From Single Image to List of Objects BasedonEdgeandBlobDetection

Transcription

1 From Single Image to List of Objects BasedonEdgeandBlobDetection Rafa l Grycuk 1, Marcin Gabryel 1, Marcin Korytkowski 1, Rafa l Scherer 1, and Sviatoslav Voloshynovskiy 2 1 Institute of Computational Intelligence, Czȩstochowa University of Technology Al. Armii Krajowej 36, Czȩstochowa, Poland {rafal.grycuk,marcin.gabryel,marcin.korytkowski, rafal.scherer}@iisi.pcz.pl 2 University of Geneva, Computer Science Department, 7 Route de Drize, Geneva, Switzerland Abstract. In this paper we present a new method for obtaining a list of interest objects from a single image. Our object extraction method works on two well known algorithms: the Canny edge detection method and the quadrilaterals detection. Our approach allows to select only the significant elements of the image. In addition, this method allows to filter out unnecessary key points in a simple way (for example obtained by the SIFT algorithm) from the background image. The effectiveness of the method is confirmed by experimental research. 1 Introduction Content-based image retrieval is one of the greatest challenges of present computer science. Effective browsing and retrieving images is required in many various fields of life e.g. medicine, architecture, forensic, publishing, fashion, archives and many others. In the process of image recognition users search through databases which consist of thousands, even millions of images. The aim can be the retrieval of a similar image or images containing certain objects [4][6]. Retrieving mechanisms use image recognition methods. This is a sophisticated process which requires the use of algorithms from many different areas such as computational intelligence [7][8][12][20][21][22][24][27][29][32], machine learning [23], mathematics [2][13] and image processing [11]. An interesting approach for CBIR, based on nonparametric estimates (see e.g. [14][25][26]) was presented in [10]. One of important aspects of content-based image retrieval is the ability to find specific objects in the image. This is particularly important in the case of large image databases which are searched for specific objects. Many ways to detect objects can be found in the literature and one of them is image segmentation. The goal of the image segmentation is to cluster pixels into salient image regions, i.e. regions corresponding to individual surfaces, objects, or natural parts L. Rutkowski et al. (Eds.): ICAISC 2014, Part II, LNAI 8468, pp , c Springer International Publishing Switzerland 2014

2 618 R. Grycuk et al. of objects. There are many methods for image segmentation: thresholding [3], clustering methods [1], histogram-based methods [30], edge detection [28], stereo vision based methods [16] and many others. We propose a new method for extracting objects from images. This method consists of two parts: the Canny edge detection method [5] and the method for quadrilaterals detection [17]. An important element in our algorithm is the additional step which consists in filtering out the key points from the background. The result is an image with key points only in the most interesting areas of the image. Our method uses the SIFT (Scale-invariant feature transform) algorithm to detect and describe local features of an image. It was presented for the first time in [18] and is now patented by the University of British Columbia. For each key point which describes the local image feature, we generate a feature vector, that can be used for further processing. SIFT contains four main steps [15][19]: extraction of potential key points, selection of stable key points (resistant to change of scale and rotation), finding key point orientation immune to the image transformation, generating a vector describing the key point. SIFT key points contain 128 values. The paper consists of several sections. In the next sections we will present methods used in the proposed approach and a method for image segmentation algorithm. The last section presents the experimental results on an original softwarewrittenin.net. 2 Canny Edge Detection The Canny edge detector [5] is one of the most commonly used image processing methods for detecting edges. It takes as input a gray scale image, and produces as output an image showing the positions of tracked intensity discontinuities. The algorithm runs in 5 separate steps: 1. Noise reduction. The image is smoothed by applying an appropriate Gaussian filter. 2. Finding the intensity gradient of the image. During this step the edges should be marked where gradients of the image have large magnitudes. 3. Non-maxima suppression. If the gradient magnitude at a pixel is larger than those at its two neighbors in the gradient direction, mark the pixel as an edge. Otherwise, mark the pixel as the background. 4. Edge tracking by hysteresis. Final edges are determined by suppressing all edges that are not connected to genuine edges. The effect of the Canny operator is determined by parameters: The width of the Gaussian filter used in the first stage directly affects the results of the Canny algorithm, The thresholds used during edge tracking by hysteresis. It is difficult to give a generic threshold that works well on all images.

3 From Single Image to List of Objects Based on Edge and Blob Detection Blob Extraction Blob extraction is one of the basic methods of image processing. It allows to detect and extract a list of blobs (objects) in the image. Unfortunately, obtaining homogeneous objects from an image as a list of pixels is extremely complicated. Especially when we deal with a heterogeneous background, i.e. the objects containing multicolored background. There are many methods for extracting objects (blobs) from images [9]. In this paper we use methods implemented in the AForge.NET library. These algorithms are described by Andrew Kirillov [17]. There are four types of the algorithms: Convex full, Left/Right Edges, Top/Bottom Edges, Quadrilateral. Fig.1 describes these blob detection methods. Figure 6A illustrates Quadrilaterals detection method. As can be seen, round edges of the objects are not detected correctly. Much better results are obtained by the Top/Bottom Edges algorithm (Figure 1C). Edges of objects are detected mostly correctly, with individual exceptions. The Left/Right Edges method behaves similarly (Fig. 1B). The last method has a problem with the detection of vertices inside figures, e.g. star type objects (Fig. 1D). Quadrilateral detection method can be described by the following steps: 1. Locate each separate object in the input image, 2. Find object edge pixels (methods: Top/Bottom, Left/Right), 3. Detect four corners of quadrilateral, Fig. 1. Comparison of methods for blob detection used in the AForge.NET library [17]

4 620 R. Grycuk et al. 4. Set distortionlimit. This value determines how different the object can be. Set 0 to detect perfect quadrilateral, 5. Check how well the analyzed shape fits into the quadrilateral with the assumed parameter (see Fig. 1D ). 6. Check each mean distance between given edge pixels and the edge of assumed quadrilateral, 7. If mean distance is not greater then distortionlimit, then we can assume that the shape is quadrilateral. 4 Proposed Method for Segmentation Algorithm In this section we present a new approach to segmentation. When we have a single image described by pixel matrix (e.g. RGB), we can segment it into many objects. This section describes a methodology of this process. The algorithm can be divided into several steps. First of all we need to create mathematical description of the input image. In this step we use the SIFT algorithm. In return we obtain a list of key points (whole image). The next step is to create edge detected image. In this step we use the Canny algorithm described in section 2. Another step detects blobs, with all their properties such as: center of gravity, area, mean color, blob rectangle, position. When we have detected these object contours as blobs, we can extract rectangles for these objects. The next step detects and saves key points in each rectangle. In other words, we check if the key point position lies in rectangle area, if so it is assigned to this rectangle. The last step extracts objects from the original (input) image and saves them as separate objects. This method allows to extract the list of objects from the image, not only as raw part of the image but also with a list of key points assigned to these objects. In other words, on the output of our method we obtain the list of objects (raw part) with assigned key points (vectors) to each object [31]. Figure 2 describes steps of the algorithm, witch can be represented by the following pseudo code: The blob detection step generates lots of small blobs. Fig. 2. Block diagram of the proposed method

5 From Single Image to List of Objects Based on Edge and Blob Detection 621 INPUT: Single image, singleinputimage OUTPUT: Segmented image, list of objects with descriptors assign to each object, descriptorslist, objectslist alldescriptors := Generate Descriptor(singleInputImage); edgedimage := Canny Edge Detector(singleInputImage, thesh, threshlinking); blobs := Detect Blobs(edgedImage); foreach blob blobs do if blob.area >= minblobarea then extractedblob := Extract Blob(singleInputImage, startp osition, blob.w idth, blob.heigth); objectslist.add To List(extractedBlob); extracteddescriptor := Extract Descriptor(allDescriptors, startp osition, blob.w idth, blob.heigth); descriptorslist.add To List(extractedDescriptor); end end Algorithm 1. Segmentation steps The minblobarea describes min area for extracted objects. All blobs with area equal or greater will be extracted. For blob detection process we use Quadrilaterals detection method (see section 3). The extraction process (blob and descriptor extraction) is realized simply by copying entire blob rectangle into newly created image (pixel by pixel, key point by key point). Algorithm stages were implemented in.net C# using AForge and Emgu CV libraries. The first and second step is based on Emgu CV. The third was implemented using AForge. Two last steps were implemented by the authors. 5 Experimental Results In this section we present the results of the experiments. For this purpose we created simulation application based on described algorithms. On the input we can put a single image and in return we obtain the list of objects with assigned key points. Images in simulations were labelled as follows: Input images were labelled by characters (i.e. A,B,C...) Extracted objects were labelled by object number and letter of input image (i.e. 1A means first objects from image A.) Figure 3 presents input images of four simulations. Three of them (A,C,D) are from the same image class (cars), but they were taken from different perspective. In Fig. 4 we present the results of the proposed method. For each input image (A,B,C,D) we obtain a list of objects (1A, 2A,...,1B, 2B...). As we can see the algorithm segmented the image properly. Figure 5 illustrates another type of images i.e. astro images, underwater images, many different objects. These images were given as the algorithm input and were segmented correctly. Object 5G in

6 622 R. Grycuk et al. Fig. 6 was not entirely extracted. The rest of the objects (image G) were separated properly. Table 1 and tab. 6 represent segmented key points. First column describes object id (i.e. 1A) and the second determine key points count for each object. The third represents input image key point count (before segmentation). Figure 7A presents the input image after key points detection. As can be seen, the SIFT algorithm detects multiple key points in background of the image. They are located both on objects and background. The next step is Canny edge detection. This part of the algorithm is extremely important. Performing blob detection on the input image is pointless because, the algorithm detects only one blob (the entire image). To eliminate this drawback we use Canny edge detection as preprocessing. Then, on that image type we execute the blob detection algorithm. After that, we extract those blobs simply by copy the entire rectangle represented by this blob. As can be see in Fig. 7B, not significant key points has been reduced. We obtain extracted objects saved as separated images. Fig. 3. Simulation input images. Experiments A-D. 6 Final Remarks The presented method is a novel approach for image segmentation. Conducted experiments proved effectiveness of our method. Algorithm takes a single RGB image as input and returns a list of objects as output. Most of extracted objects are detected correctly, but some images contain fragments of other objects. This is caused by intersection of these objects. The next step of our research will be color distinction of obtained objects to remove non homogeneous objects. Our method extracts objects from different types of images, such as: astro images, underwater images and many others.

7 From Single Image to List of Objects Based on Edge and Blob Detection 623 Fig. 4. Objects obtained from images presented in Fig 3. Experiments A-D. Object labels correspond with the input image (eg. 1B, 2B.., are objects belongs to input image B). Table 1. Key points extraction, experiments A-D. Key points are assigned to each ments E-G. Key points are assigned to each Table 2. Key points extraction, experi- object. The labels correspond with Fig. 4. object. The labels correspond with Fig. 6. Blob Object key points Input image number count key points count 1A 138 2A 65 3A A 160 5A 175 1B 10 2B B 16 1C 137 2C 107 3C 172 4C 201 5C C 189 7C 71 8C 48 9C 82 1D 43 2D 41 3D D 77 Blob Object key points Input image number count key points count 1E 241 2E E 281 1F 63 2F F 18 1G 239 2G 67 3G 87 4G 45 5G 79 6G G 15 8G 75 9G 70 10G 33 11G 97 1H 264 2H

8 624 R. Grycuk et al. Fig. 5. Simulation input images. Experiments E-H. Fig. 6. Objects obtained from images presented in Fig 5. Experiments E-H. Object labels corresponding with input image (e.g. 1G, 2G,..,) are objects that belongs to input image G).

9 From Single Image to List of Objects Based on Edge and Blob Detection 625 Fig. 7. Background key points removal simulation. Fig. 7A presents the image after key points detection and Fig. 7B after background key points removal. Acknowledgments. The work presented in this paper was supported by a grant from Switzerland through the Swiss Contribution to the enlarged European Union. References [1] Barghout, L., Sheynin, J.: Real-world scene perception and perceptual organization: Lessons from computer vision. Journal of Vision 13(9), 709 (2013) [2] Bartczuk, L., Przybyl, A., Dziwinski, P.: Hybrid state variables - fuzzy logic modelling of nonlinear objects. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp Springer, Heidelberg (2013) [3] Batenburg, K., Sijbers, J.: Optimal threshold selection for tomogram segmentation by projection distance minimization. IEEE Transactions on Medical Imaging 28(5), (2009) [4] Bazarganigilani, M.: Optimized image feature selection using pairwise classifiers. Journal of Artificial Intelligence and Soft Computing Research 1(2), (2011) [5] Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), (1986) [6] Chang, Y., Wang, Y., Chen, C., Ricanek, K.: Improved image-based automatic gender classification by feature selection. Journal of Artificial Intelligence and Soft Computing Research 1(3), (2011) [7] Cpalka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Transactions on Neural Networks 20(4), (2009) [8] Cpalka, K., Rutkowski, L.: Flexible takagi sugeno neuro-fuzzy structures for nonlinear approximation. WSEAS Transactions on Systems 4(9), (2005) [9] Damiand, G., Resch, P.: Split-and-merge algorithms defined on topological maps for 3d image segmentation. Graphical Models 65(1), (2003)

10 626 R. Grycuk et al. [10] Duda, P., Jaworski, M., Pietruczuk, L., Scherer, R., Korytkowski, M., Gabryel, M.: On the application of fourier series density estimation for image classification based on feature description. In: Proceedings of the 8th International Conference on Knowledge, Information and Creativity Support Systems, Krakow, Poland, November 7-9, pp (2013) [11] Gabryel, M., Korytkowski, M., Scherer, R., Rutkowski, L.: Object detection by simple fuzzy classifiers generated by boosting. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp Springer, Heidelberg (2013) [12] Gabryel, M., Nowicki, R.K., Woźniak, M., Kempa, W.M.: Genetic cost optimization of the GI/M/1/N finite-buffer queue with a single vacation policy. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp Springer, Heidelberg (2013) [13] Gabryel, M., Woźniak, M., Nowicki, R.K.: Creating learning sets for control systems using an evolutionary method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) SIDE 2012 and EC LNCS, vol. 7269, pp Springer, Heidelberg (2012) [14] Greblicki, W., Rutkowska, D., Rutkowski, L.: An orthogonal series estimate of time-varying regression. Annals of the Institute of Statistical Mathematics 35(1), (1983) [15] Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image indexing by data clustering and inverse document frequency. In: Mrozek, S.K.D., Kasprowski, P., Ma lysiak-mrozek, B., Kostrzewa, D. (eds.) BDAS CCIS, vol. 424, pp Springer, Heidelberg (2014) [16] Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Romanowski, J.: Improved digital image segmentation based on stereo vision and mean shift algorithm. In: Parallel Processing and Applied Mathematics LNCS. Springer, Heidelberg (2014) (manuscript accepted for publication) [17] Kirillov, A.: Detecting some simple shapes in images (2010), [18] Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp IEEE (1999) [19] Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), (2004) [20] Nowicki, R.: Rough-neuro-fuzzy system with micog defuzzification. In: 2006 IEEE International Conference on Fuzzy Systems, pp (2006) [21] Nowicki, R.: On classification with missing data using rough-neuro-fuzzy systems. International Journal of Applied Mathematics and Computer Science 20(1), (2010) [22] Nowicki, R., Rutkowski, L.: Soft techniques for bayesian classification. In: Neural Networks and Soft Computing, pp Springer (2003) [23] Peteiro-Barral, D., Guijarro-Bardinas, B., Perez-Sanchez, B.: Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms. Journal of Artificial Intelligence and Soft Computing Research 2(1), 5 20 (2012) [24] Przyby l, A., Cpa lka, K.: A new method to construct of interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp Springer, Heidelberg (2012)

11 From Single Image to List of Objects Based on Edge and Blob Detection 627 [25] Rutkowski, L.: A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Transactions on Circuits and Systems 33(8), (1986) [26] Rutkowski, L.: Non-parametric learning algorithms in time-varying environments. Signal Processing 18(2), (1989) [27] Rutkowski, L., Przyby l, A., Cpa lka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp Springer, Heidelberg (2010) [28] Sankaranarayanan, V., Lakshmi, S.: A study of edge detection techniques for segmentation computing approaches. IJCA, Special Issue on CASCT (1), (2010) [29] Starczewski, J.T.: A type-1 approximation of interval type-2 FLS. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds.) WILF LNCS, vol. 5571, pp Springer, Heidelberg (2009) [30] Stockman, G., Shapiro, L.G.: Computer Vision, 1st edn. Prentice Hall PTR, Upper Saddle River (2001) [31] Tamaki, T., Yamamura, T., Ohnishi, N.: Image segmentation and object extraction based on geometric features of regions. In: Electronic Imaging International Society for Optics and Photonics, pp (1998) [32] Zalasiński, M., Cpa lka, K.: Novel algorithm for the on-line signature verification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp Springer, Heidelberg (2012)