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Exploitation and using of clean and renewable energy to improve the domestic energy structure is important for building smart city. Photovoltaic (PV) power generation is a good choose, and modules inspection is an important direction of PV power generation. The manual inspection is a common way but is not feasible for large-scale PV systems in practice due to low efficiency, high error rate and long...
Inspired by recent successful deep learning methods, this paper presents a new approach for polarimetric synthetic aperture radar (PolSAR) image classification. It combines both advantages of pixel-based and object-based methods. An improved simple linear iterative clustering (SLIC) superpixel segmentation algorithm is used to obtain spatial information in the PolSAR image. Then, a Deep Belief Network...
The segmentation of multispectral images is considered as a key step in image processing for biomedical applications. Performing this step using the appropriate methodology is a real issue that being investigated by the research community. In this paper, we propose a new algorithm to perform automatic segmentation based on k-means methodology within an automatic generation of the optimal value of...
Automatic segmentation in Ziehl-Neelsen Stained Tissue Slide Images is to help identify whether the blood cells that have been exposed to tuberculosis. In an image segmentation in the detection of TB disease are still many obstacles and requires in many time. in this study perform segmentation is useful to help detect the germs of TB disease in the blood cells and segmentation, there are several ways...
We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal...
Tattoos, a soft biometrie trait, are gradually being used to identify suspects in forensic science. Based on LAB color space and K-mean clustering, we propose a novel segmentation algorithm to improve the segmentation accuracy of color tattoos. The process consists of three parts. Firstly, we use K-mean clustering and human skin color segmentation in LAB color space to detect the skin area. Then,...
Image segmentation is a fundamental problem in image processing and computer vision. Its goal is to separate an image into a collection of distinct regions, after which other high-level tasks can be performed. Nomalized cut (Ncut) algorithm is the most popular one in image segmentation algorithms. However, the number of segmentation regions needs to be specified by users or experts before the Ncut...
As one of the most popular methods for image segmentation, fuzzy C-means algorithm suffers two unavoidable initialization difficulties including obtaining initial cluster centroids and deciding cluster number, which affect the algorithm performance. Motivated by the above, an automatic fuzzy clustering algorithm is proposed in this paper, where observation matrix, judgment matrix and set partitioning...
The automation process of Pap smear analysis holds the potential to address women's health care in the face of an increasing population and respective collected data. A fundamental step for automating analysis is cell detection from light microscopy images. Such information serves as input to cell classification algorithms and diagnostic recommendation tools. This paper describes an approach to nuclei...
To acquire the fruit region of green apple image in close color more completely, this work designed a method of region extraction before fruit region combination. Firstly, channel images in every color space were analyzed to determine appropriate color channel images: images of G and b color channel in RGB and Lab color spaces, respectively. Then, these images were segmented by K-means clustering...
Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different...
Modern robotics involves partial or complete automation systems, which in turn requires the presence of elements of artificial intelligence. That's why appears a necessity in system of pattern recognition.
This paper presents a novel unsupervised superpixel-based change detection approach to detect multiple changes in Very-High-Resolution remote sensing images. The proposed approach investigates the spectral-spatial variations at superpixel level which aims to enhance the traditional pixel level change detection performance. In particular, superpixel representation of the spectral change vectors is...
Indian economy highly depends on agricultural productivity. An important role is played by the detection of disease to obtain a perfect results in agriculture, and it is natural to have disease in plants. Proper care should be taken in this area for product quality and quantity. To reduce the large amount of monitoring in field automatic detection techniques can be used. This paper discuss different...
This paper presents a set of procedures for detecting the primary embryo development of chicken eggs using Self-Organizing Mapping (SOM) technique and K-means clustering algorithm. Our strategy consists of preprocessing of an acquired color image with color space transformation, grouping the data by Self-Organizing Mapping technique and predicting the embryo development by K-means clustering method...
Clustering is an unsupervised technique is used for organizing the data for efficient retrieval. This is mainly used in pattern reorganization and data analysis. Today many cluster analysis techniques are used for data analysis and have proven to be very useful in segmentation. Performance of these algorithms is data dependent. In this paper K-Means and Fuzzy C-Means are implemented for segmenting...
Superpixel segmentation targets at grouping pixels in an image into atomic regions that align well with the natural object boundaries. In this paper, we propose a novel superpixel segmentation method based on an iterative and adaptive clustering algorithm that embraces color, contour, texture, and spatial features together. The algorithm adjusts the weights of different features automatically in a...
The paper proposes an intelligent K-means segmentation algorithm that clearly segments foreground objects and completely occluded objects. When a person completely occludes an object while entering into the area of video surveillance, it is considered as an anomaly. The paper comes up with a robust technical solution to address this. The proposed algorithm chooses an optimal value for K and segments...
The particle size distribution (PSD) of a dispersed phase is a fundamental geometrical characteristic that needs to be determined from digital images for many industrial processes involving a multiphase flow. Nevertheless, when dealing with 2-D images, only the projections of the particles are visualized and therefore the particles can overlap each other. In this way, this paper aims to develop and...
Superpixels are perceptually meaningful atomic regions that can effectively capture image features. We propose a novel scale adaptive supervoxel segmentation algorithm for RGB-D images, i.e., small supervoxels in content-dense regions (e.g., with high intensity or color variation) and large supervoxels in content-sparse regions. Among various methods for computing uniform superpixels, simple linear...
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