There is an increasing need for automatically segmenting the regions of different landforms from a multispectral satellite image. The problem of Landform classification using data only from a 3-band optical sensor (IRS-series), in the absence of DEM (Digital Elevation Model) data, is complex due to overlapping and confusing spectral reflectance from several different landform classes. We propose a hierarchical method for landform classification for identifying a wide variety of landforms occurring over parts of the Indian subcontinent. At the first stage, the image is classified into one of three broad categories: Desertic, Coastal or Fluvial, using decision fusion of three SVMs (Support Vector Machine). In the second stage, the image is then segmented into different regions of landforms, specifically belonging to the class (category) identified at stage 1. To show the improvement in accuracy of our classification method, the results are compared with two other methods of classification.

Close The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1558-884X) immediately upon acceptance. Submit Manuscript Fusion of 3D Appearance and 2D Shape Cues for Generic Object Recognition Sunando Sengupta, Manisha Kalra, Sukhendu Das JPRR Vol 3, No 1 (2008); doi:10.13176/11.40

This paper addresses the problem of Generic Object Recognition by modeling the perceptual capability of human beings. In contrast to the traditional approaches, we have approached the recognition problem by proposing a framework which involves two stages of processing. First, an intelligent generic recognizer based on independent component analysis (ICA) is employed to reduce the search space to a few rank-ordered samples. It is shown that ICA captures the appearance characteristics of objects. Shape cues (distance transform based matching) are then used to verify the result of the appearance-based classifier and identify the correct object class and pose. Experiments were conducted using objects with complex appearance and shape characteristics. Sensitivity of recognition to the number of independent components and number of learning samples is analyzed on COIL-100 database. The performance of the generic classifier using ICA with and without shape matching is also analyzed.

This paper addresses the problem of pose invariant Generic Object Recognition by modeling the perceptual capability of human beings. We propose a novel framework using a combination of appearance and shape cues to recognize the object class and viewpoint (axis of rotation) as well as determine its pose (angle of view). The appearance model of the object from multiple viewpoints is captured using Linear Subspace Analysis techniques and is used to reduce the search space to a few rank-ordered candidates. We have used a decision-fusion based combination of 2D PCA and ICA to integrate the complementary information of classifiers and improve recognition accuracy. For matching based on shape features, we propose the use of distance transform based correlation. A decision fusion using ‘Sum Rule' of 2D PCA and ICA subspace classifiers, and distance transform based correlation is then used to verify the correct object class and determine its viewpoint and pose. Experiments were conducted on COIL-100 and IGOIL (IITM Generic Object Image Library) databases which contain objects with complex appearance and shape characteristics. IGOIL database was captured to analyze the appearance manifolds along two orthogonal axes of rotation.

This paper addresses the problem of Generic Object Recognition (GOR) from arbitrary viewpoints by modeling the perceptual capability of human beings. We propose a novel framework which uses a combination of 3D appearance and 2D shape cues to recognize the object class as well as determine its pose. We propose a hierarchical framework for GOR, which combines two stages of processing. First, the 3D appearance model of the object is captured from multiple viewpoints using Linear Subspace Analysis techniques. These appearance cues are used to reduce the search space to a few rank-ordered samples. We have used a decision-fusion based combination of 2D PCA and ICA to integrate the complementary information of classifiers and improve appearance-based recognition accuracy. Shape matching is then performed on the reduced search space, using either distance transform based correlation or shape context based matching. The proposed framework for GOR uses a decision fusion technique, in which evidences from 3D appearance and 2D shape are combined (fused) to obtain the correct object class and its pose. Experiments were conducted using objects with complex appearance and shape characteristics, and the performance of the proposed framework has been shown to be superior, using the COIL-100 and IGOIL (IITM Generic Object Image Library) databases. IGOIL database was also used to analyze the appearance manifolds along two orthogonal axis of rotation. Performance degradation in case of noisy images has also been presented.