Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Image Tangent Space for Image Retrieval

Abstract: Image tangent space is actually high-level semantic space learned from low-level feature space by modified local tangent space alignment which was originally proposed for nonlinear manifold learning. Under the assumption that a data point in image space can be linearly approximated by some nearest neighbors in its local neighborhood, we develop a lazy learning method to locally approximate the optimal mapping function between image space and image tangent space. That is, the semantics of a new query image in image space can be inferred by the local approximation in its corresponding image tangent space. While Euclidean distance induced by the ambient space is often used to represent the difference between images, clearly, their natural distance is possibly different from Euclidean distance. Here, we compare three distance metrics: Chebyshev, Manhattan and Euclidean distances, and find that Chebyshev distance outperforms the other two in measuring the semantic similarity during retrieval. Experimental results show that our approach is effective in improving the performance of image retrieval systems.

Euclidean distance transform by stack filters

Abstract: Euclidean distance transform (EDT) using a gray-scale morphological erosion with a large distance structuring element is difficult to implement in real-time Herein, a stack filtering method that permits the implementation of EDT in real-time using only binary logic gates is proposed This letter reports a fast errorless method for EDT implementation

Object presence density measuring instrument

Abstract: PROBLEM TO BE SOLVED: To provide a device with which a detection can be carried out regardless of the color of an object to become a detection target. SOLUTION: The image of a prescribed image pickup area is picked up by a wide angle distance measuring instrument 10 utilizing a conical mirror 20 while changing a focal distance, and a distance image is generated while defining a distance to an object projected at each of points in a reflected image as image data. On the basis of the comparison of a reference image composed of such a distance image and a present image, an object invading the field of vision is detected. Since the presence density of the object is calculated on the basis of distance information, the detection can be carried out regardless of the color of the object to become the detection target.

A hierarchical image matching scheme based on the dynamic detection of interesting points

Abstract: This paper presents a parallel approach to a hierarchical image matching scheme using the Hausdorff distance for object recognition and localization in aerial images. Unlike the conventional matching methods in which edge pixels are considered as image feature pixels, the distance transform and the blind pointwise comparison procedure is simplified and extended in terms of the Hausdorff distance, and a guided image matching system is developed by the hierarchical detection of interesting points via a dynamic thresholding scheme for the search of the best matching between two image sets. Furthermore, the concept of remote procedure call (RPC) in distributed systems is introduced for the parallel implementation to achieve the speedup without specific software and hardware requirements.

Multivariate mathematical morphology and Bayesian classifier application to colour and medical images

Abstract: Multivariate images are now commonly produced in many applications. If their process is possible due to computers power and new programming languages, theoretical difficulties have still to be solved. Standard image analysis operators are defined for scalars rather than for vectors and their extension is not immediate. Several solutions exist but their pertinence is hardly linked to context. In the present paper we are going to get interested in segmentation of vector images also including a priori knowledge. The proposed strategy combines a decision procedure (where points are classified) and an automatic segmentation scheme (where regions are properly extracted). The classification is made using a Bayesian classifier. The segmentation is computed via a region growing method: the morphological Watershed transform. A direct computation of the Watershed transform on vector images is not possible since vector sets are not ordered. So, the Bayesian classifier is used for computing a scalar distance map where regions are enhanced or attenuated depending on their similitude to a reference shape: the current distance is the Mahalanobis distance. This combination allows to transfer the decision function from pixels to regions and to preserve the advantages of the original Watershed transform defined for scalar functions. The algorithm is applied for segmenting colour images (with a priori) and medical images, especially dermatology images where skin lesions have to be detected.