Systems and method for determining, based on a gray scale image, focus of an imaging system. A method of this invention includes the steps of: a) acquiring a gray scale image of a test target, b) binarizing the acquired gray scale image, c) analyzing the binarized acquired image, d) adjusting focus of acquisition optics of the imaging system, and e) repeating steps a) through d) until a substantially maximum number of line pairs of a number of groups of line pairs of the test target is obtained, wherein each group from the number of groups of line pairs has a predetermined spatial frequency.

The present invention facilitates solid object reconstruction from a two-dimensional image. If an object is of known and regular shape, information about the object can be extracted from at least one view by utilizing appropriate constraints and measuring a distance between a camera and the object and/or by estimating a scale factor between a camera image and a real world image. The same device can perform both the image capture and the distance measurement or the scaling factor estimation. The following processes can be performed for object identification: parameter estimation; image enhancement; detection of line segments; aggregation of short line segments into segments; detection of proximity clusters of segments; estimation of a convex hull of at least one cluster; derivation of an object outline from the convex hull; combination of the object outline, shape constraints, and distance value.

Reconstructing the shape of the surface of an object in greater than two dimensions is performed using a noise-tolerant reconstruction process and/or a multi-resolution reconstruction process. The noise-tolerant reconstruction process can be a Bayesian reconstruction process that adds noise information representing the noise distribution in optical image(s) of the object to surface gradient information estimated from the images to determine surface height information that defines the shape of the surface of the object in greater than two dimensions. In the multi-resolution reconstruction process, for each resolution of the image, the surface gradient information is estimated and the surface height information is calculated using the estimated surface gradient information. To obtain the final surface height map, the surface height information from each resolution is combined to reconstruct the shape of the surface of the object in greater than two dimensions. The multi-resolution reconstruction process can be used with the Bayesian reconstruction process or with another decomposition process, such as a wavelet decomposition process.

Methods, systems, and computer program products for acquiring three-dimensional range information are disclosed. According to one aspect, acquiring three-dimensional range information of a scene includes obtaining multiple pixel-shifted images of a scene, where at least one of the images being shifted by a non-integer pixel value with respect to another of the images. Obtaining each of the pixel-shifted images includes generating electromagnetic radiation that is reflected from target objects in the scene about which range information is desired. The reflected electromagnetic radiation is collected using a sensor array of at least two dimensions, where an array of pixels is captured simultaneously. A data value is determined for each captured pixel based on analysis of the collected electromagnetic radiation, by combining the data values from the pixel-shifted images to create a super-resolution image of the scene, and by analyzing the data values of the super-resolution image to determine a three-dimensional location for each pixel of the super-resolution image.

Method, computer program product, and apparatus for displaying a 2D image data set combined with a 3D range data set. A 3D range data set and at least one 2D image data set which differs from the 3D range data set in resolution, origin, or both, are provided. The 3D range data set is represented as a first displayed image while each 2D image data set is represented as at least a second displayed image. Corresponding features are identified within each displayed image. 3D transformation is computed between the 3D range data set and at least one 2D image data set using the specified corresponding features to establish geometric correspondence. At least one 2D image data set is represented as a third displayed image, wherein a 3D coordinate can be computed for any specified pixel of each 2D image data set using the 3D transformation and 3D range data set.