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IoT along with Image segmentation is applied in a variety of applications separately. This character of application in agriculture field is still existing and attained positive approaches in success, though the combinations of these tools are unreal. This paper illustrates a methodology for combining IoT along with image segmentation for resolving the ecological factor as well as man-made factor (pesticides/fertilizers). Here a variety of sensing devices which is used for measuring the vital environmental factors and growth of plant is captured and segmented using morphological segmentation to the leaf lattice which is evaluated with the help of histogram analysis.

We present a novel method, which by analogy with the term "mass-spectroscopy" can be called "quasistatic elasto-spectoscopy" based on quantitative compressional Optical Coherence Elastogrphy (C-OCE). The method relies on initial determining of characteristic stiffness ranges for various morphological components of the tissue by comparing the OCE-based stiffness maps and histology. Such pre-calibration allows one to perform morphological segmentation of OCE-images demonstrating excellent correlation with segmentation of conventional histological images, but can be performed using freshly-extracted samples and even in vivo.

with the development of the society, The demand for daily necessities continues to increase, At the same time, the production efficiency and quality requirements of the products are getting higher and higher. Aiming at this problem A feasible method for 3D printing physical surface defect detection based on OpenCV. Get the products you need quickly through reverse technology, Guaranteed efficient production. At the same time, using OpenCV-based surface defect inspection to ensure product quality. First, obtain a physical surface image through a color industrial CCD camera. Secondly, the histogram equalization method can enhance the contrast of the image and Image preprocessing. Finally, image morphological segmentation of dual-core defect recognition technology is used to detect the position of physical surface defects. This method can accurately detect 3D printing physical surface defects. Achieved the intended purpose.

Morphological segmentation is useful for processing Mongolian. In this paper, we manually build a morphological segmentation data set for Mongolian. We then present a character-based encoder-decoder model with attention mechanism to perform the morphological segmentation task. We further investigate the influence of analogy features extracted from scratch and improve the performance of our model using multi languages setting. Experimental results show that our encoder-decoder model with attention mechanism provides a strong baseline for Mongolian morphological segmentation. The analogy features provide useful information to the model and improve the performance of the system. The use of multi languages data set shows the capability of our model to acquire knowledge through different languages and delivers the best result.

In this paper, branching entropy techniques and isiXhosa language heuristics are adapted to develop unsupervised morphological segmenters for isiXhosa. An overview of isiXhosa segmentation issues is given, followed by a discussion on previous work in automated segmentation, and segmentation of isiXhosa in particular. Two unsupervised isiXhosa segmenters are presented and compared to a random minimum baseline and Morfessor-Baseline, a standard in unsupervised word segmentation. Morfessor-Baseline outperforms both isiXhosa segmenters at 79.10% boundary identification accuracy. The IsiXhosa Branching Entropy Segmenter (XBES) performance varies depending on the segmentation mode used, with a maximum of 73.39%. The IsiXhosa Heuristic Maximum Likelihood Segmenter (XHMLS) achieves 72.42%. The study suggests that unsupervised isiXhosa morphological segmentation is feasible with better optimization of the current attempt

The purpose of this study is to develop mammographie density classification, which consists of three major steps. Firstly, the digitization of mammographie images module for images and data archiving. Secondly, a morphological segmentation algorithm is proposed to detect the segment of mammographie masses with salt-and-pepper noise. Thirdly, the percentage of fibroglandular tissue in the total of breast tissue area is calculated and classified with BI-RADS criteria. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms. The overall accuracy of computerized method classification is 75%. The Kappa coefficient (0.67) indicates the good relationship and Chi-square value (7.69, p=0.053) shows no statistically significant difference. In conclusion, the computerized method based on the morphological segmentation is useful as the radiologist assistant for classifying mammographie density and is suitable for mammographie density classification.

In this paper, we propose a fully automated method able to perform accurate nuclear segmentation in immunohistochemical breast tissue images in order to provide quantitative assessment of estrogen receptor's status that will help pathologists in their diagnosis. The presented approach is based on color deconvolution and an enhanced morphological processing, which is used to identify positive stained nuclei and to separate all touching nuclei in the microscopic image for a subsequent cancer evaluation. Experiments on several breast cancer images of different patients admitted into the Tunisian Salah Azaiez Cancer Center, show the efficiency of the proposed method when compared to the manual evaluation of experts.

In the process of packaging integrated circuits (ICs), special markings are drawn to indicate the defective chips detected by test & inspection equipments. During the automated optical inspection process, these markings are detected and identified by the inspection equipment for a special treatment of detective IC packages. The marking is sometimes difficult to segment by the imaging device due to the complex background features among which the marking is drawn, such as circuit features and other graphic features. In this paper, we develop several marking segmentation algorithms based on grayscale hit-or-miss morphological transformation operations. The markings are assumed of known width different from other background features and of detectable contrast from background. Performance of the newly developed algorithms are extensively tested and compared with simple well-known morphological algorithms and the result shows equal or better segmentation accuracy.

Date of Conference: 11-13 April 2007 Conference name: 2007 Urban Remote Sensing Joint Event Automatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information. © 2007 IEEE.

Date of Conference: 11-13 June 2007 Conference name: IEEE 15th Signal Processing and Communications Applications, 2007 Yüksek çözünürlükteki uzaktan algılamalı uydu görüntülerinde bölütleme kent uygulamalarında önemli bir problemdir çünkü elde edilen bölütlemelerle sınıflandırma için piksel tabanlı spektral bilginin yanında uzamsal ve yapısal bilgiler elde edilebilir. Bu bildiride, biçimbilimsel işlemlerle çıkarılan yapısal bilgi ve ana bileşenler analizi ile özetlenen spektral bilgi kullanılarak gürültüden etkilenmeyen bölütler elde eden bir yöntem sunduk. Yapılan deneyler yöntemin görüntü üzerinde komşuluk bilgisini ve spektral bilgiyi beraber kullanmayan başka bir yönteme göre daha düzgün ve anlamlı yapılar bulduğunu göstermiştir. Automatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.

We propose a method to automate the extraction of organ areas from computing tomography (CT) images using three-dimensional mathematical morphology. Morphological segmentation provides us the theory and tools to analyze shapes directly. This characteristic leads itself well to analyzing and extracting organ areas according to size and grey level features. After a system for three-dimensional morphological processing was built, we employed a new approach, differential top-hats, to our new three-dimensional organ extraction system. The experimental results show that organ areas can be extracted automatically with a success rate of 96%.