20,148 research outputs found
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras
Aerators are essential and crucial auxiliary devices in intensive culture,
especially in industrial culture in China. The traditional methods cannot
accurately detect abnormal condition of aerators in time. Surveillance cameras
are widely used as visual perception modules of the Internet of Things, and
then using these widely existing surveillance cameras to realize real-time
anomaly detection of aerators is a cost-free and easy-to-promote method.
However, it is difficult to develop such an expert system due to some technical
and applied challenges, e.g., illumination, occlusion, complex background, etc.
To tackle these aforementioned challenges, we propose a real-time expert system
based on computer vision technology and existing surveillance cameras for
anomaly detection of aerators, which consists of two modules, i.e., object
region detection and working state detection. First, it is difficult to detect
the working state for some small object regions in whole images, and the time
complexity of global feature comparison is also high, so we present an object
region detection method based on the region proposal idea. Moreover, we propose
a novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm
for motion feature extraction in fixed regions. Then, we present a dimension
reduction method of time series for establishing a feature dataset with obvious
boundaries between classes. Finally, we use machine learning algorithms to
build the feature classifier. The experimental results in both the actual video
dataset and the augmented video dataset show that the accuracy for detecting
object region and working state of aerators is 100% and 99.9% respectively, and
the detection speed is 77-333 frames per second (FPS) according to the
different types of surveillance cameras.Comment: 17 figure
Covfefe: A Computer Vision Approach For Estimating Force Exertion
Cumulative exposure to repetitive and forceful activities may lead to
musculoskeletal injuries which not only reduce workers' efficiency and
productivity, but also affect their quality of life. Thus, widely accessible
techniques for reliable detection of unsafe muscle force exertion levels for
human activity is necessary for their well-being. However, measurement of force
exertion levels is challenging and the existing techniques pose a great
challenge as they are either intrusive, interfere with human-machine interface,
and/or subjective in the nature, thus are not scalable for all workers. In this
work, we use face videos and the photoplethysmography (PPG) signals to classify
force exertion levels of 0\%, 50\%, and 100\% (representing rest, moderate
effort, and high effort), thus providing a non-intrusive and scalable approach.
Efficient feature extraction approaches have been investigated, including
standard deviation of the movement of different landmarks of the face,
distances between peaks and troughs in the PPG signals. We note that the PPG
signals can be obtained from the face videos, thus giving an efficient
classification algorithm for the force exertion levels using face videos. Based
on the data collected from 20 subjects, features extracted from the face videos
give 90\% accuracy in classification among the 100\% and the combination of 0\%
and 50\% datasets. Further combining the PPG signals provide 81.7\% accuracy.
The approach is also shown to be robust to the correctly identify force level
when the person is talking, even though such datasets are not included in the
training.Comment: 12 page
Bone marrow cells detection: A technique for the microscopic image analysis
In the detection of myeloproliferative, the number of cells in each type of
bone marrow cells (BMC) is an important parameter for the evaluation. In this
study, we propose a new counting method, which also consists of three modules
including localization, segmentation and classification. The localization of
BMC is achieved from a color transformation enhanced BMC sample image and
stepwise averaging method (SAM). In the nucleus segmentation, both SAM and
Otsu's method will be applied to obtain a weighted threshold for segmenting the
patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color
weakening transformation, an improved region growing method and the K-Means
algorithm are used. The connected cells with BMC will be separated by the
marker-controlled watershed algorithm. The features will be extracted for the
classification after the segmentation. In this study, the BMC are classified
using the SVM, Random Forest, Artificial Neural Networks, Adaboost and Bayesian
Networks into five classes including one outlier, namely, neutrophilic split
granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes
and the outlier (all other cells not listed). Our experimental results show
that the best average recognition rate is 87.49% for the SVM.Comment: 11 pages, 8 figures and 4 table
Fingerprint Recognition Using Minutia Score Matching
The popular Biometric used to authenticate a person is Fingerprint which is
unique and permanent throughout a person's life. A minutia matching is widely
used for fingerprint recognition and can be classified as ridge ending and
ridge bifurcation. In this paper we projected Fingerprint Recognition using
Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block
Filter is used, which scans the image at the boundary to preserves the quality
of the image and extract the minutiae from the thinned image. The false
matching ratio is better compared to the existing algorithm.Comment: 8 Page
Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree
We presents in this paper a novel fish classification methodology based on a
combination between robust feature selection, image segmentation and
geometrical parameter techniques using Artificial Neural Network and Decision
Tree. Unlike existing works for fish classification, which propose descriptors
and do not analyze their individual impacts in the whole classification task
and do not make the combination between the feature selection, image
segmentation and geometrical parameter, we propose a general set of features
extraction using robust feature selection, image segmentation and geometrical
parameter and their correspondent weights that should be used as a priori
information by the classifier. In this sense, instead of studying techniques
for improving the classifiers structure itself, we consider it as a black box
and focus our research in the determination of which input information must
bring a robust fish discrimination.The main contribution of this paper is
enhancement recognize and classify fishes based on digital image and To develop
and implement a novel fish recognition prototype using global feature
extraction, image segmentation and geometrical parameters, it have the ability
to Categorize the given fish into its cluster and Categorize the clustered fish
into poison or non-poison fish, and categorizes the non-poison fish into its
family .Comment: 7 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS November 2009, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
This paper addresses the task of nuclei segmentation in high-resolution
histopathological images. We propose an auto- matic end-to-end deep neural
network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary
model is introduced to predict nuclei and their boundaries simultaneously using
a fully convolutional neural network. Given a color normalized image, the model
directly outputs an estimated nuclei map and a boundary map. A simple, fast and
parameter-free post-processing procedure is performed on the estimated nuclei
map to produce the final segmented nuclei. An overlapped patch extraction and
assembling method is also designed for seamless prediction of nuclei in large
whole-slide images. We also show the effectiveness of data augmentation methods
for nuclei segmentation task. Our experiments showed our method outperforms
prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000
image can be segmented in less than 5 seconds. This makes it possible to
precisely segment the whole-slide image in acceptable timeComment: 13 pages. 12 figure
Intelligent System for Speaker Identification using Lip features with PCA and ICA
Biometric authentication techniques are more consistent and efficient than
conventional authentication techniques and can be used in monitoring,
transaction authentication, information retrieval, access control, forensics,
etc. In this paper, we have presented a detailed comparative analysis between
Principle Component Analysis (PCA) and Independent Component Analysis (ICA)
which are used for feature extraction on the basis of different Artificial
Neural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF)
and Learning Vector Quantization (LVQ). In this paper, we have chosen "TULIPS1
database, (Movellan, 1995)" which is a small audiovisual database of 12
subjects saying the first 4 digits in English for the incorporation of above
methods. The six geometric lip features i.e. height of the outer corners of the
mouth, width of the outer corners of the mouth, height of the inner corners of
the mouth, width of the inner corners of the mouth, height of the upper lip,
and height of the lower lip which extracts the identity relevant information
are considered for the research work. After the comprehensive analysis and
evaluation a maximum of 91.07% accuracy in speaker recognition is achieved
using PCA and RBF and 87.36% accuracy is achieved using ICA and RBF. Speaker
identification has a wide scope of applications such as access control,
monitoring, transaction authentication, information retrieval, forensics, etc.Comment: https://sites.google.com/site/journalofcomputing
Minutiae Extraction from Fingerprint Images - a Review
Fingerprints are the oldest and most widely used form of biometric
identification. Everyone is known to have unique, immutable fingerprints. As
most Automatic Fingerprint Recognition Systems are based on local ridge
features known as minutiae, marking minutiae accurately and rejecting false
ones is very important. However, fingerprint images get degraded and corrupted
due to variations in skin and impression conditions. Thus, image enhancement
techniques are employed prior to minutiae extraction. A critical step in
automatic fingerprint matching is to reliably extract minutiae from the input
fingerprint images. This paper presents a review of a large number of
techniques present in the literature for extracting fingerprint minutiae. The
techniques are broadly classified as those working on binarized images and
those that work on gray scale images directly.Comment: 12 pages; IJCSI International Journal of Computer Science Issues,
Vol. 8, Issue 5, September 201
A General Framework for Multi-focal Image Classification and Authentication: Application to Microscope Pollen Images
In this article, we propose a general framework for multi-focal image
classification and authentication, the methodology being demonstrated on
microscope pollen images. The framework is meant to be generic and based on a
brute force-like approach aimed to be efficient not only on any kind, and any
number, of pollen images (regardless of the pollen type), but also on any kind
of multi-focal images. All stages of the framework's pipeline are designed to
be used in an automatic fashion. First, the optimal focus is selected using the
absolute gradient method. Then, pollen grains are extracted using a
coarse-to-fine approach involving both clustering and morphological techniques
(coarse stage), and a snake-based segmentation (fine stage). Finally, features
are extracted and selected using a generalized approach, and their
classification is tested with four classifiers: Weighted Neighbor Distance,
Neural Network, Decision Tree and Random Forest. The latter method, which has
shown the best and more robust classification accuracy results (above 97\% for
any number of pollen types), is finally used for the authentication stage
Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network
Medical image analysis, especially segmenting a specific organ, has an
important role in developing clinical decision support systems. In cardiac
magnetic resonance (MR) imaging, segmenting the left and right ventricles helps
physicians diagnose different heart abnormalities. There are challenges for
this task, including the intensity and shape similarity between left ventricle
and other organs, inaccurate boundaries and presence of noise in most of the
images. In this paper we propose an automated method for segmenting the left
ventricle in cardiac MR images. We first automatically extract the region of
interest, and then employ it as an input of a fully convolutional network. We
train the network accurately despite the small number of left ventricle pixels
in comparison with the whole image. Thresholding on the output map of the fully
convolutional network and selection of regions based on their roundness are
performed in our proposed post-processing phase. The Dice score of our method
reaches 87.24% by applying this algorithm on the York dataset of heart images.Comment: 4 pages, 3 figure
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