Prototype analysis of different object recognition techniques in image processing

In picture processing, picture segmentation, object classification and recognition is an impressive concept in present days because of increasing computer vision applications in real time applications. Based on required application, divide a picture into particular relevant clusters based on equivalent partitioning. Object identification is a minimum work to identify objects in a given picture or video with pixel values. The main representation of object classification from a given picture is to detect picture instances with respect to categorization of object. Object recognition is a complex task, when the pictures are poor quality, occlusion, and noise and in picture with background cutter sequences, this becomes more and efficient challenging task in picture processing. To appear object categorization and recognition effectively in pictures, there are several methods and approaches were introduced to solve object representation effectively. In this paper we review the different techniques of contextual feature extraction with respect to picture information of object classification, categorization and recognition of scalability and optimizations in real time picture processing applications. Our research gives better review of different approaches in picture categorization.

[1]  Pietro Perona,et al.  Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition , 2007, International Journal of Computer Vision.

[2]  Björn Ommer,et al.  Beyond straight lines — Object detection using curvature , 2011, 2011 18th IEEE International Conference on Image Processing.

[3]  Aykut Erdem,et al.  Dissimilarity between two skeletal trees in a context , 2009, Pattern Recognit..

[4]  Antonio Torralba,et al.  Object Recognition by Scene Alignment , 2007, NIPS.

[5]  M. Bar.  Visual objects in context , 2004, Nature Reviews Neuroscience.

[6]  Reza Oji,et al.  An Automatic Algorithm for Object Recognition and Detection Based on ASIFT Keypoints , 2012, ArXiv.

[7]  Chih-Hsien Hsia,et al.  An efficient object recognition system for humanoid robot vision , 2009, 2009 Joint Conferences on Pervasive Computing (JCPC).

[8]  Serge J. Belongie,et al.  Context based object categorization: A critical survey , 2010, Comput. Vis. Image Underst..

[9]  Khushboo Khurana,et al.  Techniques for Object Recognition in Images and Multi-Object Detection , 2013 .

[10]  B. Poornima,et al.  SEGMENTATION AND OBJECT RECOGNITION USING EDGE DETECTION TECHNIQUES , 2010 .

[11]  Cordelia Schmid,et al.  Viewpoint-independent object class detection using 3D Feature Maps , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Ilia Voronkov,et al.  Object Detection using Image Processing , 2016, ArXiv.

[13]  Sven J. Dickinson,et al.  Object categorization using bone graphs , 2011, Comput. Vis. Image Underst..

[14]  P. Fua,et al.  Pose estimation for category specific multiview object localization , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Farshad Tajeripour,et al.  Full Object Boundary Detection by Applying Scale Invariant Features in a Region Merging Segmentation Algorithm , 2012, ArXiv.

[16]  Longin Jan Latecki,et al.  Path Similarity Skeleton Graph Matching , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Sven J. Dickinson,et al.  Bone graphs: Medial shape parsing and abstraction , 2011, Comput. Vis. Image Underst..

[18]  Alaa El-Din Sayed Hafez,et al.  Template Match Object Detection for Inertial Navigation Systems , 2011 .

[19]  Li Dong,et al.  HOG based multi-stage object detection and pose recognition for service robot , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[20]  Nando de Freitas,et al.  A Statistical Model for General Contextual Object Recognition , 2004, ECCV.

[21]  Daphne Koller,et al.  A segmentation-aware object detection model with occlusion handling , 2011, CVPR 2011.

[22]  Serge J. Belongie,et al.  Object categorization using co-occurrence, location and appearance , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Fahad Shahbaz Khan,et al.  Color attributes for object detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Silvio Savarese,et al.  Multi-view Object Categorization and Pose Estimation , 2010, Computer Vision: Detection, Recognition and Reconstruction.