Object representation for multi-beam sonar image using local higher-order statistics

Object representation for multi-beam sonar image using local higher-order statistics

Multi-beam sonar imaging has been widely used in various underwater tasks such as object recognition and object tracking. Problems remain, however, when the sonar images are characterized by low signal-to-noise ratio, low resolution, and amplitude alterations due to viewpoint changes. This paper investigates the capacity of local higher-order statistics (HOS) to represent objects in multi-beam ...

Erratum to: Object representation for multi-beam sonar image using local higher-order statistics

Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images

This paper presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of p...

Image Restoration using Higher Order Statistics

Most of the techniques for image restoration are based on some known degradation models. But in many situations it is difficult to accurately measure the degradation factors or noise type that is the real motivation behind the use of blind deconvolution technique for image restoration. Here the observed degraded image is restored without having any prior knowledge about the noise type. Most of ...

IDIAP Higher - Order Statistics in Visual Object

In this paper, we develop a higher-order statistical theory of matching models against images. The basic idea is not only to take into account how much of an object can be seen in the image, but also what parts of it are jointly present. We show that this additional information can improve the speciicity (i.e., reduce the probability of false positive matches) of a recognition algorithm. We dem...