Texture is one of the visual features used in Content Based Image Retrieval (CBIR) to represent the contents of the image with respect to the characteristics brightness, color, shape, size, etc. Texture is a property that represents spatial distribution of an Image. Texture can be defined as a repetition of an element or pattern in a problem space. Texture analysis can be used for classificatio...

In this paper, we present a new image segmentation method based on the concept of sparse subset selection. Starting with an over-segmentation, we adopt local spectral histogram features to encode the visual information of the small segments into high-dimensional vectors, called superpixel features. Then, the superpixel features are fed into a novel convex model which efficiently leverages the f...

Large collections of images can be indexed by projections on a few “eigenfeatures”, the dominant eigenvectors of the images covariance matrix. A preliminary step of registering the images is common practice. A quantitative analysis of what is being gained by registration was not performed in previous work, and heuristics were used to determine on what to register the images. We show that the re...

Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussia...

In many cases complex scenes are composed of densely occluded objects, which means that only a small fraction of the scene is visible, while the remaining parts are occluded. This property can be exploited to accelerate rendering by simply removing most of the hidden parts before sending the polygons to the graphics hardware. In recent years, a lot of so called occlusion-culling methods have be...

This paper presents a statistical method termed Information Sampling for selecting the most relevant data from an a priori set of images. These data could be a single pixel or a number scattered throughout an image. The main problem addressed is how to determine which image data points contain the most relevant information. As distinct from other techniques, we utilize the inherent information ...

Adequate user authentication is a persistent problem, particularly with mobile devices such as Personal Digital Assistants (PDAs), which tend to be highly personal and at the fringes of an organization's influence. Yet these devices are being used increasingly in military and government agencies, hospitals, and other business settings, where they pose a risk to security and privacy, not only fr...

The “shape-from-contour method” reconstructs the 3D shape of the surface of an object by extracting its contour from each of a series of successive images of the object. This can be realized by using a CCD camera, and is a relatively accurate method of obtaining environmental information. However, to obtain an accurate result, many images must be processed. Therefore, it is important to select ...

Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image segmentation, the a priori distribution should capture the knowledge about objects. Taking inspiration from (Alvarez et al., 1999), we design a prior density that penalizes the area of homogeneous parts in images. The segmentation problem is fur...