K. Ohba

Bio: K. Ohba is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: 3D single-object recognition & Similarity measure. The author has an hindex of 2, co-authored 2 publications receiving 204 citations.

Detectability, uniqueness, and reliability of eigen windows for stable verification of partially occluded objects

Abstract: This paper describes a method for recognizing partially occluded objects for bin-picking tasks using eigenspace analysis, referred to as the "eigen window" method, that stores multiple partial appearances of an object in an eigenspace. Such partial appearances require a large amount of memory space. Three measurements, detectability, uniqueness, and reliability, on windows are developed to eliminate redundant windows and thereby reduce memory requirements. Using a pose clustering technique, the method determines the pose of an object and the object type itself. We have implemented the method and verified its validity.

Recognition of the multi specularity objects for bin-picking task

Abstract: This paper describes a method for recognizing partially occluded objects for bin-picking tasks using the eigen-space analysis. Although effective in recognizing an isolated object, as was shown by Murase and Nayar (1995), the current method can not be applied to piratically occluded objects that are typical in bin-picking tasks. The analysis also requires that the object is centered in an image before recognition. These limitations of the eigen-space analysis are due to the fact that the whole appearance of an object is utilized as a template for the analysis. We propose a new method, referred to as the "eigen-window" method, that stores multiple partial appearances of an object in the eigen-space. Such partial appearances require a large number of memory space. To reduce the memory requirement by avoiding redundant windows and to select only effective windows to be stored, a similarity measure among windows is developed. Using a pose clustering method among windows, the method determines the pose of an object and the object type of itself. We have implemented the method and verify the validity of the method.

Object recognition from local scale-invariant features

Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

Using spin images for efficient object recognition in cluttered 3D scenes

Abstract: We present a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin image representation. The spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.

Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class

Abstract: The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.

Recognition without Correspondence using MultidimensionalReceptive Field Histograms

Abstract: The appearance of an object is composed of local structure. This local structure can be described and characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This article presents a technique where appearances of objects are represented by the joint statistics of such local neighborhood operators. As such, this represents a new class of appearance based techniques for computer vision. Based on joint statistics, the paper develops techniques for the identification of multiple objects at arbitrary positions and orientations in a cluttered scene. Experiments show that these techniques can identify over 100 objects in the presence of major occlusions. Most remarkably, the techniques have low complexity and therefore run in real-time.