Set Of Blobs Research Articles

Zooming in: From spatially extended traveling waves to localized structures: The case of the Sine-Gordon equation in (1+3) dimensions.

The Sine-Gordon equation in (1+3) dimensions has N-traveling front (“kink”, “domain wall”)- solutions for all N ≥ 1. A nonlinear functional of the solution, which vanishes on a single-front, maps multi-front solutions onto sets of infinitely long, but laterally bounded, rods, which move in space. Each rod is localized in the vicinity of the intersection of two Sine-Gordon fronts. The rod systems are solutions of the linear wave equation, driven by a term that is constructed out of Sine-Gordon fronts. An additional linear operation maps multi-rod systems onto sets of blobs. Each blob is localized in the vicinity of rod intersection, and moves in space. The blob systems are solutions of the linear wave equation, driven by a term that is also constructed out of Sine-Gordon fronts. The temporal evolution of multi-blob solutions mimics elastic collisions of systems of spatially extended particles.

색상 분포 및 인체의 상황정보를 활용한 다중카메라 기반의 사람 대응

In this paper, we proposed a method which corresponds people under the structured spaces with multiple cameras. The correspondence takes an important role for using multiple camera system. For solving this correspondence, the proposed method consists of three main steps. Firstly, moving objects are detected by background subtraction using a multiple background model. The temporal difference is simultaneously used to reduce a noise in the temporal change. When more than two people are detected, those detected regions are divided into each label to represent an individual person. Secondly, the detected region is segmented as features for correspondence by a criterion with the color distribution and context information of human body. The segmented region is represented as a set of blobs. Each blob is described as Gaussian probability distribution, i.e., a person model is generated from the blobs as a Gaussian Mixture Model (GMM). Finally, a GMM of each person from a camera is matched with the model of other people from different cameras by maximum likelihood. From those results, we identify a same person in different view. The experiment was performed according to three scenarios and verified the performance in qualitative and quantitative results.

Multiple Camera-based Correspondences of People using Camera Networks and Context Information

Abstract This paper proposes a method to correspondence a same person from different views. To design a system with multi vision sensors, we should to solve the correspondence problem. For obtain a correspondence, a proposed method has three steps. The first step is to detect moving objects by background subtraction from multiple background model. The background model is generated from image sequence and it updates by itself in each process. The temporal difference is used jointly to compensate a limitation of multiple background model. The detected silhouettes are divided as individual by labeling when more than two people are detected in an image. The second step is to generate blobs from detected silhouette. The silhouette is segmented as blobs by a criterion with color range and context information of human body. A person is composed of a set of blobs. The blob set is represented as a model by estimation of Gaussian mixture model (GMM). In the final step, a GMM of detected person from a camera is matched with other GMMs from different cameras to identify a same person.

Context-dependent segmentation and matching in image databases

The content of an image can be summarized by a set of homogeneous regions in an appropriate feature space. When exact shape is not important, the regions can be represented by simple “blobs.” Even for similar images, the blob representation of the two images might vary in shape, position, the number of blobs, and the represented features. In addition, separate blobs in one image might correspond to a single blob in the other image and vice versa. In this paper we present the BlobEMD framework as a novel method to compute the dissimilarity of two sets of blobs while allowing for context-based adaptation of the image representation. This results in representations that represent well the original images but at the same time are best aligned with respect to the representations of the context images. Similarly, we can perform image segmentation where the segmentation of an image is guided by a reference image. This novel approach makes segmentation a context-based task. We compute the blobs by using Gaussian mixture modeling and use the Earth mover’s distance (EMD) to compute both the dissimilarity of the images and the flow-matrix of the blobs between the images. The BlobEMD flow-matrix is used to find optimal correspondences between source and target image representations and to adapt the representation of the source image to that of the target image. This allows for similarity measures between images that are insensitive to the segmentation process and to different levels of details of the representation. We show applications of this method for content-based image retrieval, image segmentation, and matching models of heavily dithered images with models of full resolution images.

A Data Mining Approach for Analyzing Density Maps Representing Macromolecular Structures

Results of electron microscopy-based three-dimensional reconstructions of macromolecules or their complexes are usually stored as density maps. Each point (“voxel”) in the map represents a density value and one approach for studying details of the map is to display an isosurface enclosing areas of interest. We have taken a data mining approach not only focusing on the areas of immediate interest but determining all possible separate entities (“blobs”) from a density map. After the entire density map is analyzed with our mining program BLOBBER, properties of all detected blobs can be browsed and sets of blobs can be visualized using our VIZBLOB program. Since BLOBBER analyzes density maps using only density information and relates it to spatial relationships, BLOBBER can be used to analyze symmetrical or asymmetrical density maps from any source. To test our program we have analyzed published bacteriophage PRD1 reconstructions. We identified various structural details ranging from individual proteins to major complexes such as the whole capsid shell and more elaborate details of possible connections between membrane interfaces. This approach can also be a useful preprocessing tool for visualizing reconstructions.