow local and deep semantic features. ESTNet is trained on one part of a synthetic light-field dataset and then tested on another part of the synthetic light-field dataset and real light-field dataset. Ablation experiments show that our ESTNet structure is reasonable. Experiments on the synthetic light-field dataset and real light-field dataset show that our ESTNet can balance the accuracy of depth estimation and computational time.

Guest-Mediated Hierarchical Self-Assembly of Dissymmetric Organic Cages to Form Supramolecular Ferroelectrics

Guest-Mediated Hierarchical Self-Assembly of Dissymmetric Organic Cages to Form Supramolecular Ferroelectrics

A multimodal Discourse Analysis of the First Promotional Video in #This is Egypt Campaign

This paper investigates, from a multimodal perspective, the effectiveness of the first promotional video in #This is Egypt tourism campaign which has been launched in 2016 by the Egyptian tourism ministry as a mean of developing tourism industry in Egypt. The study also aims to examine the interconnection of different modes of communication such as image, music , typography and color through applying Kress and Van Leeuween’s (2006) theory of visual grammar , David Machin's (2010) theatrical framework of analyzing sound and typography and Van Leeuween's (2002) taxonomy of color. Results suggest that the deployment, interconnection and complementation of the four modes contribute to the success of the video. Narrative processes, conceptual processes, close shots, frontal horizontal and vertical images, high modality, fully saturated and bright colors, low and quick rhythm and narrowed, heavy and upright subtitles construct a picturesque image of Egypt as a great country with a magnificent culture and great history.

Augmented reality-based autostereoscopic surgical visualization system for telesurgery.

The visualization of remote surgical scenes is the key to realizing the remote operation of surgical robots. However, current non-endoscopic surgical robot systems lack an effective visualization tool to offer sufficient surgical scene information and depth perception. We propose a novel autostereoscopic surgical visualization system integrating 3D intraoperative scene reconstruction, autostereoscopic 3D display, and augmented reality-based image fusion. The preoperative organ structure and the intraoperative surface point cloud are obtained from medical imaging and the RGB-D camera, respectively, and aligned by an automatic marker-free intraoperative registration algorithm. After registration, preoperative meshes with precalculated illumination and intraoperative textured point cloud are blended in real time. Finally, the fused image is shown on a 3D autostereoscopic display device to achieve depth perception. A prototype of the autostereoscopic surgical visualization system was built. The system had a horizontal image resolution of 1.31mm, a vertical image resolution of 0.82mm, an average rendering rate of 33.1 FPS, an average registration rate of 20.5 FPS, and average registration errors of approximately 3mm. A telesurgical robot prototype based on 3D autostereoscopic display was built. The quantitative evaluation experiments showed that our system achieved similar operational accuracy (1.79 ± 0.87mm) as the conventional system (1.95 ± 0.71mm), while having advantages in terms of completion time (with 34.11% reduction) and path length (with 35.87% reduction). Post-experimental questionnaires indicated that the system was user-friendly for novices and experts. We propose a 3D surgical visualization system with augmented instruction and depth perception for telesurgery. The qualitative and quantitative evaluation results illustrate the accuracy and efficiency of the proposed system. Therefore, it shows great prospects in robotic surgery and telesurgery.

A multimodal Discourse Analysis of the First Promotional Video in #This is Egypt Campaign

This paper investigates, from a multimodal perspective, the effectiveness of the first promotional video in #This is Egypt tourism campaign which has been launched in 2016 by the Egyptian tourism ministry as a mean of developing tourism industry in Egypt. The study also aims to examine the interconnection of different modes of communication such as image, music , typography and color through applying Kress and Van Leeuween’s (2006) theory of visual grammar , David Machin's (2010) theatrical framework of analyzing sound and typography and Van Leeuween's (2002) taxonomy of color. Results suggest that the deployment, interconnection and complementation of the four modes contribute to the success of the video. Narrative processes, conceptual processes, close shots, frontal horizontal and vertical images, high modality, fully saturated and bright colors, low and quick rhythm and narrowed, heavy and upright subtitles construct a picturesque image of Egypt as a great country with a magnificent culture and great history. يدرس البحث من منظور لغوي متعدد الوسائط تأثير وفاعلية الفيديو الترويجي الأول لحملة # هذة مصر والتي قامت وزارة السياحة المصرية بإطلاقها عام 2016 من أجل تطوير السياحة في مصر ويستهدف البحث دراسة ترابط وتکامل أنماط الإتصال المختلفة مثل الصورة والموسيقي والخط واللون وذلک من خلال تطبيق نظرية العالمان کريس وفان ليفن (2006) لتحليل الصورة ونظرية ديفيد ماشين (2010) لتحليل الصوت والخط ونظرية فان ليفين (2002) لتحليل اللون. وأوضحت الدراسة أن تناسق وسائل الاتصال الأربعة يسهم بشکل کبير في نجاح الفيديو وکذلک إبراز صورة مصر کبلد سياحي کبير.

A Study on the Large-Area Image Acquisition Using Stage Transfer Synchronization Method in a Low-Energy Microcolumn System

We studied the stage transfer synchronization method to improve the low throughput which is the biggest drawback of the raster scan method when applied to image acquisition in a low energy microcolumn system. Through preliminary experiments, we were able to acquire some sample images and some images of the measured samples by applying the synchronization method to a conventional single-microcolumn system. The image acquired in this preliminary experiment was three times larger than that acquired by the raster scan method in the vertical direction. In this study, we tested two types of the synchronization method: the first method acquires an image by horizontal electron beam scanning followed by a vertical stage transfer, and the second one acquires an image by vertical electron beam scanning followed by a horizontal stage transfer. By applying the vertical stage transfer synchronization method, we could acquire a larger image than that acquired by the conventional raster scan method. The vertical stage transfer synchronization method image was 1.8 times larger in the horizontal direction and 6 times larger in the vertical direction. In addition, a larger image approximately nine times in the horizontal direction and five times in the vertical direction was acquired by applying the horizontal stage transfer synchronization method.

Fractal analysis of muddy submarine channel slope instability from sub-bottom profile images

Submarine channel slope stability is a key factor in marine traffic safety. Channel slope instability and collapse may change the geometry of a channel, block ports, and lead to marine traffic accidents. In this study, fractal and multifractal analyses are performed on sub-bottom profile images obtained before, during and after episodes of slope instability and collapse in sediments obtained from a muddy submarine channel. Results show that sub-bottom profiles exhibit distinctive fractal properties, and that changes in sub-bottom profiles are reflected by changes in fractal dimensions. For example, fractal dimensions are highest during slope collapse. During episodes of slope instability and collapse, fractal dimensions of temporal domain sub-bottom profile images exhibit distinct variations at different depths along the slope. Meanwhile, the fractal dimensions of vertical spatial domain sub-bottom profile images reveal that the slope experiences multiple fluctuating changes during collapse. Multifractal spectrum parameters highlight image non-uniformity. For example, spectrum width temporarily increases during slope instability and collapse. Results of this study suggest that the fractal dimensions and multifractal parameters of sub-bottom profiles are sensitive to channel slope instability and collapse, thereby providing useful indicators with high potential for use in slope collapse prediction and early warning systems.

High-resolution compositional mapping of surfaces in non-contact atomic force microscopy by a new multi-frequency excitation

In this paper, a new multi-frequency excitation method based on combination resonance is introduced to enhance the non-contact atomic force microscopy performance. In combination resonance, excitation frequencies are selected so that summation/subtraction of excitation frequencies is close to the natural frequencies of the microcantilever. Due to the nonlinear nature of this method, the probe response to excitation is very sensitive to change in tip-sample forces. This could be used to generate high-resolution compositional mapping and topographical images of the surface. The present study reveals that both amplitude and phase shift of the combination resonance are sensitive to change in parameters such as Hamaker constant, damping coefficient, Young’s modulus and tip-sample initial distance. It is observed that because of high sensitivity to Hamaker constant a small change in the surface material leads to considerable variations in amplitude and phase shift. This sensitivity is employed to improve compositional mapping of the surface materials. It is also found out that the response amplitude in the combination resonance is very sensitive to change in the tip-sample initial distance. This sensitivity may be used to reduce the vertical noise and increase image resolution, especially in environments with low quality factors. Overall, using this technique the image contrast increases significantly and high resolution compositional mapping of surfaces is achieved.

Coastal erosion in NW Spain: Recent patterns under extreme storm wave events

Coastal dunes are sensitive to both anthropic and natural processes of erosion. In this study, we analyse the geomorphic changes in 15 dune-fringed coastlines of Asturias (NW Spain) for the period 1992 to 2014 to determine specific drivers of erosion. Coastline migration patterns were obtained using geospatial analysis of vertical aerial images and field surveys. The annual and maximum wave energy, maximum significant wave height, duration and number of storm events were determined for each climatic year by using available wave data from both recorded buoy data (1997–2014) and the WAM numerical model (1956–2015). The study shows that since 2006, coastline retreat has significantly accelerated along the Asturias coast. In particular, severe erosion of sandy beaches and dunes occurred in 2013/14, reaching up to 40 m of coastline retreat and destroying numerous ports and seafront promenades. Increased frequency of unusually strong storm waves, with significant wave height (Hs) exceeding 9 m, together with an unusual 5° westward variation from the mode in offshore wave approach direction during these storms, appear to be the main catalysts in heightened erosion capacity of storm waves in the 2013/14 period. This resulted in the removal of small dune areas and a severe recession in the easternmost part of the largest (natural) dune fields in the region. Under these stormy periods, foredunes and climbing dunes developed notably pronounced escarpments and blowouts, with sand subsequently relocated into the foreshore and backshore zones. This study demonstrated that increased frequency in powerful storm events (Hs > 9 m) and an alteration in storm approach direction, can lead to significantly enhanced erosion of dune coastlines, even along those that are modally-attuned to high energy events.

EYES DETECTION IMPROVEMENT BY TRADITIONAL AND MODIFIED PULSE COUPLED NEURAL NETWORK

This paper presents a fast method and robust for eyes detection, using Traditional and Modified Pulse Coupled Neural Networks (MPCNN). The functionality of MPCNN is not the same as Traditional Pulse Coupled Neural Networks (TPCNN) because we are taking care about human visual perception not only the neighbor pixel characteristic. Due of this feature, the algorithm response time is around two milliseconds. The approach has two components including: face area detection based on segmentation and eyes detection using edge. Segmentation operation is ensured by MPCNN and edge detection by TPCNN.The biggest region which is constituted by pixel value one will be the human face area. The segmented face zone which will be the input of TPCNN for edge detection undergoes a vertical gradient operation. The two gravity’s center of close edge near the horizontal line which corresponds to the peak value of horizontal projection of vertical gradient image will be the eyes.

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic.

Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lightweight pedestrian detection network to accurately detect pedestrians by human head detection in real-time and then calculate the social distancing between pedestrians on UAV images. In particular, our network follows the PeleeNet as backbone and further incorporates the multi-scale features and spatial attention to enhance the features of small objects, like human heads. The experimental results on Merge-Head dataset show that our method achieves 92.22% AP (average precision) and 76 FPS (frames per second), outperforming YOLOv3 models and SSD models and enabling real-time detection in actual applications. The ablation experiments also indicate that multi-scale feature and spatial attention significantly contribute the performance of pedestrian detection. The test results on UAV-Head dataset show that our method can also achieve high precision pedestrian detection on UAV images with 88.5% AP and 75 FPS. In addition, we have conducted a precision calibration test to obtain the transformation matrix from images (vertical images and tilted images) to real-world coordinate. Based on the accurate pedestrian detection and the transformation matrix, the social distancing monitoring between individuals is reliably achieved.

Three-dimensional histological explanation of the dermoscopy patterns in acral melanocytic lesions.

Dermoscopic images of pigmented lesions have distinct features on the sole where skin ridges and furrows exist. Pigmentation of benign nevus usually locates on the skin furrow, while the malignant melanoma is pigmented on the skin ridge. Correspondence between dermoscopy and pathology in the pigmented lesions on soles have been studied based on conventional vertical pathological images. However, for the full understanding of the correspondence, observation of horizontal histological images would be required, because the epidermis constructs unique horizontal structures, namely crista profunda limitans, crista profunda intermedia, and transverse ridge. In this study, we analyzed basic dermoscopic images of the representative acral melanocytic lesions (nevus, lentigo, and malignant melanoma) by horizonal histological images. We created serial horizontal pathological images by digital reconstruction of a hundred of serial vertical images. We could show that parallel furrow pattern is created by the pigmentation of crista profunda limitans, parallel ridge pattern by the pigmentation of both of crista profunda limitans and crista profunda intermediate, and lattice-like pattern by the pigmentation of transverse ridge. Our results would be useful for the intuitive histological understanding of dermoscopy.