Edge Map Research Articles

Edge detection using fractional derivatives and information sets

The inherent memory effect of the Grunwald–Letnikov fractional derivatives is combined with the effectiveness of information sets for detecting edges in digital images. Fractional derivatives are utilized in the computation of fractional gradients, which are further processed using information sets for the proposed edge detector to extract more edges than possible with the traditional edge detectors. In the proposed approach, first the gradient operators of the Sobel mask are converted into the fractional form and convolved with the given gray image. In the next step, the histogram of the fractional gradients is fuzzified using the Gaussian membership function and the sigmoidal membership function. The optimal parameters for these membership functions are selected through a grid search method based on the information set-based edge strength factor ESf. In the final step, defuzzification is performed to obtain the final edge maps. By plotting the edge maps, analyzing the boundary information, and Pratt’s figure of merit scores for images in the Berkeley segmentation dataset, it is observed that the resulting edge maps of the proposed edge detector contain more quantitative and qualitative information than that of traditional edge detectors even in the presence of noise.

Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters

BackgroundRician noise, bias fields and blur are the common distortions that degrade MRI images during acquisition. Blur is unique in comparison to Rician noise and bias fields because it can be introduced into an image beyond the acquisition stage such as postacquisition processing and the manifestation of pathological conditions. Most current blur assessment algorithms are designed and validated on consumer electronics such as television, video and mobile appliances. The few algorithms dedicated to medical images either requires a reference image or incorporate manual approach. For these reasons it is difficult to compare quality measures from different images and images with different contents. Furthermore, they will not be suitable in environments where large volumes of images are processed. In this report we propose a new blind blur assessment method for different types of MRI images and for different applications including automated environments.MethodsTwo copies of the test image are generated. Edge map is extracted by separately convolving each copy of the test image with two parallel difference of Gaussian filters. At the start of the multiscale representation, the initial output of the filters are equal. In subsequent scales of the multiscale representation, each filter is tuned to different operating parameters over the same fixed range of Gaussian scales. The filters are termed low and high energy filters based on their characteristics to successively attenuate and highlight edges over the range of multiscale representation. Quality score is predicted from the distance between the normalized mean of the edge maps at the final output of the filters.ResultsThe proposed method was evaluated on cardiac and brain MRI images. Performance evaluation shows that the quality index has very good correlation with human perception and will be suitable for application in routine clinical practice and clinical research.

Characterization of Lithium Ion Battery Materials with Valence Electron Energy-Loss Spectroscopy.

Cutting-edge research on materials for lithium ion batteries regularly focuses on nanoscale and atomic-scale phenomena. Electron energy-loss spectroscopy (EELS) is one of the most powerful ways of characterizing composition and aspects of the electronic structure of battery materials, particularly lithium and the transition metal mixed oxides found in the electrodes. However, the characteristic EELS signal from battery materials is challenging to analyze when there is strong overlap of spectral features, poor signal-to-background ratios, or thicker and uneven sample areas. A potential alternative or complementary approach comes from utilizing the valence EELS features (<20 eV loss) of battery materials. For example, the valence EELS features in LiCoO2 maintain higher jump ratios than the Li-K edge, most notably when spectra are collected with minimal acquisition times or from thick sample regions. EELS maps of these valence features give comparable results to the Li-K edge EELS maps of LiCoO2. With some spectral processing, the valence EELS maps more accurately highlight the morphology and distribution of LiCoO2 than the Li-K edge maps, especially in thicker sample regions. This approach is beneficial for cases where sample thickness or beam sensitivity limit EELS analysis, and could be used to minimize electron dosage and sample damage or contamination.

A Review of Supervised Edge Detection Evaluation Methods and an Objective Comparison of Filtering Gradient Computations Using Hysteresis Thresholds

Useful for human visual perception, edge detection remains a crucial stage in numerous image processing applications. One of the most challenging goals in contour detection is to operate algorithms that can process visual information as humans require. To ensure that an edge detection technique is reliable, it needs to be rigorously assessed before being used in a computer vision tool. This assessment corresponds to a supervised evaluation process to quantify differences between a reference edge map and a candidate, computed by a performance measure/criterion. To achieve this task, a supervised evaluation computes a score between a ground truth edge map and a candidate image. This paper presents a survey of supervised edge detection evaluation methods. Considering a ground truth edge map, various methods have been developed to assess a desired contour. Several techniques are based on the number of false positive, false negative, true positive and/or true negative points. Other methods strongly penalize misplaced points when they are outside a window centered on a true or false point. In addition, many approaches compute the distance from the position where a contour point should be located. Most of these edge detection assessment methods will be detailed, highlighting their drawbacks using several examples. In this study, a new supervised edge map quality measure is proposed. The new measure provides an overall evaluation of the quality of a contour map by taking into account the number of false positives and false negatives, and the degrees of shifting. Numerous examples and experiments show the importance of penalizing false negative points differently than false positive pixels because some false points may not necessarily disturb the visibility of desired objects, whereas false negative points can significantly change the aspect of an object. Finally, an objective assessment is performed by varying the hysteresis thresholds on contours of real images obtained by filtering techniques. Theoretically, by varying the hysteresis thresholds of the thin edges obtained by filtering gradient computations, the minimum score of the measure corresponds to the best edge map, compared to the ground truth. Twenty-eight measures are compared using different edge detectors that are robust or not robust regarding noise. The scores of the different measures and different edge detectors are recorded and plotted as a function of the noise level in the original image. The plotted curve of a reliable edge detection measure must increase monotonously with the noise level and a reliable edge detector must be less penalized than a poor detector. In addition, the obtained edge map tied to the minimum score of a considered measure exposes the reliability of an edge detection evaluation measure if the edge map obtained is visually closer to the ground truth or not. Hence, experiments illustrate that the desired objects are not always completely visible using ill-suited evaluation measure.

Partially shaded sketch-based image search in real mobile device environments via sketch-oriented compact neural codes

With the advent of touch screens in mobile devices, sketch-based image search is becoming the most intuitive method to query multimedia contents. Traditionally, sketch-based queries were formulated with hand-drawn shapes without any shades or colors. The absence of such critical information from sketches increased the ambiguity between natural images and their sketches. Although it was previously considered too cumbersome for users to add colors to hand-drawn sketches in image retrieval systems, the modern day touch input devices make it convenient to add shades or colors to query sketches. In this work, we propose deep neural codes extracted from partially colored sketches by an efficient convolutional neural network (CNN) fine-tuned on sketch-oriented augmented dataset. The training dataset is constructed with hand-drawn sketches, natural color images, de-colorized, and de-texturized images, coarse and fine edge maps, and flipped and rotated images. Fine-tuning CNN with augmented dataset enabled it to capture features effectively for representing partially colored sketches. We also studied the effects of shading and partial coloring on retrieval performance and show that the proposed method provides superior performance in sketch-based large-scale image retrieval on mobile devices as compared to other state-of-the-art methods.

Semantic modeling of indoor scenes with support inference from a single photograph

AbstractWe present an automatic approach for the semantic modeling of indoor scenes based on a single photograph, instead of relying on depth sensors. Without using handcrafted features, we guide indoor scene modeling with feature maps extracted by fully convolutional networks. Three parallel fully convolutional networks are adopted to generate object instance masks, a depth map, and an edge map of the room layout. Based on these high‐level features, support relationships between indoor objects can be efficiently inferred in a data‐driven manner. Constrained by the support context, a global‐to‐local model matching strategy is followed to retrieve the whole indoor scene. We demonstrate that the proposed method can efficiently retrieve indoor objects including situations where the objects are badly occluded. This approach enables efficient semantic‐based scene editing.

Example‐based synthesis for sound of ocean waves caused by bubble dynamics

AbstractWe present an automatic approach for the semantic modeling of indoor scenes based on a single photograph, instead of relying on depth sensors. Without using handcrafted features, we guide indoor scene modeling with feature maps extracted by fully convolutional networks. Three parallel fully convolutional networks are adopted to generate object instance masks, a depth map, and an edge map of the room layout. Based on these high‐level features, support relationships between indoor objects can be efficiently inferred in a data‐driven manner. Constrained by the support context, a global‐to‐local model matching strategy is followed to retrieve the whole indoor scene. We demonstrate that the proposed method can efficiently retrieve indoor objects including situations where the objects are badly occluded. This approach enables efficient semantic‐based scene editing.

Globally Measuring the Similarity of Superpixels by Binary Edge Maps for Superpixel Clustering

This paper proposes an edge-based superpixel similarity measurement, which globally evaluates the similarity between superpixels by binary edge maps. The basic idea is to assess whether the superpixels are surrounded by the same edges. To this end, we first describe the edge spatial distributions by directional regions and then use the directional regions to represent the surrounding relationships of superpixels and edges by their traverse relationships, which form the histogram feature. Finally, the similarity is simply calculated by the distances between the features. To verify the proposed similarity measurement, we use our global similarity measurement to perform superpixel clustering. Two clustering methods, the directed graph clustering (DGC) and spectral clustering (ultrametric contour map) are combined to achieve the clustering process. The combination of our global similarity measurement and DGC to form a new three-layer-based superpixel generation method, which can quickly generate the superpixel from edge maps, is highlighted. We verify the global similarity measurement by the BSDS500 dataset. The experimental results demonstrate that the proposed global similarity measurement can improve the clustering accuracy in terms of larger intersection-over-union-criterion-based values. The code can be downloaded from https://github.com/FanmanMeng/Superpixel-Similarity-Measurement .

Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells.

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.

Local directional ZigZag pattern: A rotation invariant descriptor for texture classification

Local feature descriptors play a key role in texture classification tasks. However, such traditional descriptors are deficient to capture the edges and orientations information and local intrinsic structure of images. This letter introduces a simple, new, yet powerful rotation invariant texture descriptor named Local Directional ZigZag Pattern (Ldzp) by ZigZag scanning for effective representation of texture. Here at first we compute the directional edge information, so called local directional edge map (Ldem) of a texture image using the Kirsch compass mask in six different directions. Then Local ZigZag Pattern (Lzp) is extracted from all Ldem images. Basically, the Lzp characterizes the spatial ZigZag structure based on the relation between reference pixel and its adjacent neighboring pixels and is insensitive to the illumination changes. Finally, the uniform pattern histograms are computed from all directional Lzp maps which are concatenated to form the final Ldzp descriptor. Extensive experiments on texture classification shows the proposed Ldzp descriptor achieves state-of-the-art performance in terms of average classification accuracy when applied to the large and well-known benchmark Outex database. We have also shown that Ldzp descriptor is equally powerful for human face recognition.

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection.

Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges. An initial blur estimate is computed at the detected scale-consistent edge points and a novel connected edge filter is proposed to smooth the sparse blur map based on pixel connectivity within detected edge contours. Finally, a fast guided filter is used to propagate the sparse blur map through the whole image. Experimental results show that the proposed approach presents a very good compromise between estimation error and running time when compared with the state-of-the-art methods. We also explore our blur estimation method in the context of image deblurring, and show that metrics typically used to evaluate blur estimation may not correlate as expected with the visual quality of the deblurred image.

Parametric active contour based on sparse decomposition for multi-objects extraction

Active contour model has been widely used over the past decade in image segmentation. For parametric active contour model, it is always used to segment objects because of its simplicity and fast evolution. However, it could not extract multi-objects with one initial contour because of topological invariance of parametric contour and existing equilibrium points between objects in vector fields. In this paper, a method of multi-objects extraction with parametric active contour model is proposed. Firstly, after an edge map is computed, the idea of sparse representation and decomposition for edge map is introduced in order to obtain new edge maps to better describe the objects; Secondly, the obtained edge maps are used to generate vector fields; Finally, one initial contour is evolved in every vector field to extract corresponding objects. Experimental results show that the proposed model could extract multi-target objects by using one initial contour. Quantitative evaluation of the segmentation results with tested methods also shows that the proposed method is more robust to noise and gets the better effect of segmentation accuracy. Furthermore, the proposed method allows parallel computation of algorithms, hence further reducing the computational time.

Turtle edge encoding and flood fill based image compression scheme

Over the last two decades, great improvements have been made in image and video compression techniques driven by a growing demand for storage and transmission of visual information. This paper focuses on image compression, the main objective of an image compression technique is to remove as much redundant information as possible without destroying the image integrity. This paper proposes an edge based image compression scheme for cartoon images. Initially the edges of the image are identified using zero-crossings edge detector, and the edges are decoded by using a novel encoder based on turtle graphics. From the edge map the closed regions are labelled to estimate the color quantization levels. However the isolated edges falls inside the closed regions are stored separately and the region is encoded with its color/gray value at a random seed pixel. While decoding the image, a flood-fill algorithm is used to fill each region by its corresponding color, starting from the seed point. The boundary of each region is marked with the edge contour (only for the closed regions), and the isolated edges are marked over the decoded image from the original edge map. The proposed Turtle Edge encoder and flood-fill based image compression approach is analyzed with a collection of cartoon images. The performance of the proposed compression method is compared with the state-of-art compression methods like JPEG and JPEG2000 and the recent algorithms, the experimental results indicate that the proposed turtle edge and flood-fill based approach is able to achieve better compression ratio within less computation time.

Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming

The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.

Quantum-Confined Electronic States Arising from the Moiré Pattern of MoS2-WSe2 Heterobilayers.

A two-dimensional (2D) heterobilayer system consisting of MoS2 on WSe2, deposited on epitaxial graphene, is studied by scanning tunneling microscopy and spectroscopy at temperatures of 5 and 80 K. A moiré pattern is observed, arising from lattice mismatch of 3.7% between the MoS2 and WSe2. Significant energy shifts are observed in tunneling spectra observed at the maxima of the moiré corrugation, as compared with spectra obtained at corrugation minima, consistent with prior work. Furthermore, at the minima of the moiré corrugation, sharp peaks in the spectra at energies near the band edges are observed for spectra acquired at 5 K. The peaks correspond to discrete states that are confined within the moiré unit cells. Conductance mapping is employed to reveal the detailed structure of the wave functions of the states. For measurements at 80 K, the sharp peaks in the spectra are absent, and conductance maps of the band edges reveal little structure.

Explicit Edge Inconsistency Evaluation Model for Color-Guided Depth Map Enhancement

Color-guided depth enhancement is used to refine depth maps according to the assumption that the depth edges and the color edges at the corresponding locations are consistent. In methods on such low-level vision tasks, the Markov random field (MRF), including its variants, is one of the major approaches that have dominated this area for several years. However, the assumption above is not always true. To tackle the problem, the state-of-the-art solutions are to adjust the weighting coefficient inside the smoothness term of the MRF model. These methods lack an explicit evaluation model to quantitatively measure the inconsistency between the depth edge map and the color edge map, so they cannot adaptively control the efforts of the guidance from the color image for depth enhancement, leading to various defects such as texture-copy artifacts and blurring depth edges. In this paper, we propose a quantitative measurement on such inconsistency and explicitly embed it into the smoothness term. The proposed method demonstrates promising experimental results compared with the benchmark and state-of-the-art methods on the Middlebury ToF-Mark, and NYU data sets.

Natural Images Contour Segmentation

This paper, a combination of edge detection and contour based segmentation approach for object contour delineation is proposed. The proposed approach employs a new methodology for segmenting the fruit contour from the indoor and outdoo r natural images more effectively. The overall process is carried out in five steps. The first step is to pre - process the image in order to convert the colour image to grayscale image. Second step is the adoption of Laplacian of Gaussian edge detection and a new corner template detection algorithm for adjustment of the pixels along the edge map in the interpolation process. Third step is the reconstruction process by implementing two morphology operators with embedded of inversion condition and dynamic thr eshold to preserve and reconstruct object contour. Fifth step is ground mask process in which the outputs of the inference obtained for each pixel is combined to a final segmented output, which provides a segmented foreground against the black background. This proposed algorithm is tested over 150 indoor and 40 outdoor fruit images in order to analyse its efficiency. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 100 % in segmenting indoor and outdoor natural images. This algorithm also present a fully automatic model based system for segmenting fruit images of the natural environment.

Delineation of Carpal Bones From Hand X-Ray Images Through Prior Model, and Integration of Region-Based and Boundary-Based Segmentations

Image segmentation is critical and challenging in computer vision and medical image analysis. Despite decades of research, existing segmentation algorithms are still subject to typical segmentation problems, such as over-segmentation, under-segmentation, and non-closed and spurious edges. In this paper, taking the carpal bones from hand X-ray images as the foreground regions, we propose a segmentation approach to integrate segmentations from region-based and boundary-based methods to tackle these typical segmentation problems. First, adaptive local thresholding and adaptive Canny edge detection are explored to extract foreground regions and the edge map. Second, the integration of the edge map and foreground regions by XORing is proposed, to tackle the over-segmentation by adding a background boundary from the edge map near the carpal bone boundary so as to break the connection between the foreground and the over-segmented background, to handle under-segmentation by adding a foreground boundary from the edge map near the carpal bone boundary so as to enclose the missing foreground due to under-segmentation, and to complement non-closed edge and spurious edge from the edge map through the carpal bone regions from the local adaptive thresholding. Optionally, marker-controlled watershed segmentation or an active contour-based method is employed to refine the integrated segmentation. The segmented foreground regions are identified through exploring a prior model. The proposed approach has been validated on 30 representative X-ray hand images for non-overlapping carpal bones to yield an average Dice coefficient of 0.976 ± 0.006. The proposed method could provide a theory or tool for accurately segmenting images with discernible boundaries and non-uniform grayscale distribution both within the background and foreground.

An approach to extract edge maps in infrared based breast images using inverse Perona-Malik diffusion filter

Infrared thermography is the adjunctive tool for early diagnosis of breast cancer. The infrared breast images are of low Signal to Noise Ratio (SNR) and amorphous in nature which makes analysis a challenging task. In this work, an attempt is made to extract the edge map from infrared breast images using inverse Perona-Malik (PM) model. This non-linear filter varies the diffusion near the interferences and edges using inverse gradient and new nearest-neighbour scheme. The edge maps are extracted for various gradient thresholds. The statistical features such as average gradient, contrast, entropy and variance are extracted from the edge map to find the optimal gradient threshold. The diffused image of optimal gradient threshold value is validated using SNR. Results show that the statistical features are found to have maximum value for the gradient threshold of 5. It is also observed that SNR obtained from the diffused image is improved by 30 dB compared to raw image. Inverse PM model is found to reduce the noise and enhance the edges. The integration of denoising and edge map extraction would result in accurate segmentation and aid for early diagnosis.

An approach to extract edge maps in infrared based breast images using inverse Perona-Malik diffusion filter

Infrared thermography is the adjunctive tool for early diagnosis of breast cancer. The infrared breast images are of low Signal to Noise Ratio (SNR) and amorphous in nature which makes analysis a challenging task. In this work, an attempt is made to extract the edge map from infrared breast images using inverse Perona-Malik (PM) model. This non-linear filter varies the diffusion near the interferences and edges using inverse gradient and new nearest-neighbour scheme. The edge maps are extracted for various gradient thresholds. The statistical features such as average gradient, contrast, entropy and variance are extracted from the edge map to find the optimal gradient threshold. The diffused image of optimal gradient threshold value is validated using SNR. Results show that the statistical features are found to have maximum value for the gradient threshold of 5. It is also observed that SNR obtained from the diffused image is improved by 30 dB compared to raw image. Inverse PM model is found to reduce the noise and enhance the edges. The integration of denoising and edge map extraction would result in accurate segmentation and aid for early diagnosis.