POINT-BASED MORPHOLOGICAL OPENING WITH INPUT DATA RETRIEVAL

Mathematical morphology is a technique recently applied directly for point cloud data. Its working principle is based on the removal and addition of points from an auxiliary point cloud that acts as a structuring element. However, in certain applications within a more complex process, these changes to the original data represent an unacceptable loss of information. The aim of this work is to provide a modification of the morphological opening to retain original points and attributes. The proposed amendment involved in the morphological opening: erosion followed by dilatation. In morphological erosion, the new eroded points are retained. In morphological dilation, the structuring element does not add its points directly, but uses the point positions to search through the previously eroded points and retrieve them for the dilated point cloud. The modification was tested on synthetic and real data, showing a correct performance at the morphological level, and preserving the precision of the original points and their attributes. Furthermore, the conservation is shown to be very relevant in two possible applications such as traffic sign segmentation and occluded edge detection.

SEGMENTATION OF TRAFFIC SIGNS FROM POLES WITH MATHEMATICAL MORPHOLOGY APPLIED TO POINT CLOUDS

Traffic signs are one of the most relevant road assets for driving, as the safety of drivers depends to a great extent on their correct location. In this paper two methods are compared for the segmentation of the sign and the pole supporting it. Both methods are based on the morphological opening to identify the sign points, the first one directly employs the mathematical morphology directly applied to point clouds and the second one through point cloud rasterization into images. The comparison was conducted on twenty real traffic signs acquired with Mobile Laser Scanning obtaining point clouds from environments with signposts, traffic lights and lampposts. The results showed a correct segmentation of the signs, obtaining a F-score of 0.81 by the point-based method and a 0.75 by 2D image method. In particular, the point-based mathematical morphology proved to be more accurate in the segmentation of traffic sings installed on traffic lights and lampposts, avoiding over detection shown by the 2D image method.

Image Restoration by Learning Morphological Opening-Closing Network

Mathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

Patch analysis based lung cancer classification

Lung Cancer may be a variety of Cancer that begins in the Lungs because of those that smokes often. However, there Area unit rare probabilities those area unit non-smokers get Affected because of unhealthy pollution and Harmful gasses. The detection of tumor is incredibly vital that helps to detect affected neoplasm areas in the lungs. Computed tomography help us to understand the cancer positions in patients. The detection of cancer tumours are performed by scanning the images of computed tomography. Lung cancer identification system goes with a method of Morphological opening and Gray level co-occurrence matrix (GLCM) feature extraction and Normalized cross-correlation with patches Analysis. Lung cancer classification using Linear Discriminant Analysis (LDA) gives good results of Accuracy of 81.81%. Patch Analysis is a new method to find lung cancer.

HAIR ARTIFACT REMOVAL AND SKIN LESION SEGMENTATION OF DERMOSCOPY IMAGES

Objective: The objective of this research is to perform automatic hair artifact removal and skin lesion segmentation on dermoscopy images.Methods: Dermoscopy images are images from the examination of the skin lesion using a dermatoscope. There are different types of skin lesion artifacts, structures, or objects that are present in dermoscopy images. This is a pertinent problem that can inhibit the proper examination and accurately segment the skin lesion from the surrounding skin area. Artifacts, such as hair strands, introduce additional features that can also cause problems during classification. Our process starts with hair removal using a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels. The detected hair regions are then filled up using harmonic inpainting. Then, skin lesion segmentation is performed using a binary conversion, a dilation, a perimeter detection and morphological opening, and then the removal of small connected pixels.Results: Experiments were carried out on the PH2 dermoscopy images. The border of the lesion was quantified for evaluation by four statistical metrics with the lesions identified by the PH2 as the reference image, resulting with a true detection rate (TDR) of 82.31 and a false detection rate of 5.69.Conclusions: The results obtained in the research work on hair artifacts removal and skin lesion segmentation provides acceptable results in terms of TDR and low false-positive rates.

Study on Underwater Sea Cucumber Rapid Locating Based on Morphological Opening Reconstruction and Max-Entropy Threshold Algorithm

In China, sea cucumber cultivation is developing rapidly, but sea cucumber catching still relies on inefficient manual work. Nowadays, the acquisition of underwater sea cucumber images and locating of sea cucumber target have provided technical support for underwater sea cucumber catching robots. However, there are still some problems to be solved, such as the degradation, edge blur and low contrast of underwater sea cucumber images due to the uneven light underwater and the absorption and scattering of light by water; and the cusp noises in the sea cucumber images produced by shells, gravers, planktons and other things in natural environment. Aiming at these problems, the underwater sea cucumber rapid locating method based on morphological opening reconstruction and max-entropy threshold algorithm (OR-META) is proposed in this paper. Firstly, the morphological opening reconstruction is adopted to smooth the original sea cucumber gray image; next, the max-entropy threshold segmentation algorithm is employed to segment the smoothed sea cucumber image, and the sea cucumber region in binary image is recognized according to area characteristics; finally, the position of sea cucumber region in the image is determined utilizing its centroid. In order to obtain the best result of sea cucumber image segmentation, three typical image segmentation algorithms OR-2DMETA, OR-OTSU and OR-2DOTSU are selected to compare with the OR-META. It is observed that the OR-META is obviously superior to the other three algorithms in qualitatively analyzing the image segmentation quality and quantitatively calculating the running time. To further analyze the image segmentation quality and locating accuracy, the images segmented by the OR-META are compared with manually segmented images. The comparison shows that although the image segmentation accuracy of the OR-META is low, with average target segmentation correct rate of 68.99%, the position of sea cucumber target in segmentation is predictable, which means that it will be within the directly drawn sea cucumber region. This demonstrates that the locating accuracy of the method is high. In addition, the method also has good real-time performance. It can be concluded from the experiment that for a RGB image with resolution ratio of 1280[Formula: see text][Formula: see text][Formula: see text]720 pixels, the average centroid Euclidean distance error of the OR-META segmented image is only 52.67 pixels, and its average running time is 0.6 s, which are qualified for the requirements of the sea cucumber catching robots.

CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS

Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.