SCALE INVARIANT FEATURE EXTRACTION FOR IDENTIFYING AN OBJECT IN THE IMAGE USING MOMENT INVARIANTS

Transcription

1 Research Article SCALE INVARIAN FEAURE EXRACION FOR IDENIFYING AN OBJEC IN HE IMAGE USING MOMEN INVARIANS 1 R.Muralidharan, Dr.C.Chandrasear Address for Correspondence 1 Department of Computer Applications, KSR College of Engineering, iruchengode, India. Reader, Department of Computer Science, Periyar University, Salem, India. E Mail murali79npm@yahoo.com, ccsear@gmail.com ABSRAC Feature extraction is the first and foremost activity in object recognition and detection processing. It reduces the amount of data by representing the image in the form of distinctive, representative interest points. his paper deals with the extraction of global features from the pre-processed images. Geometric Moment invariant produces a set of seven normalized moment invariants that are invariant under shifting, scaling and rotation. Geometric Moment invariant is widely used to extract global features for pattern recognition due to its discrimination power and robustness. After the feature extraction is done the dimensionality of the feature is reduced using the concept of Principal Component Analysis. Finally, the reduced feature vector is used for the recognition of object using the Nearest Neighbor. KEYORDS: Geometric Moment Invariants, Principal Component Analysis, Nearest Neighbor, Feature Extraction, Canny Edge Detection, Object Recognition, Scale Invariant. I. INRODUCION Invariant and its characteristics are described. Recognizing objects through vision is an important tas in every ones lives lie, recognizing people when he/she tal to others; recognizing their laptop, recognizing their car in the car paring lot and so on. hile this tas is performed with great accuracy and apparently little effort by humans, it is still unclear how this performance is achieved. Creating computer methods for object recognition gives rise to challenging theoretical problems such as how to model the visual appearance of the objects or to recognize the object. Pattern Recognition is an essential part of any high level image analysis systems. In general the object recognition systems are organized as Image Acquisition, preprocessing of the images, feature extraction and classification [1]. Ahmed et.al, states that the importance of the SIF eypoints in object identification. Ananthashayana V.K and Asha.V found that the PCA and IPCA plays vital role in the feature extraction process. J.Gao et.al, suggests the nearest neighbor is the best method for classifications of patterns. In this paper, an object recognition system is designed to identify an object from the captured image. he organization of paper is as follows: Section II discusses Canny s Edge Detection. In Section III, Geometric Moment Section IV discusses Principal Component Analysis. Section V reports experimental results, while section VI gives conclusions and discussions for further study. II. CANNY S EDGE DEECION In most Image Processing applications, edge detection is used to detect outlines of an object and boundaries between objects and the bacground in the image. he goal of edge detection is to convert a D image into a set of curves. hat is meant to extract salient features of the scene. he salient features are expected to be the boundaries of objects that tend to produce sudden changes in the image intensity. For example, different objects in an image usually have different colors, hues, or light intensity and this cause the image intensity to change. hus, much of the geometric information that would be conveyed in a line drawing is captured by the intensity changes in an image. Canny edge detection uses linear filtering with a Gaussian ernel to smooth noise and then computes the edge strength and direction for each pixel in the smoothed image. his is done by differentiating the image in two orthogonal directions and computing the gradient magnitude as the root sum of squares of the derivatives. he gradient direction is JERS/Vol.II/ Issue I/January-March 011/99-1

2 computed using the arctangent of the ratio of the derivatives [3] [4]. Candidate edge pixels are identified as the pixels that survive a thinning process called non-maximal suppression. In this process, the edge strength of each candidate edge pixel is set to zero if its edge strength is not larger than the edge strength of the two adjacent pixels in the gradient direction. hresholding is then done on the thinned edge magnitude image using hysteresis. In hysteresis, two edge strength thresholds are used. All candidate edge pixels below the lower threshold are labeled as non-edges and all pixels above the low threshold that can be connected to any pixel above the high threshold through a chain of edge pixels are labeled as edge pixels. he Canny edge detector requires the user to input three parameters. he first is sigma, the standard deviation of the Gaussian filter specified in pixels. he second parameter, low, is the low threshold which is specified as a fraction of a computed high threshold. he third parameter high is the high threshold to use in the hysteresis and is specified as a percentage point in the distribution of gradient magnitude values for the candidate edge pixels. III. GEOMERIC MOMEN INVARIANS he two-dimensional geometric moment of order (p+q th of a function f(x,y is defined as a b p q m pq x y f, a1 b1 ( x y (1 where p,q 0,1,,. Note that the monomial product x p y q is the basis function for this moment definition. A set of n moments consists of all m pq s for p+ q n, i.e., the set contains 1 ( n + 1( n+ elements. Hu and Alt inspired the use of moments for image analysis and pattern recognition. Historically Hu published the first significant paper on the utilization of moment invariants for image analysis and object representation. Hu s approach was based on the wor of the nineteenth century mathematicians Boole, Cayley, and Sylvester, on the theory of algebraic forms. Hu stated that if f(x,y is piecewise continuous and has nonzero values only in a finite region of the x,y plane, then dx dy the moment sequence {m pq } is uniquely determined by f(x,y and conversely, f(x,y is uniquely determined by{m pq }. Since an image segment has finite area in worst case, is piecewise continuous, a moment set can be computed and used to uniquely describe the information contained in the image segment. Using non-linear combinations of geometric moments, Hu derived a set of invariant moments, which has the desirable properties of being invariant under image translation, scaling and rotation. However the reconstruction of the image from these moments is deemed to be quite difficult. A. Moment Invariants Moment invariants are used in many pattern recognition applications. he idea of using moments in shape recognition gained prominence in 1961, when Hu derived a set of moment invariants using the theory of algebraic invariants. Image or shape feature invariants remain unchanged if that image or shape undergoes any combination of the following changes Change of size (Scale Change of position (ranslation Change of Orientation (Rotation Reflection he Moment invariants are very useful way for extracting features from two-dimensional images [7]. Moment invariants are properties of connected regions in binary images that are invariant to translation, rotation and scale. hey are useful because they define a simply calculated set of region properties that can be used for shape classification and part recognition. he normalized central moments (, denoted by η pq are defined as µ pq η pq γ µ 00 ( p + q where γ + 1, p + q,3,4... A set of seven invariants can be derived from the second and third normalized central moments. his set of seven HU moment invariants (3 to (9 is invariant to translation, rotation, and scale change. JERS/Vol.II/ Issue I/January-March 011/99-1

3 η 0 0 JERS/Vol.II/ Issue I/January-March 011/99-1 Journal of Engineering Research and Studies E-ISSN ( 3 ( η 11 3η 1 + ( 3η 1 ( 5 ( ( η ( 3η ( [ η 1 1 ] + 4η ( 8 ( 3η ( 3η 3 1 IV. PRINCIPAL COMPONEN ANALYSIS PCA technique, also nown as Karhunen-Loeve transform chooses a dimensionality reducing linear projection that maximizes the scatter of all projected samples [6]. If the total scatter is defined by S N 1 ( x µ ( x ( 10 S µ where x R n are sample images, N is the total number of sample images and µ is the mean image of all sample images then after applying a linear transform the resultant obtained are transformed features y R m in the reduced dimensional subspace; y x 1 ( 11 where R nxm is a matrix with orthonormal columns. In PCA, the projection matrix opt will be chosen to maximize the determinant of the total scatter of the transformed features. opt arg max w S [ ] [ ] ( 7 [ ] [ ] ( 9 ( 1 where S is the scatter matrix of transformed features. Principal Component Analysis is an eigenvector/value-based approach used in dimensionality reduction (or feature extraction to the multivariate data. It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and 1 1 differences. It is widely used in most of the pattern recognition applications lie face recognition, image compression, and is common technique for finding patterns in high dimensional data [10]. V. EXPERIMENAL RESULS he proposed feature extraction process was experimented in MALAB 7.5 and with images of 14 objects, size 160x10 in different angles and poses, and also with the images of coil-100 database. During the experimentation, the first phase is to convert the color image into gray image and perform some filtering process to remove the noise and then for the given image the canny s edge detection is performed and the edge detected image is saved for further processing. In fig.1 shows the grey image and fig. shows the edge detected image. After the edge detection is performed, the moment invariant is generated for the edge detected image. he following table 1 shows the normalized moment invariant generated and the fig.3 showing the graph for the moments of similar images in various poses. Fig. 1 Grey image Fig. Edged Detected image

4 able 1 Normalized moment invariants for objects 1 and Moment Invariants First Second hird Fourth Fifth Sixth Seventh Patterns 1.00E E E E-0 7.9E- 8.98E E-01 (5⁰ clocwise 1.00E E E E E E E-01 (15⁰ clocwise 1.00E+00 4.E E E-01.65E-0 6.E E-01 (5⁰ clocwise 1.00E E-0.84E E E-0 4.8E E-01 Object 1.00E E E E E E E+00 Object (5⁰ clocwise 1.00E E E E E E E E+00 Moment Invariant values 1.00E E E E-01.00E E+00 Seven Moment Invariants Fig.3 Line Graph for the table 1 Once the normalized moment invariant is generated determine the class label of x 0, where x 0 is the the feature vector is formed by arranging the feature vector of the training images and test moment invariants of the training images and test images. ithout prior nowledge, the KNN image in a matrix form. After the feature vector is classifier usually applies Euclidean distances as the created, the Principal component analysis is distance metric. However, this simple and easy-toimplement performed to reduce the dimensionality of the method can still yield competitive feature by creating Eigen vectors. Once the results even compared to the most sophisticated dimensionality is reduced, the nearest neighbor machine learning methods. his proposed concept algorithm is applied for the identifying the given was applied to a number of COIL100 images and image by comparing the training image using that 93 percent of obtained results were Euclidean distance metric. he nearest neighbor satisfactory. he experimental results showed that decision rule assigns to an unclassified sample the the recognition rate of the nearest neighbor classification of the nearest of a set of previously classifier based on Hu moments. he results are classified samples [8]. he K-nearest neighbor given in able. (KNN classification algorithm tries to find the K nearest neighbors of x 0 and uses a majority vote to JERS/Vol.II/ Issue I/January-March 011/99-1

5 Fig. 4. Some examples of objects from COIL- 100 database able. Recognition Rate of Hu Moments using Nearest Neighbor classifier Image % of Accuracy Bottle 95% Car 93% Can 9% Cup 90% oy 9% omato 95% VI. CONCLUSION In this paper the feature extraction process for identifying an object in the image is presented, the geometric moment invariant is extracted from the image. In order to extract the moment invariants, the unwanted pixels in the image is eliminated by the process of Edge detection, for this canny edge detector is used. PCA technique is applied for dimensionality reduction. From the experiments, it is found that PCA is able to reduce the number of invariants for the objects without losing their useful information s. In other words, after PCA, the extracted components of third order moments are used for classifications using classification algorithm. Dimensionality reduction using PCA indirectly saves the computation time and space by using less number of invariant variables for object detection. Further the results from the above experimentation are to be compared with the Neural Networ Model. And also Hu Moment is to be compared with the Other Moment Invariant Methods lie Zernie and Legendre. VII. REFERENCES 1. Ahmed Fawzi Otoom, Hatice Gunes, Massimo Piccardi, Feature extraction techniques for abandoned object classification in video surveillance, IEEE Proceedings of the International Conference on Image processing, pp , Ananthashayana V.K and Asha.V, Appearance Based 3D Object Recognition Using IPCA- ICA, he International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1. Beijing, pp D.Lowe, Object recognition from local scaleinvariant features," in Proc. International Conference on Computer Vision, (Corfu, Greece, IEEE Press, Sept Duda and Hart, Pattern Classification and Scene Analysis. New Yor. NY: iley, J. Gao, Z. Xie, and X. u, Generic object recognition with regional statistical models and layer joint boosting, Pattern Recognition Letters, vol. 8, no. 16, pp. 7-37, Jacson J.E., A User s Guide to Principal Components, John iley & Sons, New Yor, K.Miolajczy and C.Schmid, A performance evaluation of local descriptors, IEEE ransactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 10, pp , Kunlun Li, Xuerong Luo and Ming Jin, Semisupervised Learning for SVM-KNN, Journal of Computers, vol. 5, no. 5, pp , May Miel D. Rodriguez and Mubara Shah, "Detecting and Segmenting Humans in Crowded Scenes", ACM September 3 8, Paul Viola, Michael Jones, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision, Vol-63, no., pp , S. Margret Anouncia and J. Godwin Joseph, Approaches for Automated Object Recognition and Extraction from Images a Study, Journal of Computing and Information echnology, Vol-4, pp , S. atanabe, Pattern Recognition: Human and Mechanical, New Yor: iley, S. J. Perantonis and P. J. G. Lisboa, ranslation, rotation, and scale invariant pattern recognition by high-order neural networs and moment classifiers, IEEE rans. Neural Networs, vol. 3, pp , Mar JERS/Vol.II/ Issue I/January-March 011/99-1