Retrieving Unrecognized Objects from HSV into JPEG Video at various Light Resolutions by ijcsiseditor


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									                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                             Vol. 9, No. 10, October 2011

              Retrieving unrecognized objects from HSV into jpeg video
                              at various light resolutions
                 T. Arumuga Maria Devi 1                    Nallaperumal Krishnan2                     K.K Sherin 3
           Assistant Professor, Dept. of CITE           HOD, SMIEEE, Dept. of CITE             P G Scholar, Dept. of CITE
                                            Centre for Information Technology and Engineering
                                            Manonmaniam Sundaranar University, Tirunelveli.
              Email: 1.            Email: 2.      Email: 3.
                  Phone No:9677996337                       Phone No: 9443117397                 Phone No:9442055500

Abstract        This paper deals mainly with the performance study and       sequence of frames of still images as in figure 1. Generally,
analysis of image retrieval techniques for retrieving unrecognized objects   two types of image frames are defined: Intra-frames (I-frames)
from an image using Hyper spectral camera at low light resolution. Since     and Inter-frames (P- frames). I-frames are treated as
the identification of moving object in a camera is not possible in a low     independent key images and P-frames are treated as Predicted
light environment as the object has low reflectance due to lack of lights.
Using Hyper spectral data cubes, each object can be identified on the        frames. An obvious solution to video compression would be
basis of object luminosity. Moving object can be identified by identifying   predictive coding of P-frames based on previous frames and
the variation in frame value. The main work identified are that efficient    compression is made by coding the residual error. Temporal
retrieval of unrecognized objects in an image will be made possible using    redundancy removal is included in P-frame coding, whereas I-
Hyper spectral analysis and various other methods such as Estimation of
Reflectance, Feature and mean shift tracker, Traced feature located on       frame coding performs only spatial redundancy removal.
image, Band pass filter (Background removal) etc. These methods used
above to retrieve unrecognized object from a low light resolution are
found to be more efficient in comparison with the other image retrieval
techniques.                                                                                         II. TECHNIQUE
           Keywords        Anomaly suspect, mean shift algorithms,               The problem laid in the past decades in identifying the
spectral detection, .
                                                                             unrecognized objects from a low light resolution. If the image
                        I. INTRODUCTION                                      is created from a hyper spectral camera, the problem still laid
                                                                             in identifying what actually the object was, since the hyper

   T       he process of recovering unrecognized objects from
           an image in low light is a trivial task which finds its
           need in recognizing objects from a distant location.
Since there is a need in retrieving unrecognized objects from
the image, some form of object extraction method from an
                                                                             spectral image detects only the presence of an object, not what
                                                                             an object actually is. Various reflectance [24] methods were
                                                                             used in order to obtain the specific property of the image. But
                                                                             since the above methods does not specify what the object
                                                                             property was, there should be a method in order to specify
image is necessary. The application of detecting objects from                what the object in an image actually was. Since the image
an image is as follows. Here, we focus on the problem of                     taken from a hyper spectral camera suffers from low
tracking objects through challenging conditions, such as                     resolution, we could not identify what actually the particular
tracking objects at low light where the presence of the object               object was, even though it detects the presence of an object.
is difficult to identify. For example, an object which is fastly             There is a need for image applications in the detection of
moving on a plane surface in an abrupt weather condition is                  objects from a distant location. Normally, the image would be
normally difficult to identify. A new framework that                         such that the presence of an object could not be detected from
incorporates emission theory to estimate object reflectance                  it. But, from a hyper spectral camera, the object, if it was on
and the mean shift algorithm to simultaneously track the                     that location, could be captured in the hyper spectral camera.
object based on its reflectance spectra is proposed. The                     Also, an image taken from a hyper spectral camera suffers
combination of spectral detection and motion prediction                      from low resolution and thus does not show the exact
enables the tracker to be robust against abrupt motions, and                 properties of an image. Since the identification of moving
facilitate fast convergence of the mean shift tracker. Video                 object in a camera is not possible from distant location, to
images are moving pictures which are sampled at frequent                     overcome this problem we can use Hyper spectral camera to
intervals usually, 25 frames per second and stored as sequence               identify the object.. Thus, the problem areas are such that
of frames. A problem, however, is that digital video data rates              there should be a methodology in identifying an object from a
are very large, typically in the range of 150 Megabits/second.               low light resolution. That is, it should detect the points from a
Data rates of this magnitude would consume a lot of the                      hyper spectral image which are the points that specify the
bandwidth in transmission, storage and computing resources                   particular objects in the image by reflectance mechanisms of
in the typical personal computer. Hence, to overcome these                   the object. The next problem is such that if an object is fastly
issues, Video Compression standards have been developed                      moving on a plane surface, it is not necessary that the object
and intensive research is going on to derive effective                       will be present on every frame. The points that resembles the
techniques to eliminate picture redundancy, allowing video                   object in the hyper spectral image should be able to be used in
information to be transmitted and stored in a compact and                    retrieving the objects by using background removal. Related
efficient manner[6].A video image consists of a time-ordered
                                                                             to the implementation of transcoding, the work is as follows .

                                                                                                        ISSN 1947-5500
                                                    (IJCSIS) International Journal of Computer Science and Information Security,
                                                    Vol. 9, No. 10, October 2011

The objective of this work is to study the relationship between   adopted as part of the JPEG 2000 standard [8] and for still
the operational domains for prediction, according to temporal     image texture coding in the MPEG-4 standard.
redundancies between the sequences to be encoded. Based on
the motion characteristics of the inter frames, the system will   Temporal Residual Prediction
adaptively select the spatial or wavelet domain for prediction.
Also the work is to develop a temporal predictor which                Motion estimation obtains the motion information by
exploits the motion information among adjacent frames using       finding the motion field between the reference frame and the
extremely low side information.                                   current frame. It exploits temporal redundancy of video
The proposed temporal predictor has to work without the           sequence, and, as a result, the required storage or transmission
requirement of the transmission of complete motion vector set     bandwidth is reduced by a factor of four. Block matching is
and hence much overhead would be reduced due to the               one of the most popular and time consuming methods of
omission of motion vectors.                                       motion estimation. This method compares blocks of each
                                                                  frame with the blocks of its next frame to compute a motion
Adaptive Domain Selection                                         vector for each block; therefore, the next frame can be
                                                                  generated using the current frame and the motion vectors for
    This step aims to determine the operational mode of video     each block of the frame. Block matching algorithm is one of
sequence compression according to its motion characteristics.     the simplest motion estimation techniques that compare one
The candidate operational modes are spatial domain and            block of the current frame with all of the blocks of the next
wavelet domain. The wavelet domain is extensively used for        frame to decide where the matching block is located.
compression due to its excellent energy compaction.               Considering the number of computations that has to be done
However, it is pointed out that motion estimation in the          for each motion vector, each frame of the video is partitioned
wavelet domain might be inefficient due to shift invariant        into search windows of size H*W pixels. Each search window
properties of wavelet transform. Hence, it is unwise to predict   is then divided into smaller macro blocks of size 8*8 or 16*16
all kinds of video sequences in the spatial domain alone or in    pixels. To calculate the motion vectors, each block of the
the wavelet domain alone. Hence a method is introduced to         current frame must be compared to all of the blocks of the
determine the prediction mode of a video sequence adaptively      next frame with in the search range and the Mean Absolute
according to its temporal redundancies. The amount of             Difference (MAD) for each matching block is calculated.
temporal redundancy is estimated by the inter frame               Where N*N is the block size, x(i,j) is the pixel values of
correlation coefficients of the test video sequence. The inter    current frame at (i,j) th position and y(i+m,j+n) is the pixel
frame correlation coefficient between frames can be               value of reference frame at (i+m,j+n) th position. The block
calculated. If the inter frame correlation coefficients are       with the minimum value of the Mean Absolute Difference
smaller than a predefined threshold, then the sequence is         (MAD) is the preferred matching block. The location of that
likely to be a high motion video sequence. In this case, motion   block is the motion displacement vector for that block in
compensation and coding the temporal prediction residuals in      current frame. The motion activities of the neighboring pixels
wavelet domain would be inefficient; therefore, it is wise to     for aspecific frame are different but highly correlated since
operate on the sequence in the spatial mode. Those sequences      they usually characterize very similar motion structures.
that have larger inter frame correlation coefficients are         Therefore, motion information of the pixel p i(x,y) can be
predicted in direct spatial domain. The frames that have more     approximated by the neighboring pixels in the same frame.
similarities with very few motion changes are coded using         The initial motion vector (Vx, Vy) of the current pixel is
temporal prediction in integer wavelet domain.                    approximated by the motion activity of the upper-left
                                                                  neighboring pixels in the same frame.
Discrete Wavelet Transform
                                                                  Coding the Prediction Residual
   Discrete Wavelet Transform (DWT) is the most popular
transform for image-based application [14], [16], [18]. A 2-         The temporal prediction residuals from adaptive prediction
dimensional wavelet transform is applied to the original image    are encoded using Huffman codes. Huffman codes are used
in order to decompose it into a series of filtered sub band       for data compression that will use a variable length code
images. At the top left of the image is a low-pass filtered       instead of a fixed length code, with fewer bits to store the
version of the original and moving to the bottom right, each      common characters, and more bits to store the rare characters.
component       contains      progressively    higher-frequency   The idea is that the frequently occurring symbols are assigned
information that adds the detail of the image. It is clear that   short codes and symbols with less frequency are coded using
the higher-frequency components are relatively sparse, i.e.,      more bits. The Huffman code can be constructed using a tree.
many of the coefficients in these components are zero or          The probability of each intensity level is computed and a
insignificant. The wavelet transform is thus an efficient way     column of intensity level with descending probabilities is
of decorrelating or concentrating the important information       created. The intensities of this column constitute the levels of
into a few significant coefficients. The wavelet transform is     Huffman code tree. At each step the two tree nodes having
particularly effective for still image compression and has been   minimal probabilities are connected to form an intermediate

                                                                                               ISSN 1947-5500
                                                       (IJCSIS) International Journal of Computer Science and Information Security,
                                                       Vol. 9, No. 10, October 2011

node. The probability assigned to this node is the sum of                                         IV. FIGURES
probabilities of the two branches. The procedure is repeated
until all branches are used and the probability sum is 1.Each
edge in the binary tree, represents either 0 or 1, and each leaf
corresponds to the sequence of 0s and 1s traversed to reach a
particular code. Since no prefix is shared, all legal codes are at
the leaves, and decoding a string means following edges,
according to the sequence of 0s and 1s in the string, until a
leaf is reached. The code words are constructed by traversing
the tree from root to its leaves. At each level 0 is assigned to
the top branch and 1 to the bottom branch. This procedure is
repeated until all the tree leaves are reached. Each leaf
corresponds to a unique intensity level. The codeword for
each intensity level consists of 0s and 1s that exist in the path
from the root to the specific leaf.

                                                                                       Figure 1. Background removed from a frame
                         III. DATA
   The problem areas are divided as follows:
         1.   Identifying objects in skylight (during night)
         2.   To ensure frame clarity

    The problems related to identifying the object at skylight is
handled by the following methods: The first method uses the
reflection property of the objects. Since the reflection
properties of various objects are different, then it means that
various emissions are been made by different objects and by
this way, the objects can be identified by these different
energy emissions. The second method such as the spectral
feature analysis is used to analyze the spectral images. This is                    Figure 2. Background removed from another frame
used to identify the background from the object since the
background is a constant. The third method is mean shift
tracking algorithm. This is used to identify the presence of the
object in different frames to know whether the object is
moving or not. The fourth method is the tracking algorithm
which is used to detect the background and the objects in
order to know the presence of objects. The fifth method such
as target representation is used to detect the object at a
particular target. It uses methods which compares the
threshold values to distinguish between background and the
object in order to identify it. The threshold value will be set to
a value. If the value is less than the threshold, then it will be a
background else it will be an object.

   Lossless JPEG transcoding has many other relevant                                             Figure 3. Object tracing
applications besides reencoding and rotating. For example, it
can be used by editing software to avoid a quality loss in the
unedited parts of the image. With some additional
modifications, it can also be used to perform other simple
geometric transformations on JPEG compressed images, like
cropping or mirroring. Usage of the JPEG file format and the
Huffman encoding, nothing else from the JPEG algorithm,
therefore the compression scheme is lossless.

                                                                                                    ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 9, No. 10, October 2011

                                                                                          Figure 8. Original Frame used to track object
        Figure 4. Tracking the moving object

                Figure 5. Final result                                                    Figure 9. Replicate image used to track object

                                                                                              V. CONCLUSIONS
                                                                          The classification problem of objects is handled by local
                                                                       detection method to identify the characteristics of the object.
                                                                       Local detection is made by superimposing the points obtained
                                                                       from the hyper spectral image into the high-resolution image
                                                                       there by obtaining the characteristics of the object. Since an
                                                                       accuracy of what object has been identified was not possible
                                                                       on previous methods, a threshold value is set to identify the
                                                                       background with other objects. The image is first converted
                                                                       from RGB to Gray Scale. Then the pixel values of the image
      Figure 6. Tracking of objects in the frame                       are compared with a threshold value. If the pixel value of the
                                                                       image is below the threshold value, then it is set as a
                                                                       background and is set to 0, else the pixel value is taken as the
                                                                       pixel value for an object and is set to 1. Thus we get an image
                                                                       with unnecessary objects removed by setting it as background
                                                                       and the presence of the object in the image is only shown
                                                                       ensuring frame clarity. To ensure that the frames when send to
                                                                       the receiver will contain smother edges for objects, trans
                                                                       coding technique is applied. It uses the concept of replicate
                                                                       array with filter array in order to ensure that the frames are
                                                                       send correctly at the receiver making the object in each frame
                                                                       more identifiable. This ensures that the frames when send
                                                                       from the source will be correctly received at the receiver.

Figure 7. Object discrimination by size and brightness

                                                                                                       ISSN 1947-5500
                                                                (IJCSIS) International Journal of Computer Science and Information Security,
                                                                Vol. 9, No. 10, October 2011

                           ACKNOWLEDGMENT                                       [16] C. Shan, Y. Wei, T. Tan, and F. Ojardias. Real time hand tracking by
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of CITE, M S University, Tirunelveli for various algorithms
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used in this research, and all the people who helped in                         from hyperspectral remotely sensed data. GRS, IEEE Transactions on,
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Johns Hopkins University.

[2] Transform Coding techniques for lossy hyper spectral data compression                                         T. Arumuga Maria Devi received B.E.
Barbara Penna, Tammam Tillo, Enrico Magli, Gabriela Olmo, Members IEEE                                            Degree in Electronic and Communication
                                                                                                                  Engineering        from      Manonmaniam
[3] Simple Fast and Adaptive Lossless Image Compression Algorithm                                                 Sundaranar University, Tirunelveli India in
Roman Starosolski, 2007, 37(1):65-91, DOI: 10.1002/spe.746                                                        2003, M.Tech degree in Computer and
                                                                                                                  Information          Technology         from
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Hewlett-Packard Laboratories, Palo Alto, CA 4304, USA., Guillermo Sapiro.,                                        doing Ph.D in Computer and Information
Department of Electrical and Computer Engineering University of Minnesota,                                        Technology and also the Assistant Professor
Minneapolis, MN 55455, USA.                                                                                       of Centre for Information Technology and
                                                                                                                  Engineering of Manonmaniam Sundaranar
[5] Recent trends in image compression and its applications                                                       University. Her research interests include
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SYSTEMS CONFERENCE, NSC 2008, December 17-19, 2008                                                                 and Remote Communication.
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Washington                                                                                                          Nallaperumal Krishnan received M.Sc.
                                                                                                                    degree in Mathematics from Madurai
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modified coding framework of H.264/AVC                                                                              M.Tech degree in Computer and
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