A Fast Approach to Texture-Less Object Detection Based on Orientation Compressing Map and Discriminative Regional Weight  
[PERSON]  
1 Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; 1 [EMAIL_ADDRESS] (H.Q. ); [EMAIL_ADDRESS] (M.P. )14 Correspondence: [EMAIL_ADDRESS]; Tel. : +86-25-84896490-4426  
based on region, a regression-based method usually achieves better real-time performance by using end-to-end detection frameworks. Typical regression-based CNN methods include You Only Look Once (YOLO) [11] and Single Shot multibox Detector (SSD) [12]. The regression-based methods are improved by multi-view object detection [13] and light field imaging by [14] in terms of accuracy and dimension. Despite the progress, there are also notable limitations for deep leaning methods. First, the deep leaning methods are much dependent on big training dataset which are usually lacking in texture-less object detection applications. Second, the training for neural networks with deep architecture is quite time-consuming [15]. The object detection methods based on the manual designed features methods could not automatically extract abundant representative features from the vast training samples as deep leaning methods. But these methods are attractive for object detection because they need neither a large training set nor a time-consuming training stage, which can be implemented efficiently. Nowadays, the methods of texture-less object detection based on the manual designed features can be divided into descriptor-based methods and template-based methods. Descriptor-based methods are widely used in texture-less object detection. While texture-rich objects can be detected under severe occlusions with distinctive local features, such as Scale Invariant Feature Transform (SIFT) [16] and Speeded Up Robust Features (SURF) [17], but these methods fail on texture-less objects which have large uniform regions. The technique assumes a feature point aggregation approach [18; 19; 20; 21], often adopting a partial SIFT-like pipeline to describe its grouped feature points. Some of descriptor-based methods like SIFT and SURF detect very few key points on texture-less objects and local descriptors extracted on these key points are less discriminative because only similar edges exist around the key points. To enhance the local features, the SIFT descriptor and an improved spatial sparse coding Bag-Of-Words model (BOW) [22] were integrated for objects feature extraction and representation, but object location is largely disturbed by cluttered background. [PERSON] [23] came up with Bunch of Lines Descriptor (BOLD) features to describe and detect line segments in the pipeline of SIFT-feature description and detection. It begins by applying an original Line Segment Detector (LSD) [24] algorithm for image to obtain line segment. Then BOLD used line-segment midpoints as interest-points to aggregate nearby segments via k-nearest neighbours (kNN), and the descriptor was built by unique angle primitives over pairs of neighboring segments. The kNN classifier can be used for many applications of signal and image processing [25; 26]. Given recognition performance, another work that resembles BOLD is Bounding Oriented Rectangle Descriptor for Enclosed Regions (BORDER) [27], which achieved an impressive recognition rate. The BORDER uses an adaptation of LSD to obtain equal fragmented of each extensive line-segment termed Linelets. The improvement demonstrates better resistance in the presence of occlusion. In! order to achieve scale and rotation invariance, the BORDER proposed the oriented rectangle template, which is deployed in a multi-scale rectangle scheme to enhance robustness of occlusion. Subsequently, encompassed regions are sampled linearly to accumulate rotation-invariant angle primitives [23] to generate its descriptors. For the descriptor, the key factor besides delivering exclusiveness is rotation invariance.