Object Detection Defintion object detection is "Given an image, to determine whether or not the object is present, and, if present, determine the locations and sizes of each objects". Issues: shape variance, lighting variance, Approaches Image Feature Extraction, Feature Transform, Machine Learning Image Feature Extraction is to extract informations about objects from raw images Feature Transform Machine Learning Global Approach 
Part-based Approach Deformable objects or articulate objects can be described as a combination of their parts, so that these objects can be properly described using graph models. Graph Structures Different objects, due to their distinct structure and texture properties, might results in different graph models. for example, Composition model, Constellation Model, and Pictorial Model. Optimization of computations Computational speed is another issue in the part-based method object detection algorithms. Simple graph model usually results in faster computation while complicated graph models need optimization in the computations. Dynamic program, belief propagation algorithms are already applied to accelerate the computation. Articulated Object Detection Defintion Articulated objects are usually referred to "A multi-body system composed of at least two rigid components and at most six independent degrees of freedom between any two components". For example, human bodies can be regarded as articulated objects where the arms, legs, head and torch are rigid objects. Other examples of articulated objects include human hands and animals. Issues Despite its wide applications, the research on articulated object detection is still limited to experimental systems. That is, there are still no reliable practical commercial systems in the markets because of the difficulty of detecting articulated objects. The difficulty lies in two aspects: the shape variance and the self-occlusion. Because the large number of degrees of freedom of articulated objects, it is hard to build a shape model to model all possible shapes of articulated objects, although some researchers did build such models. The other factor is the self-occlusion of articulated objects. Approaches Previous articulated object detecting systems, in order to deal with the large shape variance, either take the "pose-based" approaches or part-based approaches. References [1]J.M.Coughlan, D. Snow, C. English, and A.L.Yuille, "Efficient Deformable Template, Detection and Localization without User Initialization". Computer Vision and Image Understanding. 78, pp 303-319, 2000
[2] P.Felzenswalb, "Representation and Detection of Deformable Shapes". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 2. 2005
[3] J.M.Coughlan, , and S. Ferreira, "Finding Deformable Shapes using Loopy Belief Propoagation". In Proceedings European Conference of Computer Vision.. 2002.
[4] J.M. Coughlan, , and H. Shen, "Shape Matching with Belief Propagation: Using Dynamic Quantization to Accomodate Occlusion and Clutter". In GMBV. 2004
[5]Dong-Qing Zhang and Shih-Fu Chang, A Generative-Discriminative Hybrid Method for Multi-View Object Detection, CVPR 2006 [6]D.Crandall and P.Felzenszwalb. Spatial priors for part-based recognition using statistical models, CVPR 2005 [7]A.Holub and P.Perona. A discriminative framework for modeling object class. CVPR 2005 [8]R.Fergus, P.Perona, and A.Zisserman. Object class recognition by unsupervised scale-invariant learning, CVPR 2003 [9]M.Weber, M.Welling, and P.Perona, towards automatic discovery of object categories | |