scholarly journals Portrait Extraction Algorithm Based on Face Detection and Image Segmentation

2019 ◽  
Vol 12 (2) ◽  
pp. 1 ◽  
Author(s):  
Chongyi Yang ◽  
Wanyu Huang ◽  
Ruoqi Zhang ◽  
Rui Kong
Keyword(s):  
Image Segmentation ◽  
Closed Form ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Input Image ◽  
Fixed Size ◽  
Detection Technology ◽  
Extraction Algorithm ◽  
Photo Collection ◽  
Image Segmentation Algorithm

Aiming to solve a series of problems in photo collection over citizen’s license, this paper proposes Portrait Extraction Algorithm over our face based on facial detection technology and state-of-the-art image segmentation algorithm. Considering an input image where the foreground stands a man with unfixed size and its background is all sorts of complicated background, firstly we use Haar&Adaboost facial detection algorithm as a preprocessing method so as to divide the image into different sub-systems, and we get a fix-sized image of human face. Then we use GrabCut and closed-form algorithm to segment the preprocessed image and output an image which satisfies our requirements (i.e. the fixed size and fixed background). Up to now the GrabCut and closed-form algorithm has been realized, both of which have its own advantages and shortages.

scholarly journals Early smoke detection of forest fires based on SVM image segmentation

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 150-159
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Smoke Detection ◽  
Large Space ◽  
Merging Algorithm ◽  
Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

scholarly journals Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

2019 ◽  
Vol 9 (16) ◽  
pp. 3245 ◽  
Author(s):  
Wang ◽  
Lei ◽  
Chen ◽  
Li ◽  
Zou
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Future Research ◽  
Fixed Size ◽  
Shape Information ◽  
Microscopic Images ◽  
Analytical Technique ◽  
Heterogeneous Nature ◽  
Coarse To Fine ◽  
Identification Strategy

An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at https:/github.com/GuyooGu/MISC-Master.

Research and Simulation on the Image Segmentation Algorithm with Corn Image Based on HSI Color Model

2012 ◽  
Vol 459 ◽  
pp. 128-131
Author(s):  
Xue Feng Hou ◽  
Yuan Yuan Shang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Algorithm ◽  
Color Model ◽  
Edge Detection Algorithm ◽  
Segmentation Algorithms ◽  
Hsi Color Model ◽  
Classical Image ◽  
Image Segmentation Algorithm

Image segmentation is one focus of digital image processing. In this paper, fourteen different kinds of classical image segmentation algorithms are studied and compared using corn image and simulating in MATLAB based on HSI color model. The result reveals that the method that using H component based on HSI color model to deal with the histogram threshold algorithm and Laplace edge detection algorithm is effectively extract the plant from the corn image

scholarly journals The Study of Randomized Visual Saliency Detection Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Image Segmentation ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Input Image ◽  
Memory Space ◽  
Segmentation Process ◽  
Visual Saliency Detection

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

Bag of tricks for fabric defect detection based on Cascade R-CNN

2020 ◽  
pp. 004051752095522
Author(s):  
Feng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Input Image ◽  
Defect Size ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Single Input

In this paper, a bag of tricks is proposed to improve the precision of fabric defect detection. Although the general state-of-the-art convolutional neural network detection algorithm can achieve a better detection effect, in fact, the detection precision still has enough room to improve on fabric defect detection. Therefore, we propose three tricks to further improve the precision. Firstly, we use multiscale training, which scales the single input image into a number of images of different resolutions for training, so as to be able to adapt to the box distribution of different scales. Secondly, we use the dimension clusters method. By observing the distribution of the width and the height of the defect size in the fabric dataset, we find that the distribution of the defect size in the dataset is extremely unbalanced and the size span is large. We believe that the training results of the default prior boxes setting might not be optimal, so we conduct dimensional clustering for the width and height of the defect size of the dataset, so as to make the network model easier to learn. Thirdly, we use soft non-maximum suppression instead of traditional non-maximum suppression to avoid the situation that the same kinds of defect category in the dataset are overlapped and eliminated as repeated detection. With this bag of tricks, we effectively improve the precision of fabric defect detection by 8.9% mAP on the basis of the baseline of state-of-the-art convolutional neural network detection algorithm.

scholarly journals Fingerprint Image Segmentation: A Review of State of the Art Techniques

2017 ◽  
pp. 28-39 ◽  
Author(s):  
Krishna Prasad K. ◽  
P. S. Aithal
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Input Image ◽  
Identification System ◽  
Initial Image ◽  
Fingerprint Image ◽  
Level 3 ◽  
Level 1 ◽  
Level 2 ◽  
Art Techniques

In Automatic Fingerprint Identification System (AFIS), pre-processing of the image is a crucial process in deciding the quality and performance of the system. Pre-processing is consists many stages as Segmentation, Enhancement, Binarisation, and Thinning. In this segmentation is one of the steps of pre-processing which differentiate foreground and background region of fingerprint images. Segmentation is the separation of the fingerprint region or extraction of the presence of ridges from the background of the initial image. Segmentation is necessary because it constructs the region of interest from the input image, reduces the processing time, increases the recognition or matching process performance, and reduces the probability of false feature extraction. A 100% accurate segmentation is always very difficult, especially in the very poor quality image or partial image filled with noise such as the presence of latent. Fingerprints are made of Ridge and Valley structure and their features are classified in three levels as Level 1, Level 2, and Level 3. Level 1 Features are singular macro details like ridge pattern and ridge flows. Level 2 is ridge local features like ridge bifurcation and ridge ending or simply minutiae points or ridge orientation. Level 3 is micro details like sweat pores, incipient ridges. This paper provides an overview of the state of the art techniques of fingerprint image segmentation and contribution of other researchers on segmentation. This paper also discusses a different class of segmentation algorithms with its measuring parameters, computational complexity, advantages, limitations, and applications.

scholarly journals Colour image segmentation using perceptual colour difference saliency algorithm

2017 ◽  
Author(s):  
◽  
Taiwo Tunmike Bukola
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Segmentation Algorithm ◽  
Morphological Operations ◽  
Colour Difference ◽  
Colour Image ◽  
Colour Image Segmentation ◽  
Segmentation Algorithms ◽  
Imaging Conditions ◽  
Image Segmentation Algorithm

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

An Improved MeanShift Insulator Image Segmentation Algorithm

2013 ◽  
Vol 634-638 ◽  
pp. 3945-3949
Author(s):  
Fang Ting ◽  
Yun Biao Zhao ◽  
Xing Liu Hu ◽  
Xia Bing
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Edge Detection Algorithm ◽  
Mean Shift Algorithm ◽  
Image Segmentation Algorithm

According to color characteristics of insulator ,The paper is based on HSI model and Mean Shift algorithm in order to segmentation insulator images. It firstly introduces theory of mean shift algorithm, then explains morphological processing with edge detection algorithm to extract the insulator images contour. Last using Hough transform to obtain the segmentation results. Experiment indicates that VC6.0 combined with opencv simulation the proposed algorithm could effectively extract segmentation of insulator images provides the basis for follow-up determination of insulator faults.

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