scholarly journals FaceParser – A new face segmentation approach and labeleddatabase

2018 ◽  
Vol 7 (2.5) ◽  
pp. 1
Author(s):  
Khalil Khan ◽  
Nasir Ahmad ◽  
Irfan Uddin ◽  
Muhammad Ehsan Mazhar ◽  
Rehan Ullah Khan
Keyword(s):  
Semantic Segmentation ◽  
New Method ◽  
Facial Image ◽  
Data Set ◽  
Semantic Classes ◽  
Discriminative Model ◽  
Face Segmentation ◽  
Segmentation Approach ◽  
Class Labels ◽  
Pixel Labeling

Background and objective: A novel face parsing method is proposed in this paper which partition facial image into six semantic classes. Unlike previous approaches which segmented a facial image into three or four classes, we extended the class labels to six. Materials and Methods: A data-set of 464 images taken from FEI, MIT-CBCL, Pointing’04 and SiblingsDB databases was annotated. A discriminative model was trained by extracting features from squared patches. The built model was tested on two different semantic segmentation approaches – pixel-based and super-pixel-based semantic segmentation (PB_SS and SPB_SS).Results: A pixel labeling accuracy (PLA) of 94.68% and 90.35% was obtained with PB_SS and SPB_SS methods respectively on frontal images. Conclusions: A new method for face parts parsing was proposed which efficiently segmented a facial image into its constitute parts.

A New Approach for the Analysis of Mixture Toxicity Data

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2345-2348 ◽  
Author(s):  
C. N. Haas
Keyword(s):  
Quantitative Analysis ◽  
Mixture Toxicity ◽  
New Method ◽  
Metal Exposure ◽  
Positive Interactions ◽  
Toxicity Data ◽  
Data Set ◽  
New Approach ◽  
Negative Interactions

A new method for the quantitative analysis of multiple toxicity data is described and illustrated using a data set on metal exposure to copepods. Positive interactions are observed for Ni-Pb and Pb-Cr, with weak negative interactions observed for Ni-Cr.

scholarly journals A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

2021 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Growth Regulation ◽  
Pattern Analysis ◽  
Developmental Time ◽  
Dynamical Stability ◽  
New Method ◽  
Data Sets ◽  
Data Set ◽  
Landmark Data ◽  
Study Designs ◽  
Changes Over Time

AbstractA matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

A Novel K-Harmonic Means Clustering Based on Multiple Initial Centers

2013 ◽  
Vol 321-324 ◽  
pp. 1947-1950
Author(s):  
Lei Gu ◽  
Xian Ling Lu
Keyword(s):  
New Method ◽  
Final Solution ◽  
Data Set ◽  
Number Of Clusters ◽  
Multiple Groups ◽  
Harmonic Means

In the initialization of the traditional k-harmonic means clustering, the initial centers are generated randomly and its number is equal to the number of clusters. Although the k-harmonic means clustering is insensitive to the initial centers, this initialization method cannot improve clustering performance. In this paper, a novel k-harmonic means clustering based on multiple initial centers is proposed. The number of the initial centers is more than the number of clusters in this new method. The new method with multiple initial centers can divide the whole data set into multiple groups and combine these groups into the final solution. Experiments show that the presented algorithm can increase the better clustering accuracies than the traditional k-means and k-harmonic methods.

scholarly journals Spinal cord segmentation and classification of degenerative disease

2019 ◽  
Vol 10 (3) ◽  
pp. 2426-2432 ◽  
Author(s):  
Arjun ◽  
Kanchana V
Keyword(s):  
Image Processing ◽  
Spinal Cord ◽  
Human Life ◽  
Semantic Segmentation ◽  
Processing Technique ◽  
Degenerative Disease ◽  
Image Processing Technique ◽  
Segmentation Approach ◽  
Work First ◽  
Mri Image

spinal cord plays an important role in human life. In our work, we are using digital image processing technique, the interior part of the human body can be analyzed using MRI, CT and X-RAY etc. Medical image processing technique is extensively used in medical field. In here we are using MRI image to perform our work In our proposed work, we are finding degenerative disease from spinal cord image. In our work first, we are preprocessing the MRI image and locate the degenerative part of the spinal cord, finding the degenerative part using various segmentation approach after that classifying degenerative disease or normal spinal cord using various classification algorithm. For segmentation, we are using an efficient semantic segmentation approach

Categorization of Data Clustering Techniques

2008 ◽  
pp. 568-577
Author(s):  
Baoying Wang ◽  
Imad Rahal ◽  
Richard Leipold
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Data Clustering ◽  
Analysis Data ◽  
Discovery Process ◽  
Data Set ◽  
Market Basket ◽  
Clustering Techniques ◽  
Data Points ◽  
Class Labels

Data clustering is a discovery process that partitions a data set into groups (clusters) such that data points within the same group have high similarity while being very dissimilar to points in other groups (Han & Kamber, 2001). The ultimate goal of data clustering is to discover natural groupings in a set of patterns, points, or objects without prior knowledge of any class labels. In fact, in the machine-learning literature, data clustering is typically regarded as a form of unsupervised learning as opposed to supervised learning. In unsupervised learning or clustering, there is no training function as in supervised learning. There are many applications for data clustering including, but not limited to, pattern recognition, data analysis, data compression, image processing, understanding genomic data, and market-basket research.

scholarly journals Edge Prior Multilayer Segmentation Network Based on Bayesian Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Chu He ◽  
Zishan Shi ◽  
Peizhang Fang ◽  
Dehui Xiong ◽  
Bokun He ◽  
...  
Keyword(s):  
Prior Information ◽  
Semantic Segmentation ◽  
Bayesian Framework ◽  
Excellent Performance ◽  
Transform Method ◽  
Data Set ◽  
Distance Map ◽  
Constraint Network ◽  
Average Accuracy ◽  
Complex Boundaries

In recent years, methods based on neural network have achieved excellent performance for image segmentation. However, segmentation around the edge area is still unsatisfactory when dealing with complex boundaries. This paper proposes an edge prior semantic segmentation architecture based on Bayesian framework. The entire framework is composed of three network structures, a likelihood network and an edge prior network at the front, followed by a constraint network. The likelihood network produces a rough segmentation result, which is later optimized by edge prior information, including the edge map and the edge distance. For the constraint network, the modified domain transform method is proposed, in which the diffusion direction is revised through the newly defined distance map and some added constraint conditions. Experiments about the proposed approach and several contrastive methods show that our proposed method had good performance and outperformed FCN in terms of average accuracy for 0.0209 on ESAR data set.

scholarly journals Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches

2020 ◽  
Vol 10 (5) ◽  
pp. 1679
Author(s):  
Xinying Xu ◽  
Yujing Xue ◽  
Xiaoxia Han ◽  
Zhe Zhang ◽  
Jun Xie ◽  
...  
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Category ◽  
Semantic Segmentation ◽  
Potential Energy Function ◽  
Semantic Class ◽  
Image Patches ◽  
Benchmark Datasets ◽  
Weakly Supervised ◽  
Class Labels

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.

scholarly journals EFN: Field-Based Object Detection for Aerial Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3630
Author(s):  
Jin Liu ◽  
Haokun Zheng
Keyword(s):  
Object Detection ◽  
Semantic Segmentation ◽  
Natural Images ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Sets ◽  
Data Set ◽  
Bounding Box ◽  
Good Score ◽  
Bounding Boxes

Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and are densely arranged. Therefore, using an oriented bounding box to mark the target is a mainstream choice. However, this general method is designed based on horizontal box annotation, while the improved method for detecting an oriented bounding box has a high computational complexity. In this paper, we propose a method called ellipse field network (EFN) to organically integrate semantic segmentation and object detection. It predicts the probability distribution of the target and obtains accurate oriented bounding boxes through a post-processing step. We tested our method on the HRSC2016 and DOTA data sets, achieving mAP values of 0.863 and 0.701, respectively. At the same time, we also tested the performance of EFN on natural images and obtained a mAP of 84.7 in the VOC2012 data set. These extensive experiments demonstrate that EFN can achieve state-of-the-art results in aerial image tests and can obtain a good score when considering natural images.

scholarly journals A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2140
Author(s):  
Tomasz Hachaj
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Image Annotation ◽  
Facial Feature ◽  
Video Data ◽  
Low Power Consumption ◽  
Facial Image ◽  
True Negative ◽  
Data Set ◽  
Average Accuracy

This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research.

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