Local Edge Patterns Research Articles

Are Recent SISR Techniques Suitable for Industrial Applications at Low Magnification?

In recent years, many deep-network-based super-resolution techniques have been proposed and have achieved impressive results for 2 ${\times }$ and higher magnification factors. However, lower magnification factors encountered in some industrial applications have not received special attention, such as 720P-to-1080P (1.5 ${\times }$ magnification). Compared to traditional 2 ${\times }$ or higher magnification factors, these lower magnifications are much simpler, but reconstructions of high-definition images are time-consuming and computationally complex. Hence, in this paper, a fast image upsampling method is designed specifically for industrial applications at low magnification. In the proposed method, edge and nonedge areas are first distinguished and then reconstructed via different fast approaches. For the edge area, a local edge pattern encoding-based method is presented to recover sharp edges. For the nonedge area, a global iterative reconstruction with texture constraint is utilized. Moreover, some acceleration strategies are also presented to further reduce the complexity. The experimental results demonstrate that the proposed method can obtain performance comparable to that of some state-of-the-art methods for 720P-to-1080P magnification, but the computational cost is much lower.

Common Green Plants Recognition Based on Wavelet Transformation and Varied Local Edge Patterns

Green plant species identification plays an important role in so many aspects, such as ecological environment protection, Chinese medicine preparation, agricultural and horticultural application, etc. A method on green plants recognition based on wavelet transform and variable local edge patterns (VLEP) is proposed in this paper. Firstly, the original image is decomposed by wavelet transformation. Then texture features are extracted using VLEPs. At the same time, block-based and multi-resolution ideas are considered together to extract features after images are transformed by wavelet. Finally, the fused texture features are classified by the nearest neighbor method. The experimental results show that the proposed method is a promising method for recognizing the common green plants with the natural and complex background compared with the other state-of-the-art methods, and combination of block-based and multi-resolution ideas can further improve the accuracy rate effectively.

Using Local Edge Pattern Descriptors for Edge Detection

Edge detection is an active and critical topic in the field of image processing, and plays a vital role for some important applications such as image segmentation, pattern classification, object tracking, etc. In this paper, an edge detection approach is proposed using local edge pattern descriptor which possesses multiscale and multiresolution property, and is named varied local edge pattern (VLEP) descriptor. This method contains the following steps: firstly, Gaussian filter is used to smooth the original image. Secondly, the edge strength values, which are used to calculate the edge gradient values and can be obtained by one or more groups of VLEPs. Then, weighted fusion idea is considered when multiple groups of VLEP descriptors are used. Finally, the appropriate threshold is set to perform binarization processing on the gradient version of the image. Experimental results show that the proposed edge detection method achieved better performance than other state-of-the-art edge detection methods.

Applying Wavelet Transformation and Varied Local Edge Patterns to Plants Classification

A method on green plants recognition based on wavelet transform and varied local edge patterns (VLEP) is proposed in this paper. Firstly the original image is decomposed by wavelet transformation. Then texture features are extracted using VLEPs. At the same time, block-based and multi-resolution ideas are considered together to extract features after images are transformed by wavelet. Finally, the fused texture features are classified by the nearest neighbor method. The experimental results show that the proposed method is a promising method for recognizing the green plants. And combination of block-based and multi-resolution ideas can further improve the accuracy effectively.

A varied local edge pattern descriptor and its application to texture classification

Similar images can be classified by the aid of texture cues among which edge is widely considered as one of the most valuable features. In this paper, we firstly proposed a flexible edge descriptor, called varied local edge pattern (VLEP). Then we apply VLEP to similar texture classification. The proposed VLEP descriptor has multi-scale, multi-direction (or multi-resolution) properties. Because VLEP uses histogram spectrum to describe image information, it is very easy to fuse local binary pattern (LBP) and Zernike moments histogram spectrum features due to their excellent properties and supplementary roles. The fused histogram spectrum features representing the images are classified via the nearest neighbor classifier. Experimental results show that the VLEP-based method can be remarkably superior to other state-of-the-art texture classification methods on the large and comprehensive CUReT and Outex texture database.

Directional local ternary patterns for multimedia image indexing and retrieval

In this paper, we proposed a novel multimedia image indexing and retrieval algorithm using Directional Local Ternary Pattern (DLTP) for Content-Based Image Retrieval (CBIR). The standard Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) encode the relationship between reference pixel and its surrounding neighbours by computing grey-level difference. The proposed method extracts the directional edge information based on local ternary value (−1, 0, 1) instead of binary value (0, 1) in 0°, 45°, 90°, and 135° directions of an image. The performance of the proposed method is compared with local binary pattern LBP, Block-based LBP (BLK-LBP), Centre-Symmetric LBP (CS-LBP), Local Edge Patterns for Segmentation (LEPSEG), Local Edge Patterns for Image Retrieval (LEPINV) and other existing transform domain methods based on Corel-5000 (DB1) and Corel-10000 (DB2) databases. The retrieval performance of DLTP shows better improvement in terms of their performance measures as compared with the standard LBP.

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.

Directional local extrema patterns: a new descriptor for content based image retrieval

In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner that it extracts the directional edge information based on local extrema in 0 $$^{\circ }$$ , 45 $$^{\circ }$$ , 90 $$^{\circ }$$ , and 135 $$^{\circ }$$ directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.

Image enlargement with high-frequency component augmentation based on predefined codebook describing edge blurring properties

Image super-resolution as high-quality image enlargement is achieved by some type of restoration for high-frequency components that deteriorate through the image enlargement. The estimation methods using the given image itself are effective for the restoration, and we have proposed a method employing the codebook describing edge blurring properties that are derived from the given image. It is, however, unfavourable to apply those image-dependent methods to movies whose scene varies momentarily. In this paper, an image-independent codebook incorporating local edge patterns of images is proposed, and then the predefined codebook is applied. The effectiveness is shown through some experiments.

Iris recognition using local texture analysis

With the increasing needs in security systems, iris recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for iris recognition by analyzing iris patterns. To improve the rate of recognition, we divide the normalized iris image into several regions to keep the iris image away from several noise factors, such as eyelids, eyelashes, and motion blur. For feature extraction, the local edge pattern (LEP) operator is designed to capture local characteristics of the iris image to produce discriminating texture features in every region. A resulting 2D feature vector is mapped into a low-dimensional subspace using two dimension linear discriminant analysis (2DLDA), and then the minimum distance classifier (MDC) is adopted for recognition. Experiments on the public and freely available iris images taken from the CASIA (Institute of Automation, Chinese Academy of Sciences) and UBIRIS databases confirm the advantage of the proposed approach in terms of speed and accuracy.

Image classification using color, texture and regions

A new classification method using color, texture and regions is proposed in this study. Image-based features related to color and local edge patterns are used to prune irrelevant database images for each query image. The proposed region matching is then applied to find the match to the query image from among the set of candidate images in the database. The dissimilarity of each pair of images can be calculated on the basis of the matching results. Finally, all the database images in the candidate set can be sorted by ascending dissimilarity values. To achieve the classification goal, the k-NN rule is used to assign a class label to the query image. Note that the main contribution of this paper is to select proper features for representing color, texture and region, which, in turn, are used to achieve effective classification results. More important, all features used in the proposed method, no matter color or texture, are presented in the simple form of histogram, yet leading to effective results. Even in the stage of region matching, color and texture features in histograms are also used to obtain homogeneous regions and to measure dissimilarity. In addition, the proposed classification method can be applied to all kinds of color image databases rather than specific databases. The number of classes can be as versatile as required by the application. The effectiveness and practicability of the proposed method has been demonstrated by various experiments.

Retrieval of translated, rotated and scaled color textures

A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply analyzing only color characteristics. First, the distributions of color and local edge patterns are used to derive a similarity measure for a pair of textures. Then, a retrieval method based on the similarity measure is proposed to retrieve texture images from a database of color textures. Finally, the similarity measure is extended to retrieve texture regions from a database of natural images. Since the proposed feature distributions can resist variations in translation, rotation and scale, our method has the ability to retrieve texture images or regions that change in translation, rotation and/or scale. The effectiveness and practicability of the proposed method have been demonstrated by various experiments.

Color texture segmentation using feature distributions

A new method for color textured segmentation using feature distributions is proposed in this study. First, the distributions of color and local edge patterns are used to derive a homogeneity measure for color textured regions. Then, a coarse-to-fine method based on the homogeneity measure is employed to achieve the goal of color texture segmentation. The proposed method unifies color and edge features to solve the color texture segmentation problem rather than simply extend gray-level texture analysis to color images, or analyze only spatial interaction of colors in a neighborhood. In addition, the proposed method is simple but effective. The execution time is fast, the error rate for collages of real texture is low and the segmentation results for natural scenes are visually satisfactory. Finally, no a priori knowledge about the number and types of textures or the number of regions are required in the proposed method. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments.

Hierarchical artificial neural networks for edge enhancement

Hierarchical artificial neural networks are designed to enhance edge measurement. The neural network comprises three subnets which are connected in a hierarchical manner. The edge Contour Detection subnet determines the orientation of the most probable edge contour (if one exists) in the local edge pattern, the Gradient Adjustment subnet makes a decision to alter the gradient magnitude, and the Orientation Determination subnet adjusts the direction of the edge element of concern to be in agreement with the local surroundings. In order for the neural network system to perform correctly and accurately, each of these subnets must acquire suitable weights by learning. In our learning algorithm, a modified generalized delta rule incorporates a momentum term to achieve fast learning without oscillation. The neural network is simulated on a MIPS M120-S machine running UNIX. Test images are degraded by random noise up to 30% of the original images. The results are very promising: true edges are detected and enhanced, false edges are suppressed, noise is eliminated, and missing edge elements are interpolated.

Improving edge measurement on noisy images by hierarchical neural networks

To improve edge operator measurement, a hierarchical neural network is proposed in this paper. The neural network is designed to adjust the edge measurement based on the neighboring edges. The overall strategy is to analyze the local edge patterns to reveal and reinforce the curvilinear edge structure while suppressing unwanted random noise. The hierarchical network consists of three levels. At the first level, eight selective functional-link nets associated with eight edge directions are working in parallel. Each of the nets determines the potential adjustment on the edge of concern according to the selected processes and the input local edge pattern. The second level is a Maximum Detection Subnet which makes a decision to alter the gradient magnitude and the direction of the edge element of concern in agreement with the local surrounding. The last level computes the adjustment for the edge measurement. Using the neural network for each edge element, highly parallel processing can be achieved. To apply global analysis, an iterative approach is incorporated with the neural network. As a result, the final edge measurement is more accurate.