We present an image edge detection algorithm that is based on the concept of ordered directionally monotone functions, which permit our proposal to consider the direction of the edges at each pixel and perform accordi...
详细信息
ISBN:
(纸本)9783319668277;9783319668260
We present an image edge detection algorithm that is based on the concept of ordered directionally monotone functions, which permit our proposal to consider the direction of the edges at each pixel and perform accordingly. the results of this method are presented to the EUSFLAT 2017 Competition on Edge Detection.
this article presents an analysis of methods used for circle detection in an image along with a description of the newly proposed method of circle detection in a pre-processed image based on trigonometric functions so...
详细信息
this article presents an analysis of methods used for circle detection in an image along with a description of the newly proposed method of circle detection in a pre-processed image based on trigonometric functions so that the algorithm itself is feasible for low-power digital measuring systems. the method is based on the use of significant points found in the object that are connected with a line for which directions are calculated using the algorithm. From the information thus obtained, the geometric centre of the object can be determined using the algorithm. the developed method is compared withthe classical method of detecting circles in an image using Hough transforms. Finally, the test results are stated in the article. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Automatic road extraction from High Resolution Remote Sensing image is a challenging problem. In this paper we present a new approach for road automatically extraction which is based on topological derivative and math...
详细信息
ISBN:
(数字)9781510617261
ISBN:
(纸本)9781510617261;9781510617254
Automatic road extraction from High Resolution Remote Sensing image is a challenging problem. In this paper we present a new approach for road automatically extraction which is based on topological derivative and mathematical morphology. this approach for road extraction can be divided into three main steps: using topological derivative for image segmentation, using mathematical morphology for road network identification and filtering. the experimental results show that this approach can effectively extract roads from high-resolution remote sensing image.
this paper analyzes the efficiency of the Scale Invariant Feature Transform (SIFT) measured in terms of execution time and stability of detection for keypoint location. Our goal is to investigate the influence of vari...
详细信息
ISBN:
(纸本)9783319668246;9783319668239
this paper analyzes the efficiency of the Scale Invariant Feature Transform (SIFT) measured in terms of execution time and stability of detection for keypoint location. Our goal is to investigate the influence of various downscaling methods which pre-process images before the SIFT is executed. For every downscaling method, we estimate the total execution time, the amount of detected keypoints and the success rate of keypoint matching. We show that the best combination withthe SIFT is achieved by the F-transform downscaling algorithm.
the automatic detection of video action is still a challenging research task. In this paper, we consider a first atomic approach and its empirical evaluation to classify a single action in a short video sequence based...
详细信息
ISBN:
(纸本)9781538693858
the automatic detection of video action is still a challenging research task. In this paper, we consider a first atomic approach and its empirical evaluation to classify a single action in a short video sequence based on DITEC image characterization method. the presented method combines four different concepts: global image descriptors, image transformation algorithms, Machine Learning paradigms for supervised classification and Feature Subset Selection (FSS) techniques. Using DITEC descriptors, which are based on the Trace Transform, the information contained in a video is handled as an image. this allows us to apply imageprocessing solutions for the analysis of the video, more concretely, of the occurring action. Key features are extracted to nourish Machine Learning classifiers in order to predict the action. the final step is to use a Feature Subset Selection (FSS) standard method to select the most accurate attributes for the identification of the action. the idea of understanding videos as images widens the possibilities for the analysis of temporal behaviour of actions within a video.
In algorithms for image segmentation, distance functions represent a criterion based on which the pixels can be divided into groups of segments. Determination of the segmentation criterion includes several factors. th...
详细信息
In algorithms for image segmentation, distance functions represent a criterion based on which the pixels can be divided into groups of segments. Determination of the segmentation criterion includes several factors. the application of the aggregation operator enables the adjustment of the segmentation criteria according to the intuitively defined criterion. Depending on the characteristics of the applied aggregation operator and the distance functions as basic factors relevant for segmentation, the new distance functions with specified features are obtained. In this article one method for constructing distance functions using ordered weighted averaging aggregation operator is proposed. the application of the constructed functions is illustrated by the segmentation of the imagethrough the "Fuzzy c-means clustering" algorithm for segmentation.
Geo-referenced textual data has been the subject of multiple investigations, by providing opportunities to better understand certain phenomena according to the content that is shared, either on-line such as social net...
详细信息
ISBN:
(数字)9783319900537
ISBN:
(纸本)9783319900537;9783319900520
Geo-referenced textual data has been the subject of multiple investigations, by providing opportunities to better understand certain phenomena according to the content that is shared, either on-line such as social networks, blogs, and news;or through repositories such as scientific research articles, geo-referenced virtual books, among others. However, the characteristics of this information are studied, analyzed and processed separately, either through its textual components or its geo-spatial components, which offers a separate understanding of the results. In this paper, we propose an integration of textual and geo-spatial components from the pre-processing phase to the visualization stage, As a part of the Document Mapping process based on the phases of the Knowledge Discovery in Databases (KDD). Achieving two main results (1) minimize the problems that arise in the visual phase, such as data occlusion and (2) provide a more detailed understanding between the textual relationships of the data when plotted in a geo-spatial map.
To increase the efficiency of the laser coagulation surgery the problem of the most accurate segmentation of fundus images is especially relevant. Fundus image segmentation is carried out with high accuracy using effe...
详细信息
Lossy image compression algorithms like JPEG usually introduce visually annoying artifacts on decoded images, such as blocking artifacts, blurring and ringing effects. the tiny portable graphics (TPG) based image/vide...
详细信息
ISBN:
(纸本)9783030027384;9783030027377
Lossy image compression algorithms like JPEG usually introduce visually annoying artifacts on decoded images, such as blocking artifacts, blurring and ringing effects. the tiny portable graphics (TPG) based image/video compression technique is proposed to improve JPEG compression performance. However, the lossy compression artifacts cannot be fully removed, especially at low coding bit-rates. Recently, some shallow convolutional neural network (CNN) models have been proposed as post-processing techniques to reduce compression artifacts. Learning from the fact that deep CNNs have shown extraordinary ability in high-level vision problems, we propose to investigate how a deeper CNN can further enhance the quality of decoded images. Specifically, we adopt a network with16 residual blocks. In order to increase the receptive field, we change the first convolution layer in the first five residual blocks to dilated convolution with size 2. the primary experimental results show that the proposed model can outperform existing CNN based post-processing methods.
Generally, the improvement in resolution will lead to larger data volume and higher data dimension for Hyperspectral image, which raise a higher requirement for previous imageprocessingalgorithms. In this paper, a n...
详细信息
ISBN:
(纸本)9781538671504
Generally, the improvement in resolution will lead to larger data volume and higher data dimension for Hyperspectral image, which raise a higher requirement for previous imageprocessingalgorithms. In this paper, a novel coupled spectral-spatial tensor representation framework (CSSTR) is proposed for denoising of hyperspectral images. Specifically, the proposed method is applied to describe the spectral-spatial features which decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a matrix and a vector. Owing to the spatial-spectral constraint fed back from the tensor representation method, CSSTR can capture the structural correlations and inherent feature information of data. Finally, several experiments were conducted to illustrate the advantage of the proposed algorithm.
暂无评论