7 research outputs found
Breast cancer detection using infrared thermal imaging and a deep learning model
Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models
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Digital Image Processing via Combination of Low-Level and High-Level Approaches.
With the growth of computer power, Digital Image Processing plays a more
and more important role in the modern world, including the field of industry,
medical, communications, spaceflight technology etc. There is no clear
definition how to divide the digital image processing, but normally, digital
image processing includes three main steps: low-level, mid-level and highlevel
processing.
Low-level processing involves primitive operations, such as: image preprocessing
to reduce the noise, contrast enhancement, and image sharpening.
Mid-level processing on images involves tasks such as segmentation (partitioning
an image into regions or objects), description of those objects to
reduce them to a form suitable for computer processing, and classification
(recognition) of individual objects. Finally, higher-level processing involves
"making sense" of an ensemble of recognised objects, as in image analysis.
Based on the theory just described in the last paragraph, this thesis is
organised in three parts: Colour Edge and Face Detection; Hand motion
detection; Hand Gesture Detection and Medical Image Processing.
II
In Colour Edge Detection, two new images G-image and R-image are
built through colour space transform, after that, the two edges extracted
from G-image and R-image respectively are combined to obtain the final
new edge. In Face Detection, a skin model is built first, then the boundary
condition of this skin model can be extracted to cover almost all of the skin
pixels. After skin detection, the knowledge about size, size ratio, locations
of ears and mouth is used to recognise the face in the skin regions.
In Hand Motion Detection, frame differe is compared with an automatically
chosen threshold in order to identify the moving object. For some special
situations, with slow or smooth object motion, the background modelling
and frame differencing are combined in order to improve the performance.
In Hand Gesture Recognition, 3 features of every testing image are input
to Gaussian Mixture Model (GMM), and then the Expectation Maximization
algorithm (EM)is used to compare the GMM from testing images and GMM
from training images in order to classify the results.
In Medical Image Processing (mammograms), the Artificial Neural Network
(ANN) and clustering rule are applied to choose the feature. Two
classifier, ANN and Support Vector Machine (SVM), have been applied to
classify the results, in this processing, the balance learning theory and optimized
decision has been developed are applied to improve the performance
Gaze-Based Human-Robot Interaction by the Brunswick Model
We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered
SPATIAL TRANSFORMATION PATTERN DUE TO COMMERCIAL ACTIVITY IN KAMPONG HOUSE
ABSTRACT Kampung houses are houses in kampung area of the city. Kampung House oftenly transformed into others use as urban dynamics. One of the transfomation is related to the commercial activities addition by the house owner. It make house with full private space become into mixused house with more public spaces or completely changed into full public commercial building. This study investigate the spatial transformation pattern of the kampung houses due to their commercial activities addition. Site observations, interviews and questionnaires were performed to study the spatial transformation. This study found that in kampung houses, the spatial transformation pattern was depend on type of commercial activities and owner perceptions, and there are several steps of the spatial transformation related the commercial activity addition.
Keywords: spatial transformation pattern; commercial activity; owner perception, kampung house; adaptabilit
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words