5,028 research outputs found
Application of Fractal and Wavelets in Microcalcification Detection
Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection
Iris Recognition Based on LBP and Combined LVQ Classifier
Iris recognition is considered as one of the best biometric methods used for
human identification and verification, this is because of its unique features
that differ from one person to another, and its importance in the security
field. This paper proposes an algorithm for iris recognition and classification
using a system based on Local Binary Pattern and histogram properties as a
statistical approaches for feature extraction, and Combined Learning Vector
Quantization Classifier as Neural Network approach for classification, in order
to build a hybrid model depends on both features. The localization and
segmentation techniques are presented using both Canny edge detection and Hough
Circular Transform in order to isolate an iris from the whole eye image and for
noise detection .Feature vectors results from LBP is applied to a Combined LVQ
classifier with different classes to determine the minimum acceptable
performance, and the result is based on majority voting among several LVQ
classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different
extensions and size are presented. Since LBP is working on a grayscale level so
colored iris images should be transformed into a grayscale level. The proposed
system gives a high recognition rate 99.87 % on different iris datasets
compared with other methods.Comment: 12 Pages, 12 Figure
Skin Texture Recognition Using Neural Networks
Skin recognition is used in many applications ranging from algorithms for
face detection, hand gesture analysis, and to objectionable image filtering. In
this work a skin recognition system was developed and tested. While many skin
segmentation algorithms relay on skin color, our work relies on both skin color
and texture features (features derives from the GLCM) to give a better and more
efficient recognition accuracy of skin textures. We used feed forward neural
networks to classify input textures images to be skin or non skin textures. The
system gave very encouraging results during the neural network generalization
face.Comment: 4 pages, 6 figures, conference ACIT 2008, Tunisi
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit striking
failures on out-of-distribution inputs. One core idea of adversarial example
research is to reveal neural network errors under such distribution shifts. We
decompose these errors into two complementary sources: sensitivity and
invariance. We show deep networks are not only too sensitive to task-irrelevant
changes of their input, as is well-known from epsilon-adversarial examples, but
are also too invariant to a wide range of task-relevant changes, thus making
vast regions in input space vulnerable to adversarial attacks. We show such
excessive invariance occurs across various tasks and architecture types. On
MNIST and ImageNet one can manipulate the class-specific content of almost any
image without changing the hidden activations. We identify an insufficiency of
the standard cross-entropy loss as a reason for these failures. Further, we
extend this objective based on an information-theoretic analysis so it
encourages the model to consider all task-dependent features in its decision.
This provides the first approach tailored explicitly to overcome excessive
invariance and resulting vulnerabilities
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
Prostate cancer is the most diagnosed form of cancer in Canadian men, and is
the third leading cause of cancer death. Despite these statistics, prognosis is
relatively good with a sufficiently early diagnosis, making fast and reliable
prostate cancer detection crucial. As imaging-based prostate cancer screening,
such as magnetic resonance imaging (MRI), requires an experienced medical
professional to extensively review the data and perform a diagnosis,
radiomics-driven methods help streamline the process and has the potential to
significantly improve diagnostic accuracy and efficiency, and thus improving
patient survival rates. These radiomics-driven methods currently rely on
hand-crafted sets of quantitative imaging-based features, which are selected
manually and can limit their ability to fully characterize unique prostate
cancer tumour phenotype. In this study, we propose a novel \textit{discovery
radiomics} framework for generating custom radiomic sequences tailored for
prostate cancer detection. Discovery radiomics aims to uncover abstract
imaging-based features that capture highly unique tumour traits and
characteristics beyond what can be captured using predefined feature models. In
this paper, we discover new custom radiomic sequencers for generating new
prostate radiomic sequences using multi-parametric MRI data. We evaluated the
performance of the discovered radiomic sequencer against a state-of-the-art
hand-crafted radiomic sequencer for computer-aided prostate cancer detection
with a feedforward neural network using real clinical prostate multi-parametric
MRI data. Results for the discovered radiomic sequencer demonstrate good
performance in prostate cancer detection and clinical decision support relative
to the hand-crafted radiomic sequencer. The use of discovery radiomics shows
potential for more efficient and reliable automatic prostate cancer detection.Comment: 8 page
Broad Neural Network for Change Detection in Aerial Images
A change detection system takes as input two images of a region captured at
two different times, and predicts which pixels in the region have undergone
change over the time period. Since pixel-based analysis can be erroneous due to
noise, illumination difference and other factors, contextual information is
usually used to determine the class of a pixel (changed or not). This
contextual information is taken into account by considering a pixel of the
difference image along with its neighborhood. With the help of ground truth
information, the labeled patterns are generated. Finally, Broad Learning
classifier is used to get prediction about the class of each pixel. Results
show that Broad Learning can classify the data set with a significantly higher
F-Score than that of Multilayer Perceptron. Performance comparison has also
been made with other popular classifiers, namely Multilayer Perceptron and
Random Forest.Comment: : IEMGraph (International Conference on
Emerging Technology in Modelling and Graphics) 2018 : 6-7 September, 2018 :
Kolkatta, Indi
How far did we get in face spoofing detection?
The growing use of control access systems based on face recognition shed
light over the need for even more accurate systems to detect face spoofing
attacks. In this paper, an extensive analysis on face spoofing detection works
published in the last decade is presented. The analyzed works are categorized
by their fundamental parts, i.e., descriptors and classifiers. This structured
survey also brings the temporal evolution of the face spoofing detection field,
as well as a comparative analysis of the works considering the most important
public data sets in the field. The methodology followed in this work is
particularly relevant to observe trends in the existing approaches, to discuss
still opened issues, and to propose new perspectives for the future of face
spoofing detection
Detection and classification of masses in mammographic images in a multi-kernel approach
According to the World Health Organization, breast cancer is the main cause
of cancer death among adult women in the world. Although breast cancer occurs
indiscriminately in countries with several degrees of social and economic
development, among developing and underdevelopment countries mortality rates
are still high, due to low availability of early detection technologies. From
the clinical point of view, mammography is still the most effective diagnostic
technology, given the wide diffusion of the use and interpretation of these
images. Herein this work we propose a method to detect and classify
mammographic lesions using the regions of interest of images. Our proposal
consists in decomposing each image using multi-resolution wavelets. Zernike
moments are extracted from each wavelet component. Using this approach we can
combine both texture and shape features, which can be applied both to the
detection and classification of mammary lesions. We used 355 images of fatty
breast tissue of IRMA database, with 233 normal instances (no lesion), 72
benign, and 83 malignant cases. Classification was performed by using SVM and
ELM networks with modified kernels, in order to optimize accuracy rates,
reaching 94.11%. Considering both accuracy rates and training times, we defined
the ration between average percentage accuracy and average training time in a
reverse order. Our proposal was 50 times higher than the ratio obtained using
the best method of the state-of-the-art. As our proposed model can combine high
accuracy rate with low learning time, whenever a new data is received, our work
will be able to save a lot of time, hours, in learning process in relation to
the best method of the state-of-the-art
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Deep Learning with the Random Neural Network and its Applications
The random neural network (RNN) is a mathematical model for an "integrate and
fire" spiking network that closely resembles the stochastic behaviour of
neurons in mammalian brains. Since its proposal in 1989, there have been
numerous investigations into the RNN's applications and learning algorithms.
Deep learning (DL) has achieved great success in machine learning. Recently,
the properties of the RNN for DL have been investigated, in order to combine
their power. Recent results demonstrate that the gap between RNNs and DL can be
bridged and the DL tools based on the RNN are faster and can potentially be
used with less energy expenditure than existing methods.Comment: 23 pages, 19 figure
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