SHARK FISH CLASSIFICATION THROUGH IMAGE PROCESSING USING WAVELET TRANSFORMATION AND ENHANCED EDGE-DETECTION TECHNOLOGY
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ISSN:2229-6093
G.T.Shrivakshan et al, Int. J. Comp. Tech. Appl., Vol 2 (4), 773-783
SHARK FISH CLASSIFICATION THROUGH IMAGE PROCESSING USING
WAVELET TRANSFORMATION AND ENHANCED EDGE-DETECTION
TECHNOLOGY
G.T.Shrivakshan1 Dr.C. Chandrasekar 2
Department of Computer Science, Associate Professor, Periyar University
Swami Vivekananda Arts & Science college, Salem, TamilNadu, India.
Villupuram,TamilNadu, India. ccsekar@gmail.com
srivakshan@gmail.com
Abstract
This paper proposes a novel way of classifying discrimination, chromosome shape
shark fishes based on image processing using discrimination, optical character recognition,
Wavelet Transformation for detecting the texture discrimination, and speech recognition.
edges, specially the two dimensional Haar In this paper it narrow downs fish image
wavelet transformation of images. In this paper classification system that is proposed in this
the morphological features of different types of work. Digital image recognition has been
sharks compared with the given sample shark extremely found and studied. Various
that is being identified to which category it approaches in image processing and pattern
belongs to. Applying the wavelet recognition have been developed by scientists
transformation which incorporates the concept and engineers to solve this problem [7]. That is
of multi-threading. The paper proposes the because it has an importance in several fields.
enhanced edge detection technology and uses In this system for recognizing a fish image is
the concept of concurrency to identify the built, which may be benefited by the various
shark image. fields, the system concerning an isolated
pattern of interest, the input is considered to be
Keywords
an image of specific size and format, the image
Image processing, Haar wavelet
is processed and then recognized the given
transformation, edge-detection, multi- shark fish into its cluster and Categorize from
threading. the clustered fish. In this study we narrow
down only with the shark fish and its different
1. Introduction types. The various shark fishes are clustered
into groups. The proposed system recognizes
This paper mainly focuses on the various
the isolated pattern of shark fish which is
works that has been done by depending on the
consisting of its morphological features by
computer image processing[9][10]. In order to
which it is identified. As the system acquire an
let the processing time to be reduced and to
image consisting pattern of shark fish then, the
provide more results which are accurate, for
image will be processed into several phases
example, depending on different types of data,
such as edge-detection and identifying the fish
such as digital image, characters and digits. In
with morphological feature extraction before
order to automate system that deals with
recognizing the pattern of the shark fish given
Fingerprint verification, face recognition, iris
in fig: 1
.
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ISSN:2229-6093
G.T.Shrivakshan et al, Int. J. Comp. Tech. Appl., Vol 2 (4), 773-783
Fig: 1 Morphological Feature of Shark Fish
2. Problem description domain and considered as a potential research in utilizing the
existing technology for encouraging and pushing the agriculture
researches ahead. Although advancements have been made in the
2.1 Review of image processing Algorithm areas of developing real time data collection and on improving
The Main objective is to extract knowledge from the previous range resolutions, existing systems are still limited in their ability to
studies, several hard efforts have been taken to recognize the digital detect or classify shark fish, despite the widespread development in
image but still it is an unresolved problem. Due to distortion, noise, the world of computers and software. There is a difficulty in
segmentation errors, overlap, and occlusion of objects in color identifying the different types of sharks. The Object classification
images [5]. Recognition and classification as a technique gained a problem lies at the core of the task of estimating the prevalence of
lot of attention in the last decade wherever many scientists utilize each shark fish species. The classification is made by analyzing fish
these techniques in order to enhance the scientific fields. Fish with the following features
recognition and classification still active area in the agriculture
1) Maxillary various sources as well as by distortions and aberrations in the
2) Mouth optical system, Segmentation failures, due to its inherent difficulty,
3) Mandible segmentation may become unreliable or fail completely, To resolve
4) Eye the identify the shark from its different types, in this paper it collects
5) Operculum the information about the various sharks and its character features
6) Pectoral Fin which are displayed in table.1.
7) Scale The various shark fishes with different morphological features and
8) Pelvic Fin inhabitation, though some sharks look identical it is a variety that
9) Anal Fin has to be processed using the image processing before identifying
10) Lateral Line the shark. There are many varieties of shark but in this paper it
11) Spiny ray pertains with ten different sharks depending on its similarities. Table
12) Dorsal Fin 1 clearly depicts the various sharks which are taken as samples and
13) Caudal Fin its morphological features are distinctly specified. This gives the
Shark fish Feature variability: some features may present large insight view about sharks which is the endangered species of the
differences among different shark fishes, Environmental changes, sea. The image processing concept mainly deals with two aspects
variations in illumination parameters, such as power and color and first is the edge detection of the shark and its different types
water characteristics, such as turbidity, temperature, not uncommon. followed by the Haar two dimensional wave transformations for
The environment can be either outdoor or indoor, Poor image predicting the image more accurately.
quality, image acquisition process can be affected by noise from
Table 1
Type of the
Characteristic features
shark
The basking • large gill slits and gill rakers
shark • teeth minute and numerous
• large conical snout
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G.T.Shrivakshan et al, Int. J. Comp. Tech. Appl., Vol 2 (4), 773-783
Atlantic • May have black edged dorsal
Sharpnose and caudal fin
Shark • Long labial furrows around
corners of mouth
• Nictitating membrane over eye
Oceanic • White tipped fins
Whitetip • Broad rounded first dorsal fin
Shark
• Large paddle like pectoral fins
• Nictitating membrane over eye
Spiny • No anal fin
Dogfish • Spines in front of each dorsal
Shark fin
• Irregular white spots present
on sides and back of the body
• Strongly oblique teeth in both
jaws, with single cusp
• No subterminal notch on
caudal fin
• Pectoral fins with curved rear
margins
• Narrow anterior nasal flap
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Smooth • Can change colour
Dogfish • No dorsal fin spines
Shark
• Prominent spiracle behind eye
• Numerous small blunt teeth in both
jaws
Portuguese • Colouration: juveniles are dark blue,
Shark half grown
• individuals are black and adults are
brown
• No anal fin
• Inconspicuous dorsal fin spines
• Teeth with single cusp; upper teeth
long and pointed, lower teeth short,
broad and strongly oblique
• Large, scale-like dermal denticles
Rough • Uncertain if it has luminescent
photophores
Sagre
Shark • No anal fin
• Dorsal fin spines
• Thorn-like, nearly erect dermal
denticles
• Upper teeth with 5 cusps, lower teeth
oblique with single cusp
Porbeagle • White patch on the trailing edge of
Shark the first dorsal fin
• Caudal fin with secondary keel
• Lateral denticles on the teeth
• Lunate tail
3. Methodology
derivative of the image to find edges. This first figure shows the
There are many ways to perform the edge detection.
edges of an image detected using the gradient method (Roberts,
However, the most may be grouped into two categories, gradient
Prewitt, Sobel) and the Laplacian method (Marrs-Hildreth). It can
and Laplacian. The gradient method detects the edges by looking for
then compare the feature extraction using the Sobel edge detection
the maximum and minimum in the first derivative of the image. The
with the feature extraction using the Laplacian.
Laplacian method searches for zero crossings in the second
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Fig.2. Various Edge Detection Filters
It seems that although it does do better for some features translations of features. Another method of detecting edges is using
(i.e. the fins), it still suffers from mismapping some of the lines. A wavelets. Specifically a two-dimensional Haar wavelet transform[4]
morph constructed using individually selected points would still of the image produces essentially edge maps of the vertical,
work better. It should also be noted that this method suffers the horizontal, and diagonal edges in an image. This can be seen in the
same drawbacks as the previous page, difficulties due to large figure 9 and 10.
contrast between images and the inability to handle large
Fig 3. Haar Wavelet Transformed Image.
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Fig 4 Edge Images Generated from the Haar Wavelet Transform.
Although the Haar filter is nearly equivalent to the gradient and 3. The average of each set is computed.
Laplacian edge detection methods, it does offer the ability to easily a) m1 = average value of G1
extend our edge detection to multiscales as demonstrated in this b) m2 = average value of G2
figure 3 and 4.
4. A new threshold is created that is the average of m1 and
Generally the threshold value has to be randomly chosen but to m2
overcome this limitation in this paper, It formulates a new method a) T’ = (m1 + m2)/2
for finding the initial threshold value [14].
5. Go back to step two, now using the new threshold
In this algorithm instead of choosing a random value as threshold
computed in step four, keep repeating until the new
which may not lead to right prediction, in this paper it proposes a
threshold matches the one before it (i.e. until convergence
new way of taking the threshold value as one of the edge pixel
has been reached).
which has high intensity which is more advantages in edge detection
technology.
This iterative algorithm is a special one-dimensional case of the
Algorithm for finding the threshold value in the Wavelet enhanced k-means clustering algorithm, which has been proven to
Transformation: converge at a local minimum—meaning that a different initial
threshold may give a different final result.
1. An initial threshold (T) is chosen, this can be done by
taking one of the edge pixels which has high intensity. In this wavelet algorithm it imposes multi-threading concept which
is the modification concept that is done in the existing algorithm to
2. The image is segmented[8] into object and background identify the shark image in this work.
pixels as described above, creating two sets:
a) G1 = {f(m,n):f(m,n)>T} (object pixels) The Wavelet transformation is being applied in the sample shark
b) G2 = {f(m,n):f(m,n) T} (background pixels) fish. The steps are depicted in the DFD given below which is
(note, f(m,n) is the value of the pixel located in applying a new concept for finding the threshold value.
the mth column, nth row)
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START
IMAGE AS INPUT (WITH NOISE)
WAVELET TRANSFORMATION (DWT)
THRESHOLD
IMAGE AS INPUT (WITH NOISE)
QUANTIZER
IMAGE AS INPUT (WITH NOISE)
LOSSLESS ENCODER
LOSSLESS DECODER
DEQUANTIZER
INVERSE WAVELET TRANSFORMATION (IDWT)
IMAGE AS OUTPUT (WITHOUT NOISE)
END
Fig 5. Wavelet Transformation DFD
In this algorithm it uses the concept of multi-threading so that as Step 2. Single level wavelet decomposition of LL(c-1)I and apply
sample shark fish can be at a time compared with several shark thresholding on obtained three subbands HL, HH, LH.Find
fishes if the mismatch is detected at that junction the thread stops significant coefficient (after thresholding on three subbands) and
otherwise the thread continues its execution. The thread can be apply VQ using MFOCPN[1] for coding.
started or stopped at any time which gives an advantage in finding Step 3. Cosine Interpolate the reconstructed LLc to the size
the identical shark. (M/2c-1) x (N/2c-1) to get LL(c-1)I .
Step 4. Decode HL, HH, LH using MFOCPN decoder[11].
Step 5. Take LLc and HL, HH, LH from Step 3 and apply inverse
Algorithm-coding for Wavelet decomposition of wavelet transform (IDWT) with these four subbands and obtain
image: image I of size (M/2c-1) x (N/2c-1).
Step 6. Change c = k-1 and LLc = I (from Step 5) and if c = 0 go
Step 1. Wavelet decomposition of image for level k, and assign to Step 6 else go to Step 3.
count c = k. simultaneously check for the pattern in each Step 7. Stop.
decomposition concurrently using the concept of mult-threading.
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Modified Forward Only Counter propagation[3][6] Neural Network variants of CPN are of two types, they are forward counter
(Mfocpn) The counter propagation network is a hybrid network, and propagation and full counter propagation. The CPN has three layers
called to be a self organizing loop, having the characteristic of both namely input, instar and outstar is given in Fig (6).
self organizing map (SOM) and feed forward neural network. The
Fig.6. CPN Architecture
The notation L and H represents low pass and high pass filters 4. RESULTS AND DISCUSSIONS
respectively and the LLi, LHi, HLi,HHi, are the filters where first
letter denotes the vertical order (i.e.) the filter applied to rows and Various Edge Detection Filters
second letter denotes the horizontal order (i.e.) the filter applied to
columns. The advantage of high pass component is that it reduces Notice that the shark features (fins, tails, gills and mouth) have very
the computational time. The levels of decomposition make the sharp edges. These also happen to be the best reference points for
compression efficient. Quantizer[2][12] reduces the number of bits identifying between two images. Notice also that the Marr-Hildreth
needed to store the transformed coefficients[13]. It is considered as not only has a lot more noise than the other methods, the low-pass
many to one mapping. filtering it uses distorts the actual position of the facial features. Due
to the nature of the Sobel and Prewitt filters we can select out only
vertical and horizontal edges of the image as shown in fig.9 and 10.
Fig.7. Various Edge Detection Filters
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(a) Shark image (b) Edges using canny detector
Fig.8A
(c) Shark image with noise (d) Edges from the image with noise
FIG.8B
The next pair of images shows the horizontal and vertical shark features, such as the gills,mouth,fins and tails of different
edges selected out of the group shark images with the Sobel method sharks.
of edge detection. You will notice the difficulty it had with certain
Fig.9.Vertical Sobel Filter
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Fig.10.Horizonatal Sobel Filter
Other Methods of Edge Detection searches for zerocrossings in the second derivative of the image to
find edges. This figure 2 shows the edges of an image detected
There are many ways to perform edge detection. However, the most
using the gradient method (Roberts, Prewitt, Sobel) and the
may be grouped into two categories, gradient and Laplacian. The
Laplacian method (Marrs-Hildreth).
gradient method detects the edges by looking for the maximum and
minimum in the first derivative of the image. The Laplacian method
Fig.11.Target Image
Fig.12. Original Image with crossing Lines
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Fig.11. The Final Horizontal and vertical pair of edges which helps to identify the shark fish
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