Abstract: Indonesia has experienced annual forest fires that have
rapidly destroyed and degraded its forests. Fires in the peat swamp
forests of Riau Province, have set the stage for problems to worsen,
this being the ecosystem most prone to fires (which are also the most
difficult, to extinguish). Despite various efforts to curb deforestation,
and forest degradation processes, severe forest fires are still
occurring. To find an effective solution, the basic causes of the
problems must be identified. It is therefore critical to have an indepth
understanding of the underlying causal factors that have
contributed to deforestation and forest degradation as a whole, in
order to attain reductions in their rates. An assessment of the drivers of deforestation and forest
degradation was carried out, in order to design and implement
measures that could slow these destructive processes. Research was
conducted in Giam Siak Kecil–Bukit Batu Biosphere Reserve
(GSKBB BR), in the Riau Province of Sumatera, Indonesia. A
biosphere reserve was selected as the study site because such reserves
aim to reconcile conservation with sustainable development. A
biosphere reserve should promote a range of local human activities,
together with development values that are in line spatially and
economically with the area conservation values, through use of a
zoning system. Moreover, GSKBB BR is an area with vast peatlands,
and is experiencing forest fires annually. Various factors were
analysed to assess the drivers of deforestation and forest degradation
in GSKBB BR; data were collected from focus group discussions
with stakeholders, key informant interviews with key stakeholders,
field observation and a literature review. Landsat satellite imagery was used to map forest-cover changes
for various periods. Analysis of landsat images, taken during the
period 2010-2014, revealed that within the non-protected area of core
zone, there was a trend towards decreasing peat swamp forest areas,
increasing land clearance, and increasing areas of community oilpalm
and rubber plantations. Fire was used for land clearing and most
of the forest fires occurred in the most populous area (the transition
area). The study found a relationship between the deforested/
degraded areas, and certain distance variables, i.e. distance from
roads, villages and the borders between the core area and the buffer
zone. The further the distance from the core area of the reserve, the
higher was the degree of deforestation and forest degradation. Research findings suggested that agricultural expansion may be
the direct cause of deforestation and forest degradation in the reserve,
whereas socio-economic factors were the underlying driver of forest
cover changes; such factors consisting of a combination of sociocultural,
infrastructural, technological, institutional (policy and governance), demographic (population pressure) and economic
(market demand) considerations. These findings indicated that local
factors/problems were the critical causes of deforestation and
degradation in GSKBB BR. This research therefore concluded that
reductions in deforestation and forest degradation in GSKBB BR
could be achieved through ‘local actor’-tailored approaches such as
community empowerment.
Abstract: The purpose of this study is the discrimination of 28
postmenopausal with osteoporotic femoral fractures from an agematched
control group of 28 women using texture analysis based on
fractals. Two pre-processing approaches are applied on radiographic
images; these techniques are compared to highlight the choice of the
pre-processing method. Furthermore, the values of the fractal
dimension are compared to those of the fractal signature in terms of
the classification of the two populations. In a second analysis, the
BMD measure at proximal femur was compared to the fractal
analysis, the latter, which is a non-invasive technique, allowed a
better discrimination; the results confirm that the fractal analysis of
texture on calcaneus radiographs is able to discriminate osteoporotic
patients with femoral fracture from controls. This discrimination was
efficient compared to that obtained by BMD alone. It was also
present in comparing subgroups with overlapping values of BMD.
Abstract: The use of titanium fluoride and iron fluoride
(TiF3/FeF3) catalysts in combination with polutetrafluoroethylene
(PTFE) in plain zinc- dialkyldithiophosphate (ZDDP) oil is important
for the study of engine tribocomponents and is increasingly a strategy
to improve the formation of tribofilm and provide low friction and
excellent wear protection in reduced phosphorus plain ZDDP oil. The
influence of surface roughness and the concentration of
TiF3/FeF3/PTFE were investigated using bearing steel samples
dipped in lubricant solution at 100°C for two different heating time
durations. This paper addresses the effects of water drop contact
angle using different surface; finishes after treating them with
different lubricant combination. The calculated water drop contact
angles were analyzed using Design of Experiment software (DOE)
and it was determined that a 0.05 μm Ra surface roughness would
provide an excellent TiF3/FeF3/PTFE coating for antiwear resistance
as reflected in the Scanning electron microscopy (SEM) images and
the tribological testing under extreme pressure conditions. Both
friction and wear performance depend greatly on the PTFE/and
catalysts in plain ZDDP oil with 0.05 % phosphorous and on the
surface finish of bearing steel. The friction and wear reducing effects,
which was observed in the tribological tests, indicated a better micro
lubrication effect of the 0.05 μm Ra surface roughness treated at
100°C for 24 hours when compared to the 0.1 μm Ra surface
roughness with the same treatment.
Abstract: This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
Abstract: In this paper, we present a new segmentation approach
for liver lesions in regions of interest within MRI (Magnetic
Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans
methodology, considers the parameter variable compactness
to handle uncertainty. Fine boundaries are detected by a local
recursive merging of ambiguous pixels with a sequential forward
floating selection with Zernike moments. The method has been tested
on both synthetic and real images. When applied on synthetic images,
the proposed approach provides good performance, segmentations
obtained are accurate, their shape is consistent with the ground truth,
and the extracted information is reliable. The results obtained on MR
images confirm such observations. Our approach allows, even for
difficult cases of MR images, to extract a segmentation with good
performance in terms of accuracy and shape, which implies that the
geometry of the tumor is preserved for further clinical activities (such
as automatic extraction of pharmaco-kinetics properties, lesion
characterization, etc.).
Abstract: Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
classification.
Abstract: Aurèsregion is one of the arid and semi-arid areas that
have suffered climate crises and overexploitation of natural resources
they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and
its spatiotemporal changes in the Aurès between 1987 and 2013, for
this work, we adopted a method of analysis based on the exploitation
of the images satellite Landsat TM 1987 and Landsat OLI 2013, from
the supervised classification likelihood coupled with field surveys of
the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover
maps from 1987 and 2013, one can extract a spatial map change of
different land cover units. The results show that between 1987 and
2013 vegetation has suffered negative changes are the significant
degradation of forests and steppe rangelands, and sandy soils and
bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013
allows us to understand the extensive or regressive orientation of
vegetation and soil, this map shows that dense forests give his place
to clear forests and steppe vegetation develops from a degraded forest
vegetation and bare, sandy soils earn big steppe surfaces that explain
its remarkable extension.
The analysis of remote sensing data highlights the profound
changes in our environment over time and quantitative monitoring of
the risk of desertification.
Abstract: Optic disk segmentation plays a key role in the mass
screening of individuals with diabetic retinopathy and glaucoma
ailments. An efficient hardware-based algorithm for optic disk
localization and segmentation would aid for developing an automated
retinal image analysis system for real time applications. Herein,
TMS320C6416DSK DSP board pixel intensity based fractal analysis
algorithm for an automatic localization and segmentation of the optic
disk is reported. The experiment has been performed on color and
fluorescent angiography retinal fundus images. Initially, the images
were pre-processed to reduce the noise and enhance the quality. The
retinal vascular tree of the image was then extracted using canny
edge detection technique. Finally, a pixel intensity based fractal
analysis is performed to segment the optic disk by tracing the origin
of the vascular tree. The proposed method is examined on three
publicly available data sets of the retinal image and also with the data
set obtained from an eye clinic. The average accuracy achieved is
96.2%. To the best of the knowledge, this is the first work reporting
the use of TMS320C6416DSK DSP board and pixel intensity based
fractal analysis algorithm for an automatic localization and
segmentation of the optic disk. This will pave the way for developing
devices for detection of retinal diseases in the future.
Abstract: In this paper, we present a four-step ortho-rectification
procedure for real-time geo-referencing of video data from a low-cost
UAV equipped with a multi-sensor system. The basic procedures for
the real-time ortho-rectification are: (1) decompilation of the video
stream into individual frames; (2) establishing the interior camera
orientation parameters; (3) determining the relative orientation
parameters for each video frame with respect to each other; (4)
finding the absolute orientation parameters, using a self-calibration
bundle and adjustment with the aid of a mathematical model. Each
ortho-rectified video frame is then mosaicked together to produce a
mosaic image of the test area, which is then merged with a well
referenced existing digital map for the purpose of geo-referencing
and aerial surveillance. A test field located in Abuja, Nigeria was
used to evaluate our method. Video and telemetry data were collected
for about fifteen minutes, and they were processed using the four-step
ortho-rectification procedure. The results demonstrated that the
geometric measurement of the control field from ortho-images is
more accurate when compared with those from original perspective
images when used to pin point the exact location of targets on the
video imagery acquired by the UAV. The 2-D planimetric accuracy
when compared with the 6 control points measured by a GPS receiver
is between 3 to 5 metres.
Abstract: Background subtraction and temporal difference are
often used for moving object detection in video. Both approaches are
computationally simple and easy to be deployed in real-time image
processing. However, while the background subtraction is highly
sensitive to dynamic background and illumination changes, the
temporal difference approach is poor at extracting relevant pixels of
the moving object and at detecting the stopped or slowly moving
objects in the scene. In this paper, we propose a simple moving object
detection scheme based on adaptive background subtraction and
temporal difference exploiting dynamic background updates. The
proposed technique consists of histogram equalization, a linear
combination of background and temporal difference, followed by the
novel frame-based and pixel-based background updating techniques.
Finally, morphological operations are applied to the output images.
Experimental results show that the proposed algorithm can solve the
drawbacks of both background subtraction and temporal difference
methods and can provide better performance than that of each method.
Abstract: In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
Abstract: The rapid growth of multimedia technology demands
the secure and efficient access to information. This fast growing lose
the confidence of unauthorized duplication. Henceforth the protection
of multimedia content is becoming more important. Watermarking
solves the issue of unlawful copy of advanced data. In this paper,
blind video watermarking technique has been proposed. A luminance
layer of selected frames is interlaced into two even and odd rows of
an image, further it is deinterlaced and equalizes the coefficients of
the two shares. Color watermark is split into different blocks, and the
pieces of block are concealed in one of the share under the wavelet
transform. Stack the two images into a single image by introducing
interlaced even and odd rows in the two shares. Finally, chrominance
bands are concatenated with the watermarked luminance band. The
safeguard level of the secret information is high, and it is
undetectable. Results show that the quality of the video is not
changed also yields the better PSNR values.
Abstract: Most of the oil palm plantations have been threatened
by Basal Stem Rot (BSR) disease which causes serious economic
impact. This study was conducted to identify the healthy and BSRinfected
oil palm tree using thirteen color indices. Multispectral and
thermal camera was used to capture 216 images of the leaves taken
from frond number 1, 9 and 17. Indices of normalized difference
vegetation index (NDVI), red (R), green (G), blue (B), near infrared
(NIR), green – blue (GB), green/blue (G/B), green – red (GR),
green/red (G/R), hue (H), saturation (S), intensity (I) and thermal
index (T) were used. From this study, it can be concluded that G
index taken from frond number 9 is the best index to differentiate
between the healthy and BSR-infected oil palm trees. It not only gave
high value of correlation coefficient (R=-0.962), but also high value
of separation between healthy and BSR-infected oil palm tree.
Furthermore, power and S model developed using G index gave the
highest R2 value which is 0.985.
Abstract: Land Use Land Cover (LULC) changes due to human
activities and natural causes have become a major environmental
concern. Assessment of temporal remote sensing data provides
information about LULC impacts on environment. Land Surface
Temperature (LST) is one of the important components for modeling
environmental changes in climatological, hydrological, and
agricultural studies. In this study, LULC changes (September 7, 1984
and July 8, 2014) especially in agricultural lands together with
population changes (1985-2014) and LST status were investigated
using remotely sensed and census data in South Marmara Watershed,
Turkey. LULC changes were determined using Landsat TM and
Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM
and OLI images were classified using supervised classification
method to prepare LULC map including five classes including Forest
(F), Grazing Land (G), Agricultural Land (A), Water Surface (W),
Residential Area-Bare Soil (R-B) classes. The LST image was also
derived from thermal bands of the same dates.
LULC classification results showed that forest areas, agricultural
lands, water surfaces and residential area-bare soils were increased as
65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In
comparison, a dramatic decrement occurred in grazing land (107985
ha) within three decades. The population increased 29% between
years 1984-2014 in whole study area. Along with the natural causes,
migration also caused this increase since the study area has an
important employment potential. LULC was transformed among the
classes due to the expansion in residential, commercial and industrial
areas as well as political decisions. In the study, results showed that
agricultural lands around the settlement areas transformed to
residential areas in 30 years.
The LST images showed that mean temperatures were ranged
between 26-32°C in 1984 and 27-33°C in 2014. Minimum
temperature of agricultural lands was increased 3°C and reached to
23°C. In contrast, maximum temperature of A class decreased to
41°C from 44°C. Considering temperatures of the 2014 R-B class and
1984 status of same areas, it was seen that mean, min and max
temperatures increased by 2°C.
As a result, the dynamism of population, LULC and LST resulted
in increasing mean and maximum surface temperatures, living
spaces/industrial areas and agricultural lands.
Abstract: Perception, evaluation and representation of the
environment have been the subject of many disciplines including
psychology, geography and architecture. In environmental and social
psychology literature there are several evidences which suggest that
cognitive representations about a place consisted of not only
geographic items but also social and cultural. Mental representations
of residence area or a country are influenced and determined by
social-demographics, the physical and social context. Thus, all
mental representations of a given place are also social
representations. Cognitive maps are the main and common
instruments that are used to identify spatial images and the difference
between physical and subjective environments. The aim of the
current study is investigating the mental and social representations of
Turkey in university students’ minds. Data was collected from 249
university students from different departments (i.e. psychology,
geography, history, tourism departments) of Ege University.
Participants were requested to reflect Turkey in their mind onto the
paper drawing sketch maps. According to the results, cognitive maps
showed geographic aspects of Turkey as well as the context of
symbolic, cultural and political reality of Turkey. That is to say, these
maps had many symbolic and verbal items related to critics on social
and cultural problems, ongoing ethnic and political conflicts, and
actual political agenda of Turkey. Additionally, one of main
differentiations in these representations appeared in terms of the East
and West side of the Turkey, and the representations of the East and
West was varied correspondingly participants’ cultural background,
their ethnic values, and where they have born. The results of the
study were discussed in environmental and social psychological
perspective considering cultural and social values of Turkey and
current political circumstances of the country.
Abstract: This paper presents a new automatic vehicle detection
method from very high resolution aerial images to measure traffic
density. The proposed method starts by extracting road regions from
image using road vector data. Then, the road image is divided into
equal sections considering resolution of the images. Gradient vectors
of the road image are computed from edge map of the corresponding
image. Gradient vectors on the each boundary of the sections are
divided where the gradient vectors significantly change their
directions. Finally, number of vehicles in each section is carried out
by calculating the standard deviation of the gradient vectors in each
group and accepting the group as vehicle that has standard deviation
above predefined threshold value. The proposed method was tested in
four very high resolution aerial images acquired from Istanbul,
Turkey which illustrate roads and vehicles with diverse
characteristics. The results show the reliability of the proposed
method in detecting vehicles by producing 86% overall F1 accuracy
value.
Abstract: In addition to environmental parameters like rain,
temperature diseases on crop is a major factor which affects
production quality & quantity of crop yield. Hence disease
management is a key issue in agriculture. For the management of
disease, it needs to be detected at early stage. So, treat it properly &
control spread of the disease. Now a day, it is possible to use the
images of diseased leaf to detect the type of disease by using image
processing techniques. This can be achieved by extracting features
from the images which can be further used with classification
algorithms or content based image retrieval systems. In this paper,
color image is used to extract the features such as mean and standard
deviation after the process of region cropping. The selected features
are taken from the cropped image with different image size samples.
Then, the extracted features are taken in to the account for
classification using Fuzzy Inference System (FIS).
Abstract: High resolution images are always desired as they contain the more information and they can better represent the original data. So, to convert the low resolution image into high resolution interpolation is done. The quality of such high resolution image depends on the interpolation function and is assessed in terms of sharpness of image. This paper focuses on Wavelet based Interpolation Techniques in which an input image is divided into subbands. Each subband is processed separately and finally combined the processed subbandsto get the super resolution image.
Abstract: In present study, it was aimed to determine potential
agricultural lands (PALs) in Gokceada (Imroz) Island of Canakkale
province, Turkey. Seven-band Landsat 8 OLI images acquired on
July 12 and August 13, 2013, and their 14-band combination image
were used to identify current Land Use Land Cover (LULC) status.
Principal Component Analysis (PCA) was applied to three Landsat
datasets in order to reduce the correlation between the bands. A total
of six Original and PCA images were classified using supervised
classification method to obtain the LULC maps including 6 main
classes (“Forest”, “Agriculture”, “Water Surface”, “Residential Area-
Bare Soil”, “Reforestation” and “Other”). Accuracy assessment was
performed by checking the accuracy of 120 randomized points for
each LULC maps. The best overall accuracy and Kappa statistic
values (90.83%, 0.8791% respectively) were found for PCA images
which were generated from 14-bands combined images called 3-
B/JA.
Digital Elevation Model (DEM) with 15 m spatial resolution
(ASTER) was used to consider topographical characteristics. Soil
properties were obtained by digitizing 1:25000 scaled soil maps of
Rural Services Directorate General. Potential Agricultural Lands
(PALs) were determined using Geographic information Systems
(GIS). Procedure was applied considering that “Other” class of
LULC map may be used for agricultural purposes in the future
properties. Overlaying analysis was conducted using Slope (S), Land
Use Capability Class (LUCC), Other Soil Properties (OSP) and Land
Use Capability Sub-Class (SUBC) properties.
A total of 901.62 ha areas within “Other” class (15798.2 ha) of
LULC map were determined as PALs. These lands were ranked as
“Very Suitable”, “Suitable”, “Moderate Suitable” and “Low
Suitable”. It was determined that the 8.03 ha were classified as “Very
Suitable” while 18.59 ha as suitable and 11.44 ha as “Moderate
Suitable” for PALs. In addition, 756.56 ha were found to be “Low
Suitable”. The results obtained from this preliminary study can serve
as basis for further studies.
Abstract: In this paper, we are interested in the problem of
finding similar images in a large database. For this purpose we
propose a new algorithm based on a combination of the 2-D
histogram intersection in the HSV space and statistical moments. The
proposed histogram is based on a 3x3 window and not only on the
intensity of the pixel. This approach overcome the drawback of the
conventional 1-D histogram which is ignoring the spatial distribution
of pixels in the image, while the statistical moments are used to
escape the effects of the discretisation of the color space which is
intrinsic to the use of histograms. We compare the performance of
our new algorithm to various methods of the state of the art and we
show that it has several advantages. It is fast, consumes little memory
and requires no learning. To validate our results, we apply this
algorithm to search for similar images in different image databases.