In this work, we present a new approach to interest point detection. Different types of features in images are detected by using a common computational concept. The proposed approach considers the total variability of local regions. The total sum of squares computed on the intensity values of a local circular region is divided into three components: between-circumferences sum of squares, between-radii sum of squares, and the remainder. These three components normalized by the total sum of squares represent three new saliency measures, namely, radial, tangential, and residual. The saliency measures are computed for regions with different radii and scale spaces are built in this way. Local extrema in scale space of each of the saliency measures are located. They represent features with complementary image properties: blob-like features, corner-like features, and highly textured points. Results obtained on image sets of different object classes and image sets under different types of photometric and geometric transformations show high robustness of the method to intraclass variations as well as to different photometric transformations and moderate geometric transformations and compare favorably with the results obtained by the leading interest point detectors from the literature. The proposed approach gives a rich set of highly distinctive local regions that can be used for object recognition and image matching.

Presents an approach to a typical task of active sensing, namely how to position a camera in order to acquire the necessary geometric information to determine the maximal height of each object in the scene. The possible camera directions are limited to assure the precise data acquisition. First, the top view of the scene is obtained, which gives the arrangement of the objects and provides the geometric information necessary to compute the azimuth values for the side views. We have designed a method which determines the minimal number of side views that are sufficient to accomplish the task. Experimental results demonstrate the utility of the proposed method, which can be used in industrial environments, in particular for tasks related to robot grasping.

A strategy for acquiring 3-D data of an unknown scene, using range images obtained by a light stripe range finder is addressed. The foci of attention are occluded regions, i.e., only the scene at the borders of the occlusions is modeled to compute the next move. Since the system has knowledge of the sensor geometry, it can resolve the appearance of occlusions by analyzing them. The problem of 3-D data acquisition is divided into two subproblems due to two types of occlusions. An occlusion arises either when the reflected laser light does not reach the camera or when the directed laser light does not reach the scene surface. After taking the range image of a scene, the regions of no data due to the first kind of occlusion are extracted. The missing data are acquired by rotating the sensor system in the scanning plane, which is defined by the first scan. After a complete image of the surface illuminated from the first scanning plane has been built, the regions of missing data due to the second kind of occlusions are located. Then, the directions of the next scanning planes for further 3-D data acquisition are computed.>

The strategy of acquiring 3-D data of unknown scenes is discussed. The analysis is limited to range images obtained by a light stripe range finder. Prior knowledge given to the system is the knowledge of the sensor geometry. The emphasis on the occluded regions, i.e. their shape and the height of the scene at their borders. Since the system has knowledge of the sensor geometry it can resolve the appearance of occlusions by analyzing them.>