Real-time Textureless Object Detection and Recognition Based on an Edge-based Hierarchical Template Matching Algorithm

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1 Journal of Applied Science and Engineering, Vol. 21, No. 2, pp (2018) DOI: /jase _21(2).0011 Real-time Textureless Object Detection and Recognition Based on an Edge-based Hierarchical Template Matching Algorithm Chi-Yi Tsai* and Chao-Chun Yu Department of Electrical and Computer Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C. Abstract Textureless object recognition is a difficult task in computer vision because the object-ofinterest (OOI) may not have enough texture information for extracting object features. To address this problem, this paper presents a textureless object recognition method based on the existing Line2D algorithm. The proposed method employs an edge-based hierarchical template matching method to detect and identify a wide variety of textureless objects. Given a reference template image of an OOI, a hierarchical edge-template database containing different 2D poses of the OOI was firstly created by applying affine transformation with different rotating and scaling settings to the reference template image. Next, an edge-based template matching process is performed to detect and recognize the OOI by searching matches between the hierarchical edge-template database and the input image. Finally, the position and angle posture of the OOI can be determined by the best match having the highest similarity measure. Experimental results show that the proposed method not only can efficiently recognize the type, quantity, position, and angle information of various textureless objects in the image, but also can achieve real-time performance about 24 frames per second (fps) in processing 640x480 images. Therefore, the proposed algorithm has the potential to be used in many computer vision applications. Key Words: Textureless Object Detection, Textureless Object Recognition, Template Matching, Pose Identification, Line2D Algorithm 1. Introduction Object recognition is one of the important tasks in the field of computer vision, and most of the conventional methods use texture information of objects to produce feature descriptors for object recognition process. However, in many practical applications, the object to be recognized may not have enough texture information for extracting keypoint descriptors [1 3], greatly increasing the difficulty of the object recognition task. This problem generally is referred to as textureless object recognition. Therefore, how to solve the problem of textureless *Corresponding author. chiyi_tsai@mail.tku.edu.tw object recognition remains an important issue for many computer vision applications. Several approaches have been proposed to address the textureless object recognition problem from a single image, and we examine them into three categories: feature-based approaches, view-based approaches, and shape-based approaches. The feature-based approaches [4 6] use edge features or line segments to recognize the textureless OOI by matching 2D image features to the corresponding 3D model points of the OOI. In [4], Costa and Shapiro proposed an object recognition system named relational indexing of objects (RIO), which performs a feature-based alignment algorithm to retrieve matches between an edge image of the OOI and 2D view-class

2 230 Chi-Yi Tsai and Chao-Chun Yu object models from a large database of object models. In [5], the authors defined a 3D invariant space, in which object recognition and pose estimation can be achieved by matching a set of 3D invariant model points of a given 3D model and a set of invariant light rays in an image of the OOI. In [6], David and DeMenthon proposed a model-based object recognition algorithm that uses correspondences between model and image line features to recognize the OOI in a clutter environment. One advantage of the feature-based approaches is that the 3D pose of the OOI can be calculated from the match result directly. However, finding the correspondences between model and image features is still a challenge in these methods. The view-based approaches [7 10] compare a given image of the OOI with pre-computed 2D views of the OOI represented by using aspect graphs [7]. These methods can roughly determine the object pose; however, they have to deal with a search problem in a large full geometric search space. To reduce the complexity of the aspect graph, Cyr and Kimia proposed a similarity-based aspect graph method [8] that groups similar 2D views of the OOI into a few aspects, each of them representing a single prototype of the OOI. In the recognition process, a shape similarity metric was employed to measure the similarity between a given OOI image and these few prototypes. Eggert et al. proposed a scale-space aspect graph approach [9] to reduce the number of aspects in the graph by increasing the scale of the object model. In [10], Ulrich et al. integrated the scale-space and similaritybased aspect graph approaches to develop a hierarchical view-based 3D object recognition algorithm. To improve robustness of object recognition, the authors used a robust similarity metric proposed in [11] instead of the original one. Moreover, the scale-space concept was realized by generating a hierarchical model from computer-aided design (CAD) model of the object within some predefined pose ranges. However, the complexity of the view-based approaches is still very high for many applications. The shape-based approaches [12 16] are based on template matching of edge segments. In [12], Hinterstoisser et al. proposed a dominant orientation template matching method (also called Line2D method) for realtime textureless object detection without a time consuming training stage. They also combined the Line2D method with a matching approach in range data to form a multimodal detection algorithm, called LineMod method [13,14]. The Line2D and LineMod methods can detect multiple objects in real-time; however, both methods require users to pre-build multimodal templates of each object manually. Cai et al. proposed a two-stage cascaded object detection method [15], which is based on an edge-based sparse image descriptor and can provide a rough estimation of object pose through a voting procedure. However, this method requires a large database recording the training templates of each OOI with different poses. In [16], Tombari et al. proposed a Bunch of Lines Descriptor (BOLD), which encodes a group of neighboring line segments obtained from a fast line segment detector [17] to produce an edge-based scale- and rotation-invariant descriptor. The BOLD keypoint allows improving robustness of edge-based object detection algorithms; however, it is difficult to deal with detection of curvilinear or simple objects in a heavy clutter scene. To detect textureless objects in heavily cluttered-occluded scenes, Chan et al. proposed a Bounding Oriented-Rectangle Descriptor for Enclosed Regions (BORDER), which is a discriminative rotation-invariant descriptor based on a Linelets technique to stabilize keypoint repeatability in occlusion [18]. However, the BORDER method is computationally expensive especially when large number of keypoints needs to be processed. Moreover, the precision of BORDER keypoint is restricted to its fairly-low block resolution [4 4]. Recently, the same authors also proposed a Binary Integrated Net Descriptor (BIND), which uses binary coding to encode multi-layered regional information into binary strings for high precision edge-based description [19]. Because BIND is a binarybased textureless object detector, its recognition speed is faster than BORDER about 2.2 times. In this paper, a textureless object recognition method is proposed based on the existing Line2D algorithm. An edge-based hierarchical template matching method is proposed to efficiently detect and identify a wide variety of textureless objects. Given a reference template image of an OOI, a hierarchical edge-template database containing various 2D poses of the OOI was firstly created by applying affine transformation with different rotating and scaling settings to the reference template image.

3 Real-time Textureless Object Detection and Recognition Based on an Edge-based Hierarchical Template Matching Algorithm 231 Next, an edge-based template matching process is performed to detect and recognize the OOI by searching matches between the hierarchical edge-template database and the input image. Finally, the 2D pose of the OOI can be determined by the best match having the highest similarity measure. Experimental results show that the proposed method not only can efficiently recognize the type, quantity, position, and angle information of various textureless objects in the image, but also can identify up-down relationship between the recognized objects. In addition, the proposed method achieves real-time performance about 24 frames per second (fps) in processing images. These advantages make the proposed algorithm having the potential to be used in many computer vision applications. The rest of this paper is organized as follows. Section 2 introduces the proposed edge-based hierarchical template matching algorithm. Section 3 presents the proposed database generation method to automatically produce a hierarchical edge-template database of a given OOI. Experimental results are reported in Section 4 to evaluate the performance and robustness of the proposed textureless object detection and recognition method. Section 5 concludes the contributions of this paper. 2. The Proposed Edge-based Hierarchical Template Matching Algorithm Figure 1 shows the block diagram of the proposed textureless object detection and recognition system, which is constructed by an efficient image enhancement algorithm [20], an edge-based template matching algorithm and a database consisting of hierarchical edge templates of the OOI and response map of each edge orientation for similarity computation. This section focuses on the design of the edge-based template matching algorithm, which is divided into five stages: color edge detection, edge orientation quantization, one-hot coding of edge orientation, orientation spreading by dilation and similarity measurement. The following subsections briefly explain the function of each stage in the proposed edgebased template matching process. 2.1 Color Edge Detection To detect textureless objects, image edges are often used as image cues because they are the most stable features on textureless objects. To improve detection performance, we use edges on each color channel to detect the OOI in the input image. Given an RGB color image I rgb, two directional edge maps of each color channel can be computed by using Sobel operator such that (1) where the symbol denotes 2D convolution operator, c {R, G, B} denotes one of the color channels of the color image, and S h and S v are the horizontal and vertical convolution masks defined as For each image location, the edge with largest magni- Figure 1. Block diagram of the proposed textureless object detection and recognition system.

4 232 Chi-Yi Tsai and Chao-Chun Yu tude is chosen as the color edge of the image, denoted by g,suchthat (2) c c c T where g (, x y) [ Ih (, x y) Iv (, x y)] is the gradient vectorofcolorchannelc at image location (x, y) oftheinput color image. 2.2 Edge Orientation Quantization After extracting color edges of the input image, an edge orientation map can be computed by evaluating color gradient vector such that (3) where d min is a threshold to remove noises, and atan2(y, x) is a four quadrant arctangent function returning tan 1 (y/x) with a proper quadrant [21]. Next, the orientation of color edges is quantized into eight directions {g 0, g 1,, g 7 } with equal spaces as shown in Figure 2(a). To speed up the processing time, we divide the edge orientation map into N blocks, each of them containing eight bins to count edge orientations in the block such that (4) where i {0,1,,7},j {0, 1,..., N 1},and q = 22.5 is a constant angle interval to quantify the edge orientation. Then, the quantized edge orientation of the j-th block can be determined such that (5) where g i =(i +0.5) q is the i-th quantized edge orientation. 2.3 One-hot Coding of Edge Orientation To improve computational efficiency, the quantized edge orientation map obtained from Eq. (5) is encoded into a binarized image by one-hot coding. That is, each bit of the binary code corresponds to one quantized orientation, and the bit corresponding to the dominant edge orientation of a block is set to 1 as shown in Figure 2(b). Figure 2. (a) Quantifying the gradient orientation into eight directions {g 0, g 1,,g 7 }. (b) Binarized image of the quantized edge orientation map using one-hot coding.

5 Real-time Textureless Object Detection and Recognition Based on an Edge-based Hierarchical Template Matching Algorithm 233 These binary codes help to accelerate the computation of similarity measurement through a simple lookup table indexing operation, which is presented in section Orientation Spreading by Dilation As suggested by [13], one can diffuse the quantized edge orientation of each block to its adjacent locations to obtain a binarized spreading image, which is more robust to noise. To achieve this, the authors in [13] first computed a bit map for each quantized orientation and then combined these bit maps using shift and OR operations. Here, we propose another method to achieve the same purpose. Figure 3 shows concept of the proposed orientation spreading method, which applies morphological dilation operation on the one-hot coded binarized image to produce the binarized spreading image directly. The proposed method is very efficient because the dilation processing is bitwise, allowing to accomplish the orientation spreading operation for all eight bit-planes simultaneously. 2.5 Similarity Measurement Similarity measure is the last step of the template matching process. To measure the similarity between one block of the binarized edge orientation map and one of the quantized edge orientations g i, a similarity criterion is proposed such that (6) where B =(b 7 b 6 b 0 ) 2 is the binary code of the block to be measured, and B g ={b 7 g 7, b 6 g 6,..., b 0 g 0 } is a quantized edge orientation set associated with the binary code B.Let ref g denote the quantized edge orientation map of a given reference template, and B q is the binarized edge orientation map of a query template. Suppose that each of them contains N blocks. Based on the criterion (6), the similarity between g ref and B q can be determined by (7) where g j is the quantized edge orientation of the j-th block of g ref,andb j is the binary code of j-th block of B q. To speedup the computation of the similarity measurement (7), an efficient acceleration method was presented in [13] based on lookup tables. That is, for each quantized edge orientation, a lookup table can be precomputed with respect to all combinations of the binary code. Then, the computation of the similarity criterion (6) can be simplified as a lookup-table indexing operation, as shown in Figure 4. In this case, the quantized edge orientation is assumed as g 0 and the quantized edge orientation set is given by B g ={g 7, g 2, g 1 }. By doing so, the computations of equation (7) can be significantly reduced once the lookup table of each quantized edge orientation is pre-computed a priori. 3. Hierarchical Edge-template Database Generation As shown in Figure 1, the proposed system requires Figure 3. Binarized spreading image obtained by applying dilation operation on the one-hot coded binarized image of the quantized edge orientation map.

6 234 Chi-Yi Tsai and Chao-Chun Yu not only can deal with scale and rotation variations of the OOI, but also can efficiently determine the scale factor and rotation angle of the OOI according to the pose information of the best matched edge template in the database. The robustness and pose estimation performance of the proposed textureless object detection and recognition system is validated in the experiment section. 4. Experimental Results Figure 4. Efficiently accelerating the computation of similarity criterion (6) with respect to the quantized edge orientation g 0 using a lookup-table indexing operation. a hierarchical edge-template database of the OOI to perform the edge template matching process described in the previous section. This section thus presents an offline process to produce the hierarchical edge-template database to be used in the proposed system. Since the matching process is very sensitive to scale and rotation changes of the OOI, increasing the number of edge templates of the OOI under different poses helps to improve robustness of the proposed system against scale and rotation variations of the OOI. Given a reference image of an OOI, an efficient way to produce a variant of reference images of the OOI is setting a group of scaling s [s 1, s n ] and rotating [ 1, m ] parameters a priori. Then, applying these settings on the reference image generates a 2D hierarchical model constructed by hierarchical image templates of the OOI with different scales and rotations via affine transformation [22] such that The following experiments comprise three parts. The first part presents detection performance of the proposed method, including multi-objects detection, object classification and orientation estimation. The second part presents comparisons between the proposed method and other existing methods. The third part presents the computational efficiency of the proposed method running on a popular desktop platform. 4.1 Performance Evaluation Table 1 lists four reference images of the textureless objects (a screw nut, a screw, a key, and a scissor) used in the experiments, each of them is assigned a Class ID from 0 to 3, respectively. The database totally contains 1,944 reference images (504 screw nut images, 720 screw images, 504 key images, and 216 scissor images) (8) where p ij and p ref are pixel positions of the transformed template and the reference template, respectively. Figure 5 shows an example to produce the hierarchical image templates with different poses of the OOI. By doing so, the proposed edge-based template matching method Figure 5. An offline process to generate a 2D hierarchical model constructed by hierarchical image templates of a textureless OOI with different scale and rotation settings, s [s 1, s n ] and [ 1, m ].

7 Real-time Textureless Object Detection and Recognition Based on an Edge-based Hierarchical Template Matching Algorithm 235 Table 1. Objects-of-interest used in the experiments Reference image of textureless objects Image resolution (width height) Range of orientation angles (Degree) Number of templates Class ID to to to to generated by the proposed method described in the previous section. The orientation angle of the OOI is measured in degree, where a counterclockwise rotation is positive. The threshold d min used in Eq. (3) is set as 7.5 in the experiments. Table 2 shows the experimental result of the proposed method to detect and recognize multiple Class 0 and Class 1 textureless objects. In this experiment, several Class 0 and Class 1 objects were randomly placed on the table. From Table 2, it is clear that the proposed method successfully detects and recognizes these objects, each of them having a high similarity score greater than 87%. Moreover, the image position and orientation angle of each object also can be measured simultaneously. Note that in this paper the image position of the object is referred to the top-left point of the detected region-of-interest (ROI). Similar results can be observed in Table 3, in which the proposed method was used to detect and recognize multiple Class 2 and Class 3 textureless objects randomly placed on the table. Each detected object has a high similarity score over 86%, and its ROI position and orientation angle can be measured as well. The proposed method is able to be applied on a complex scene to detect the OOI recorded in the database. Table 4 shows the experimental results in this case, in which the Class 2 and Class 3 objects were randomly placed on the table together with other unknown objects. It is clear from Table 4 that the proposed method correctly extracts these two textureless objects from the complex scene. The similarity score of both detected objects is larger than 90%, and the measured ROI position

8 236 Chi-Yi Tsai and Chao-Chun Yu deals with the occlusion problem during textureless object detection and recognition tasks. One can see from Table 5 that the proposed method correctly distinguishes the multiple occluded Class 0 objects placed on the table. Moreover, the top-most object also has a higher similarity score than that of the occluded objects. This implies that the value of similarity score not only can be used to detect and recognize the OOI recorded in the database, but also can determine the top-most object in the case of detecting multiple occluded objects. This property may extend the proposed method to robotic manipulation applications such as random bin picking [23], pick-andplace [24] tasks. Therefore, the proposed method is suitable to be integrated in a robotic vision system to recognize textureless objects in a clutter scene. 4.2 Comparison to Other Existing Methods In the experiment, the performance of the proposed method was evaluated by comparing with the Speeded Up Robust Features (SURF) detection method [2], the Oriented FAST and Rotated BRIFE (ORB) detection method [3], and the BOLD method [16]. Figure 6 shows the experimental results of detecting and recognizing multiple textureless objects. It is clear from Figure 6 that the SURF and ORB detection methods fail to detect these and orientation angle are consistent with the pose of each OOI appeared in the captured image. The proposed method has robustness against occlusion in object detection and recognition. Table 5 presents the experimental results of the proposed method which

9 Real-time Textureless Object Detection and Recognition Based on an Edge-based Hierarchical Template Matching Algorithm 237 (a) (c) (b) (d) textureless model 0, model 5, and model 7, respectively. It can be seen from Figure 8 that the proposed method successfully detects and recognizes the target textureless object in a clutter scene. Therefore, the above experimental results validate the performance of the proposed method on the application of multiple textureless object detection and recognition, even in a complex and clutter scene. Figure 6. Comparisons between the proposed and the other existing methods: experimental result obtained by (a) the SURF detection method, (b) the ORB detection method, (c) the BOLD method, and (d) the proposed method. objects because both methods are based on the keypoint matching result, which usually contains many incorrect matches for textureless objects. Although the BOLD method is able to recognize the type of these objects, it detects only one object for each type. On the contrary, the proposed method not only succeeds to recognize the type of different textureless objects, but also correctly detects the quantity of multiple textureless objects. To further evaluate the performance of the proposed method, we also compared the proposed method with two recently published methods: the BORDER method [18] and the BIND method [19]. Figures 7(a), 7(b), and 7(c) illustrate the experimental results obtained from the BORDER method, BIND method, and the proposed method, respectively. From Figure 7, one can see that the BORDER method fails to detect both types of the textureless objects. The BIND method successfully recognizes the screw nut object, but it only finds one of the multiple screw nut objects. By contrast, the proposed method not only can recognize both types of the textureless objects, but also returns the correct number of the textureless objects in the scene. Figure 8 shows the experimental result of the proposed method tested on an online public textureless dataset [25], which includes nine textureless models and fifty-four test scenes with clutter and occlusions. Figures 8(a), 8(b), and 8(c) show the recognition result of the 4.3 Computational Efficiency The real-time performance of the proposed edge-based template matching algorithm has been tested on a Windows 8.1 machine equipped with a 2.5 GHz Intel Core (TM) i7-4710mq CPU and 8 GB of memory. Table 6 tabulates the average processing time and average frame rate of the proposed method with respect to the number of objects in the image. From Table 6, it is clear that the processing speed of the proposed method is dependent on the number of objects. That is, the more objects in the image, the longer the processing time is required. When the number of objects is increased from one to seven in the image, the average processing time of the proposed method is increased from 39 ms to 42 ms. The overall av- Figure 7. Comparisons between the proposed and two recently published methods: experimental results obtained by (a) the BORDER method, (b) the BIND method, and (c) the proposed method.

10 238 Chi-Yi Tsai and Chao-Chun Yu Figure 8. Experimental results of the proposed method tested on the D-textureless dataset [25]: recognition result of (a) the textureless model 0, (b) the textureless model 5, and (c) the textureless model 7. Table 6. Average processing time (in milliseconds) and average frame rate (in fps) of the proposed method Number of detected objects Average processing time (ms) Average frame rate (fps) Overall average erage processing time of the proposed method is about 41ms. Therefore, the average frame rate of the proposed textureless object detection system achieves real-time performance about 24 fps in processing 640-by-480 video streams for detecting up to seven textureless objects in the image. This advantage makes the proposed method applicable to realistic products. 5. Conclusions and Future Work In this paper, a novel textureless object recognition algorithm is proposed based on an edge-based template matching method to detect and recognize a wide variety of textureless objects. To improve the detection accuracy of the proposed system, a template image database of an OOI is automatically generated by applying a group of presetting affine transformations on the template image of the OOI. To detect and recognize the OOI, an efficient edge-based template matching process is performed to find the best match having the highest similarity measure between the template image database and the input image. Experimental results show that the proposed method can efficiently detect and recognize multiple textureless objects in the image. Moreover, the up-down relationship between the recognized objects also can be identified according to the similarity measures. The proposed system achieves real-time performance in processing VGA video streams, which is suitable for many applications of vision-based robotic manipulation. In future work, the proposed method will be integrated into a robot manipulator system to accomplish random binpicking function for picking textureless objects. Acknowledgements The authors would like to thank Jacob Chan of Nanyang Technological University, Singapore, for providing the BORDER and BIND programs and Han-Po Lin of Tamkang University, Taiwan, in collecting the experimental data for this research. This work was supported by the Ministry of Science and Technology (MOST) of Taiwan, ROC under grant MOST E References [1] Lowe, D. G., Distinctive Image Features from Scaleinvariant Keypoints, International Journal of Computer Vision, Vol. 60, No. 2, pp (2004). doi: /B:VISI [2] Bay, H., Tuytelaars, T. and Gool, L. V., SURF: Speeded Up Robust Features, Computer Vision and Image Understanding, Vol. 110, No. 3, pp (2008). doi: /j.cviu [3] Rublee, E., Rabaud, V., Konolige, K. and Bradski, G., ORB: an Efficient Alternative to SIFT or SURF, IEEE International Conference on Computer Vision, Colorado Springs, USA, Jun , pp (2011). doi: /ICCV [4] Costa, M. S. and Shapiro, L. G., 3D Object Recognition and Pose with Relational Indexing, Computer Vi-

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12 240 Chi-Yi Tsai and Chao-Chun Yu New York, pp (1990). [23] Tsai, C.-Y., Yu, C.-J., Yu, C.-C. and Wong, C.-C., Real-time Implementation of a Keypoint-based Automatic Target Selection and Tracking System for Applications of Random Bin Picking, ICIC Express Letters, Part B: Applications, Vol. 7, No. 5, pp (2016). [24] Tsai, C.-Y., Wong, C.-C., Yu, C.-J., Liu, C.-C. and Liu, T.-Y., A Hybrid Switched Reactive-based Visual Servo Control of 5-dof Robot Manipulators for Pick-andplace Tasks, IEEE Systems Journal, Vol. 9, No. 1, pp (2015). doi: /JSYST [25] D-textureless dataset on BOLD/dataset/. Manuscript Received: Aug. 29, 2017 Accepted: Jan. 5, 2018