78,911 research outputs found

    Image processing by region extraction using a clustering approach based on color

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    This thesis describes an image segmentation technique based on watersheds, a clustering technique which does not use spatial information, but relies on multispectral images. These are captured using a monochrome camera and narrow-band filters; we call this color segmentation, although it does not use color in a physiological sense. A major part of the work is testing the method developed using different color images. Starting with a general discussion of image processing, the different techniques used in image segmentation are reviewed, and the application of mathematical morphology to image processing is discussed. The use of watersheds as a clustering technique in two- dimensional color space is discussed, and system performance illustrated. The method can be improved for industrial applications by using normalized color to eliminate the problem of shadows. These methods are extended to segment the image into regions recursively. Different types of color images including both man made color images, and natural color images have been used to illustrate performance. There is a brief discussion and a simple illustration showing how segmentation can be used in image compression, and of the application of pyramidal data structures in clustering for coarse segmentation. The thesis concludes with an investigation of the methods which can be used to improve these segmentation results. This includes edge extraction, texture extraction, and recursive merging

    Tracking Dynamic Features in Image Sequences.

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    This dissertation deals with detecting and tracking dynamic features in image sequences using digital image analysis algorithms. The tracking problem is complicated in oceanographic images due to the dynamic nature of the features. Specifically, the features of interest move, change size and shape. In the first part of the dissertation, the design and development of a new segmentation algorithm, Histogram-based Morphological Edge Detector (HMED), is presented. Mathematical morphology has been used in the past to develop efficient and robust edge detectors. But these morphological edge detectors do not extract weak gradient edge pixels, and they introduce spurious edge pixels. The primary reason for this is due to the fact that the morphological operations are defined in the domain of a pixel\u27s neighborhood. HMED defines new operations, namely H-dilation and H-erosion, which are defined in the domain of the histogram of the pixel\u27s neighborhood. The motivation for incorporating the histogram into the dilation and erosion is primarily due to the rich information content in the histogram compared to the one available in the pixel\u27s neighborhood. As a result, HMED extracts weak gradient pixels while suppressing the spurious edge pixels. An extensive comparison of all morphological edge detectors in the context of oceanographic digital images is also presented. In the second part of the dissertation, a new augmented region and edge segmentation technique for the interpretation of oceanographic features present in the AVHRR image is presented. The augmented technique uses a topography-based method that extracts topolographical labels such as concave, convex and flat pixels from the image. In this technique, first a bicubic polynomial is fitted to a pixel and its neighborhood, and topolographical label is assigned based on the first and second directional derivatives of the polynomial surface. Second, these labeled pixels are grouped and assembled into edges and regions. The augmented technique blends the edge and region information on a proximity based criterion to detect the features. A number of experimental results are also provided to show the significant improvement in tracking the features using the augmented technique over other previously designed techniques

    Automatic Image Segmentation by Dynamic Region Merging

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    This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

    Segmentation of Sedimentary Grain in Electron Microscopy Image

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    This paper describes a novel method developed for the segmentation of sedimentary grains in electron microscopy images. The algorithm utilizes the approach of region splitting and merging. In the splitting stage, the marker-based watershed segmentation is used. In the merging phase, the typical characteristics of grains in electron microscopy images are exploited for proposing special metrics, which are then used during the merging stage to obtain a correct grain segmentation. The metrics are based on the typical intensity changes on the grain borders and the compact shape of grains. The experimental part describes the optimal setting of parameter in the splitting stage and the overall results of the proposed algorithm tested on available database of grains. The results show that the proposed technique fulfills the requirements of its intended application

    DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels

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    In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 15 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings.Comment: 12 page
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