Tensor voting for robust color edge detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-94-007-7584-8_9This chapter proposes two robust color edge detection methods based on tensor voting. The first method is a direct adaptation of the classical tensor voting to color images where tensors are initialized with either the gradient or the local color structure tensor. The second method is based on an extension of tensor voting in which the encoding and voting processes are specifically tailored to robust edge detection in color images. In this case, three tensors are used to encode local CIELAB color channels and edginess, while the voting process propagates both color and edginess by applying perception-based rules. Unlike the classical tensor voting, the second method considers the context in the voting process. Recall, discriminability, precision, false alarm rejection and robustness measurements with respect to three different ground-truths have been used to compare the proposed methods with the state-of-the-art. Experimental results show that the proposed methods are competitive, especially in robustness. Moreover, these experiments evidence the difficulty of proposing an edge detector with a perfect performance with respect to all features and fields of application.This research has been supported by the Swedish Research Council under the project VR 2012-3512

Analysis of MVD and color edge detection for depth maps enhacement

Prjecte final de carrera realitzat en col.laboració amb Fraunhofer Heinrich Hertz InstituteMVD (Multiview Video plus Depth) data consists of two components: color video and depth maps sequences. Depth maps represent the spatial arrangement (or three dimensional geometry) of the scene. The MVD representation is used for rendering virtual views in FVV (Free Viewpoint Video) and for 3DTV (3-dimensional TeleVision) applications. Distortions of the silhouettes of objects in the depth maps are a problem when rendering a stereo video pair. This Master thesis presents a system to improve the depth component of MVD . For this purpose, it introduces a new method called correlation histograms for analyzing the two components of depth-enhanced 3D video representations with special emphasis on the improved depth component. This document gives a description of this new method and presents an analysis of six di erent MVD data sets with di erent features. Moreover, a modular and exible system for improving depth maps is introduced. The idea behind is to use the color video component for extracting edges of the scene and to re-shape the depth component according to the edge information. The mentioned system basically describes a framework. Hence, it is capable to admit changes on speci c tasks if the concrete target is respected. After the improvement process, the MVD data is analyzed again via correlation histograms in order to obtain characteristics of the depth improvement. The achieved results show that correlation histograms are a good method for analyzing the impact of processing MVD data. It is also con rmed that the presented system is modular and exible, as it works with three di erent degrees of change, introducing modi cations in depth maps, according to the input characteristics. Hence, this system can be used as a framework for depth map improvement. The results show that contours with 1-pixel width jittering in depth maps have been correctly re-shaped. Additionally, constant background and foreground areas of depth maps have also been improved according to the degree of change, attaining better results in terms of temporal consistency. However, future work can focus on unresolved problems, such as jittering with more than one pixel width or by making the system more dynamic

Representations and transformations of color spaces via quaternions

Tato práce se zabývá využitím kvaternionů pro detekci hran obrazu. Zaměřuje se především na práci s obrazem reprezentovaným v různých barevných prostorech. Nejprve jsou uvedeny základní pojmy a vlastnosti algebry kvaternionů. Dále jsou popsány některé využívané barevné prostory a poté jsou uvedeny základní filtry sloužící pro detekci hran obrazu v odstínech šedi. Následně jsou představeny barevné filtry využívající kvaterniony pro detekci hran obrazu v barevném prostoru RGB. Na závěr se práce zabývá použitím těchto filtrů pro detekci hran obrazu v prostoru HSV.This thesis deals with quaternions for edge detection, particularly on image processing represented in various color spaces. First, the key concepts and the quaternion algebra properties are mentioned, then some of the commonly used color spaces are described and afterwards the thesis dedicates to the basic filters for edge detection in grayscale image. After that there are presented the color filters that use quaternions for color edge detection in RGB color space. Towards the end, these filters are used for color edge detection in HSV color space.

A new approach to Color Edge Detection

Most edge detection algorithms deal only with grayscale images, and the way of adapting them to use with RGB images is an open problem. In this work, we explore different ways of aggregating the color information of a RGB image for edges extraction, and this is made by means of well-known edge detection algorithms. In this research, it is been used the set of images from Berkeley. In order to evaluate the algorithm’s performance, F measure is computed. The way that color information -the different channels- is aggregated is proved to be relevant for the edge detection task. Moreover, post-aggregation of channels performed significatively better than the classic approach (pre-aggregation of channels)

The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow

This paper introduces the idea of the color Clifford Hardy signal, which can be used to process color images. As a complex analytic function's high-dimensional analogue, the color Clifford Hardy signal inherits many desirable qualities of analyticity. A crucial tool for getting the color and structural data is the local feature representation of a color image in the color Clifford Hardy signal. By looking at the extended Cauchy-Riemann equations in the high-dimensional space, it is possible to see the connection between the different parts of the color Clifford Hardy signal. Based on the distinctive and important local amplitude and local phase generated by the color Clifford Hardy signal, we propose five methods to identify the edges of color images with relation to a certain color. To prove the superiority of the offered methodologies, numerous comparative studies employing image quality assessment criteria are used. Specifically by using the multi-scale structure of the color Clifford Hardy signal, the proposed approaches are resistant to a variety of noises. In addition, a color optical flow detection method with anti-noise ability is provided as an example of application.Comment: 13 page

New Aggregation Approaches with HSV to Color Edge Detection

The majority of edge detection algorithms only deal with grayscale images, while their use with color images remains an open problem. This paper explores different approaches to aggregate color information of RGB and HSV images for edge extraction purposes through the usage of the Sobel operator and Canny algorithm. This paper makes use of Berkeley’s image data set, and to evaluate the performance of the different aggregations, the F-measure is computed. Higher potential of aggregations with HSV channels than with RGB channels is found. This article also shows that depending on the type of image used, RGB or HSV, some methods are more appropriate than others

Analysis of MVD and color edge detection for depth maps enhacement

Prjecte final de carrera realitzat en col.laboració amb Fraunhofer Heinrich Hertz InstituteMVD (Multiview Video plus Depth) data consists of two components: color video and depth maps sequences. Depth maps represent the spatial arrangement (or three dimensional geometry) of the scene. The MVD representation is used for rendering virtual views in FVV (Free Viewpoint Video) and for 3DTV (3-dimensional TeleVision) applications. Distortions of the silhouettes of objects in the depth maps are a problem when rendering a stereo video pair. This Master thesis presents a system to improve the depth component of MVD . For this purpose, it introduces a new method called correlation histograms for analyzing the two components of depth-enhanced 3D video representations with special emphasis on the improved depth component. This document gives a description of this new method and presents an analysis of six di erent MVD data sets with di erent features. Moreover, a modular and exible system for improving depth maps is introduced. The idea behind is to use the color video component for extracting edges of the scene and to re-shape the depth component according to the edge information. The mentioned system basically describes a framework. Hence, it is capable to admit changes on speci c tasks if the concrete target is respected. After the improvement process, the MVD data is analyzed again via correlation histograms in order to obtain characteristics of the depth improvement. The achieved results show that correlation histograms are a good method for analyzing the impact of processing MVD data. It is also con rmed that the presented system is modular and exible, as it works with three di erent degrees of change, introducing modi cations in depth maps, according to the input characteristics. Hence, this system can be used as a framework for depth map improvement. The results show that contours with 1-pixel width jittering in depth maps have been correctly re-shaped. Additionally, constant background and foreground areas of depth maps have also been improved according to the degree of change, attaining better results in terms of temporal consistency. However, future work can focus on unresolved problems, such as jittering with more than one pixel width or by making the system more dynamic

Edge Detection: A Collection of Pixel based Approach for Colored Images

The existing traditional edge detection algorithms process a single pixel on an image at a time, thereby calculating a value which shows the edge magnitude of the pixel and the edge orientation. Most of these existing algorithms convert the coloured images into gray scale before detection of edges. However, this process leads to inaccurate precision of recognized edges, thus producing false and broken edges in the image. This paper presents a profile modelling scheme for collection of pixels based on the step and ramp edges, with a view to reducing the false and broken edges present in the image. The collection of pixel scheme generated is used with the Vector Order Statistics to reduce the imprecision of recognized edges when converting from coloured to gray scale images. The Pratt Figure of Merit (PFOM) is used as a quantitative comparison between the existing traditional edge detection algorithm and the developed algorithm as a means of validation. The PFOM value obtained for the developed algorithm is 0.8480, which showed an improvement over the existing traditional edge detection algorithms.Comment: 5 Page