Projected Texture for 3D Object Recognition

Avinash Sharma (homepage)

Three dimensional objects are characterized by their shape, which can be thought of as the variation in depth over the object, from a particular view point. These variations could be deterministic as in the case of rigid objects or stochastic for surfaces containing a 3D texture. These depth variations are lost during the process of imaging and what remains is the intensity variations that are induced by the shape and lighting, as well as focus variations. Algorithms that utilize 3D shape for classification tries to recover the lost 3D information from the intensity or focus variations or using additional cues from multiple images, structured lighting, etc. This process is computationally intensive and error prone. Once the depth information is estimated, one needs to characterize the object using shape descriptors for the purpose of classification. Image-based classification algorithms try to characterize the intensity variations of the image for recognition. As we noted, the intensity variations are affected by the illumination and pose of the object. The attempt of such algorithms is to derive descriptors that are invariant to the changes in lighting and pose. Although image based classification algorithms are more efficient and robust, their classification power is limited as the 3D information is lost during the imaging process. Our problem is to find an image-based recognition method, which utilize the shape of the object, without explicitly recovering the 3D shape of the object. This implicitly avoids the high computational cost of shape recovery while achieving high accuracies. The method should be robust to view variation, occlusion and also should invariant to scale and position of the object. It should also handle partially specular and a texture-less object surfaces. We propose the use of structured lighting patterns, which we refer to as {\em projected texture}, for the purpose of object recognition. The depth variations of the object induces deformations in the projected texture, and these deformations encode the shape information. The primary idea is to view the deformation pattern as a characteristic property of the object and use it directly for classification instead of trying to recover the shape explicitly. To achieve this we need to use an appropriate projection pattern and derive features that sufficiently characterize the deformations. The patterns required could be quite different depending on the nature of the object shape and its variation across the objects. Specifically, we look at three different recognition problems and propose appropriate projection patterns, deformation characterizations, and recognition algorithms for each. The first category of objects are of fixed shape and pose, where minor differences in shape are to be used for discriminating between classes. 3D hand geometry recognition is taken as the example of class of objects. The second class of recognition problem is that of category recognition of rigid objects from arbitrary view points. We propose a classification algorithm based on popular bag-of-words paradigm for object recognition. Third problem is that of 3D texture classification, where the depth variation in surface is stochastic in nature. We propose a set of simple texture features that can capture the deformations in projected lines on 3D textured surfaces. The above mentioned approaches have been implemented, verified, tested, and compared on various datasets collected as well as available on the Internet. The analysis and comparative results demonstrate significant improvement over the existing approaches, in terms of accuracy and robustness. (more...)

Related Publications

• Avinash Sharma and Anoop M. Namboodiri - Object Category Recognition with Projected Texture IEEE Sixth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP 2008), pp. 374-381, 16-19 Dec,2008, Bhubaneswar, India. [PDF]
  

• Visesh Chari, Avinash Sharma, Anoop M Namboodiri and C.V. Jawahar - Frequency Domain Visual Servoing using Planar Contours IEEE Sixth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP 2008), pp. 87-94, 16-19 Dec,2008, Bhubaneswar, India. [PDF]
  

• Avinash Sharma and Anoop M. Namboodiri - Projected Texture for Object Classification Proceedings of the 10th European Confernece on Computer Vision (ECCV 2008), 12-18 Oct, 2008, France. [PDF]

• Avinash Sharma, Nishant Shobhit and Anoop M. Namboodiri - Projected Texture for Hand Geometry based Authentication Proceedings of CVPR Workshop on Biometrics, 28 June, Anchorage, Alaska, USA. IEEE Computer Society 2008. [PDF]
  

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