Edge Map Research Articles

On the edge: A scalable daltonization method focusing chromatic edges and contrast

Color Vision Deficiency (CVD) leads to a reduced capability to identify chromatic edges and contrast and may cause significant problems in various color tasks like, for example, comparative color tasks. Many daltonization methods to improve color perception of color-deficient people, however, change naturalness of confusion colors, which might complicate other color tasks like, for example, connotative and denotative color tasks. Thus, we present a daltonization method focusing on the enhancement of chromatic edges and contrast while preserving the naturalness of object colors as good as possible. Our proposed method, Yoshi-II-edge, is based on a previously presented method, Yoshi-II, which projects and rotates the lost information by color-deficient observers along the direction of optimal visibility. Yoshi-II-edge limits this enhancement to chromatic edges and contrast by computing an edge map obtained from the gradient of the error image between the original and its simulation. Furthermore, we propose a threshold and dilation to influence the width of the daltonized edge. We show that the performance of this method depends on the juxtaposition of confusion colors in the image. More precisely, Yoshi-II-edge performs well in images with adjacent areas of confusion colors but performs poorly in images with non-adjacent areas of confusion colors.

Image Encryption using Edge Map and Key Image

Objective: An Image Encryption algorithm is proposed which uses an image as key and concept of Edge Map for Encryption. Method/Analysis: The proposed algorithm uses gradient operators like Sobel, Roberts, Prewitt and Canny edge detectors for generating the edge map of key image and which is used for encrypting the original image with xor operation. Findings: The encrypted image shows remarkable difference from the actual image which is shown in the histogram analysis and other statistical tests. Conclusion/Applications: The proposed algorithm achieves considerable level of encryption, which makes it suitable to be used in different applications. It is used for protection of images against various kinds of common attacks like brute force attacks. Keywords: Cryptanalysis, Edge Map, Gradient Operators, Histogram Analysis, Image Encryption, Key Image

Realization of Aggregate Applications using Dynamic Behaviour of Cellular Automata

Cellular automata have proved many of its capabilities and have bestowed a lot in many fields. With the emergence of CA, fabric pattern production has increased in less amount of time. For weaving, cellular automata start with some pattern, then continues with a sequence of steps to produce a new pattern or to change the colour of the cell in a lattice, by using particular transition rules. The main purpose of using cellular automata algorithm as it provides the superlative edge maps and the outstanding quality with one pixel wide edge, with edges having no breaks. We assessed cellular automata capabilities and CA a lot contributions in pattern generation, transformation, and data processing. This research paper reflects the integration of cellular automata across different disciplines. In this paper, we assess the computationally enriched CA rules facilitating spot detection in the study of medical images for cancer diagnosis. The dynamic behaviour of CA increases the scope of transformation and makes it practical for morphing. CA provides prediction of protein structural class and processes dynamic simulation of protein. Flexibility of CA facilitates parallel processing using VLSI, colour graph modelling the linear rules, and its exercise in fabric weaving.

A 3D–2D face recognition method based on extended Gabor wavelet combining curvature and edge detection

The main limitation in 3D face recognition (FR) systems is their susceptibility to scanning difficulties and uncontrolled environments such as pose, illumination and expression variety. This paper proposes a new FR framework based on 3D to 2D mesh deforming and combined Gabor curvature and edge maps. The advantage of this method comes from the powerful saliency distribution achieved from applying extended Gabor wavelets to 2D projected face meshes. The extracted feature vectors are classified using the outstanding robustness of the support vector machine. Experiments carried out on common databases proved that valid accuracy rates can be accomplished by the proposed approach comparing to other existing methods.

A full reference Morphological Edge Similarity Index to account processing induced edge artefacts in magnetic resonance images

An objective measure of edge similarity between the original and processed images to quantify the processing induced artefacts in medical image computing is proposed in this article. Globally accepted Image Quality Analysis (IQA) indices such as Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) measure the structural similarity between the original and processed image and do not specifically reflect the resemblance of the edge content. Most of the IQA indices either do not comply with the subjective quality ratings or they are prone to noise level. In the proposed Morphological Edge Similarity Index (MESI), the binary edge maps of the reference and processed images are generated via gradient based threshold and these edge maps are objectively compared to yield a reliable edge quality metric. The index is found superior to Edge Preservation Index (EPI), Edge Strength Similarity based Image quality Metric (ESSIM) and SSIM in terms of dynamic variability, correlation with subjective quality ratings, robustness to noise and sensitivity to degradation in edge quality caused by blockiness artefacts in image compression. MESI exhibits a correlation of 0.9985, very close to unity, with the subjective quality ratings. It is useful for objectively evaluating the performance of denoising, sharpening and enhancement schemes and for the selection of optimum value of the arbitrary parameters used in them.

Vision-Based Vehicle Detection for a Forward Collision Warning System

A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition; 2) the local features are mutually processed to improve the reliability of vehicle detection; 3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions; 4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.

Thermal-to-Visible Face Alignment on Edge Map

This paper presents a novel thermal-to-visible face alignment method based on edge map. The alignment procedure is inspired by iterative closest points matching. However, iterative closest point (ICP) sometimes converges to the local minimum for edge face alignment. In this paper, pointwise distance, which refers to the linear combination of positional and edge local pattern, is proposed as it gives good initial estimation of the closest points matching in iteration. Another problem is that numerous spurious corresponding points will occur in the closest pairs. Therefore, the selection criteria for partial closest pairs are designed, according to magnitude and orientation of the displacement, to remove the spurious corresponding points caused by edge faces. The alignment performance is evaluated by the bias of facial fiducial points and heterogeneous face recognition. The experimental results, including intra-class and inter-class, are demonstrated to compare partial closest points with local pattern with ICP, manual labeling, and Gaussian fields criterion. The alignment effect of variable expressions and illumination is also tested. Besides, the wearing glasses problem is well solved by glasses replacement.

An analytical method for the adaptive computation of threshold of gradient modulus in 2D anisotropic diffusion filter

In spite of the extensive application of Anisotropic Diffusion (AD) filter in software packages for medical image analysis, denoising and edge preservation offered by it depends exclusively on the selection of the value of Threshold of Gradient Modulus (TGM). Tuning the TGM to its optimum value through trial and error is subjective and tiring. An analytical model to compute the optimum value of TGM adaptively from the mean gradient of the image itself is proposed in this article. The qualitative examination of the gradient and true edge maps of the original and restored Magnetic Resonance images revealed that analytically computed TGM ensures best trade-off between noise suppression and edge preservation.

Color-Guided Depth Map Super Resolution Using Convolutional Neural Network

With the development of 3-D applications, such as 3-D reconstruction and object recognition, accurate and high-quality depth map is urgently required. Recently, depth cameras have been affordable and widely used in daily life. However, the captured depth map always owns low resolution and poor quality, which limits its practical application. This paper proposes a color-guided depth map super resolution method using convolutional neural network. First, a dual-stream convolutional neural network, which integrates the color and depth information simultaneously, is proposed for depth map super resolution. Then, the optimized edge map generated by the high resolution color image and low resolution depth map is used as additional information to refine the object boundary in the depth map. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.

Denoising of PET Images using NSCT and Quasi-Robust Potentials

In this paper we present an algorithm for the denoising of small animal positron emission images. The proposed algorithm combines a multiresolution transform with robust filtering of regions. The image is processed in the non-subsampled contourlet domain, taking advantage of the transform ability to capture geometric information of important structures like small lesions and borders between tissues. Additionally, in the transform domain, we proposed to apply quasi‑ robust potentials in order to reduce the noise on regions without borders, this is done by estimating an edge map and a set of image regions. Finally the inverse contourlet transform is applied to obtain a denoised image. Quality tests using the NEMA NU4 2008 phantom show that the proposed method reduces the noise in the image while at the same time the average count is preserved on each region. Comparisons with other methods, using a contrast analysis on a simulated lesion show the superiority of our approach to denoise and preserve small structures such as lesions.

Spatially Adaptive Tensor Total Variation-Tikhonov Model for Depth Image Super Resolution

Depth images play an important role in 3-D applications. However, due to the limitation of depth acquisition equipment, the acquired depth images are usually in limited resolution. In this paper, a spatially adaptive tensor total variation-Tikhonov model is proposed to solve this problem. The tensor total variation regularization is adopted to maintain sharp edges that reflect latent discontinuities in the real world, while the Tikhonov regularization ensures that depth changes smoothly inside objects. Furthermore, a fused edge map is proposed to indicate edge regions and balance both regularization terms. In edge regions, tensor total variation regularization is predominant, thus edge blurring artifacts are suppressed. In non-edge regions, Tikhonov regularization plays a more important role to suppress staircasing artifacts. Specifically, texture edges are removed in the fused edge map, and texture copying artifacts are avoided. Experimental results demonstrate the effectiveness and superiority of the proposed framework. Moreover, the proposed method yields much sharper edges and a lower percentage of bad pixels.

Photo-to-Sketch Transformation in a Complex Background

We investigate the problem of sketch generation for sketch-based image retrieval (SBIR). Solving this problem is important to obtain better retrieval results, because a powerful feature extraction algorithm is inefficient. Transforming photos from raw pixels into pseudo-sketches closes the gap between these two domains and plays a significant role in SBIR. This problem is relatively challenging because of: 1) the complexity of the image background and 2) the complexity of the resulting edge map. In this paper, we develop a system to generate pseudo-sketches from photos. Saliency detection is used to extract the major objects from a photo. Then, a Gabor filter is designed to further capture the “real-major” object. Finally, the Sobel operator is used to obtain the final pseudo-sketch. Experiments are conducted using the Flickr15k data set. The results show that the obtained pseudo-sketches are reasonable and that the SBIR process produces the state-of-the-art results in certain categories.

Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Content-based video retrieval system aims at assisting a user to retrieve targeted video sequence in a large database. Most of the search engines use textual annotations to retrieve videos. These types of engines offer a low-level abstraction while the user seeks high-level semantics. Bridging this type of semantic gap in video retrieval remains an important challenge. In this paper, colour, texture and shapes are considered to be low-level features and motion is a high-level feature. Colour histograms convert the RGB colour space into YcbCr and extract hue and saturation values from frames. After colour extraction, filter mask is applied and gradient value is computed. Gradient and threshold values are compared to draw the edge map. Edges are smoothed for sharpening to remove the unnecessary connected components. These diverse shapes are then extracted and stored in shape feature vectors. Finally, an SVM classifier is used for classification of low-level features. For high-level features, depth images are extracted for motion feature identification and classification is done via echo state neural networks (ESN). ESN are a supervised learning technique and follow the principle of recurrent neural networks. ESN are well known for time series classification and also proved their effective performance in gesture detection. By combining the existing algorithms, a high-performance multimedia event detection system is constructed. The effectiveness and efficiency of proposed event detection mechanism is validated using MSR 3D action pair dataset. Experimental results show that the detection accuracy of proposed combination is better than those of other algorithms

Edge map analysis in chest X-rays for automatic pulmonary abnormality screening

Edge map analysis in chest X-rays for automatic pulmonary abnormality screening

A New Bayesian Edge-Linking Algorithm Using Single-Target Tracking Techniques

This paper proposes novel edge-linking algorithms capable of producing a set of edge segments from a binary edge map generated by a conventional edge-detection algorithm. These proposed algorithms transform the conventional edge-linking problem into a single-target tracking problem, which is a well-known problem in object tracking. The conversion of the problem enables us to apply sophisticated Bayesian inference to connect the edge points. We test our proposed approaches on real images that are corrupted with noise.

MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images.

This paper addresses the problem of horizon detection, a fundamental process in numerous object detection algorithms, in a maritime environment. The maritime environment is characterized by the absence of fixed features, the presence of numerous linear features in dynamically changing objects and background and constantly varying illumination, rendering the typically simple problem of detecting the horizon a challenging one. We present a novel method called multi-scale consistence of weighted edge Radon transform, abbreviated as MuSCoWERT. It detects the long linear features consistent over multiple scales using multi-scale median filtering of the image followed by Radon transform on a weighted edge map and computing the histogram of the detected linear features. We show that MuSCoWERT has excellent performance, better than seven other contemporary methods, for 84 challenging maritime videos, containing over 33,000 frames, and captured using visible range and near-infrared range sensors mounted onboard, onshore, or on floating buoys. It has a median error of about 2 pixels (less than 0.2%) from the center of the actual horizon and a median angular error of less than 0.4 deg. We are also sharing a new challenging horizon detection dataset of 65 videos of visible, infrared cameras for onshore and onboard ship camera placement.

Neighborhood geometry based feature matching for geostationary satellite remote sensing image

In this paper, we focus on Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database registration for remote sensing images taken from geostationary meteorological satellites. While the accuracy of feature matching is the key component. To improve it, we propose a neighborhood geometry-based feature matching scheme which includes three steps: neighborhood coding, verification and fitting. (1) Neighborhood coding represents landmarks of GSHHG as a descriptive bit-matrix, and quantifies remote sensing images to a probability-based edge map and a binary geometry-based edge map. As a result, both gradient and geometry similarity of local features in the remote sensing image and GSHHG can be measured. (2) Neighborhood verification is to encode spatial relationship among local features in neighbor, and discover outliers. (3) Neighborhood fitting fits the shorelines of GSHHG with the landmarks registered by neighborhood verification to improve recall. Experimental results on 25 pairs of newly annotated images show that the proposed method is competitive to several prior arts with respect to matching accuracy. What is more, our method is significantly more efficient than others.

A New Reference-Based Edge Map Quality Measure

Edge detection is an important task in image processing, and the quality of further processing is often reflected by the quality of edge detector outputs. Therefore, it is necessary to develop effective edge map quality measures to assist in evaluating the performance of edge detectors. Objective evaluation measures are crucial in automatically determining the optimal edge map for a given image or an application, as well as its parameter values. In this paper, a new reference-based edge measure (RBEM) is introduced to evaluate the performance of edge detector outputs relative to a ground truth. The new measure fuses four component metrics, based on edge pixel presence, edge corner localization, thick edge occurrence, and edge connectivity. Each of these metrics can be used separately or as a standalone measure to evaluate the quality of an edge map in terms of specific characteristics. The effectiveness of the proposed measure is demonstrated for selecting the best edge detector among several edge detectors, as well as for selecting the optimal parameter values, for both synthetic images and natural images. Experimental results show that the presented RBEM outperforms the existing methods according to subjective evaluation mean opinion scores, as it considers more important visual features in its evaluation.

An object localization optimization technique in medical images using plant growth simulation algorithm.

The analysis of leukocyte images has drawn interest from fields of both medicine and computer vision for quite some time where different techniques have been applied to automate the process of manual analysis and classification of such images. Manual analysis of blood samples to identify leukocytes is time-consuming and susceptible to error due to the different morphological features of the cells. In this article, the nature-inspired plant growth simulation algorithm has been applied to optimize the image processing technique of object localization of medical images of leukocytes. This paper presents a random bionic algorithm for the automated detection of white blood cells embedded in cluttered smear and stained images of blood samples that uses a fitness function that matches the resemblances of the generated candidate solution to an actual leukocyte. The set of candidate solutions evolves via successive iterations as the proposed algorithm proceeds, guaranteeing their fit with the actual leukocytes outlined in the edge map of the image. The higher precision and sensitivity of the proposed scheme from the existing methods is validated with the experimental results of blood cell images. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run time of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.

Medical image encryption using edge maps

This paper presents a medical image encryption algorithm using edge maps derived from a source image. The algorithm is composed by three parts: bit-plane decomposition, generator of random sequence, and permutation. It offers users the following flexibilities: (1) any type of images can be used as the source image; (2) different edge maps can be generated by various edge detectors and thresholds; (3) selection of appropriate bit-plane decomposition method is flexible; (4) many permutation methods can be cascaded with the proposed algorithm. A significantly large key space and strong key sensitive are possessed by the proposed algorithm to protect different types of medical images. Furthermore, it has a wider applicability than other methods for fuzzy edge maps. Experiments and security analysis further demonstrate that it has a strong resistance against various security attacks and outperforms other state-of-the-art methods.