Automatic object segmentation from calibrated images

This paper addresses the problem of automatically obtaining the object/background segmentation of a rigid 3D object observed in a set of images that have been calibrated for camera pose and intrinsics. Such segmentations can be used to obtain a shape representation of a potentially texture-less object by computing a visual hull. We propose an automatic approach where the object to be segmented is identified by the pose of the cameras instead of user input such as 2D bounding rectangles or brush-strokes. The key behind our method is a pairwise MRF framework that combines (a) foreground/background appearance models, (b) epipolar constraints and (c) weak stereo correspondence into a single segmentation cost function that can be efficiently solved by Graph-cuts. The segmentation thus obtained is further improved using silhouette coherency and then used to update the foreground/background appearance models which are fed into the next Graph-cut computation. These two steps are iterated until segmentation convergences. Our method can automatically provide a 3D surface representation even in texture-less scenes where MVS methods might fail. Furthermore, it confers improved performance in images where the object is not readily separable from the background in colour space, an area that previous segmentation approaches have found challenging. © 2011 IEEE.

青海海北高寒草甸黄嘴朱顶雀亲鸟递食率

中国科学院海北高寒草甸生态系统定位站的黄嘴朱顶雀 (Acanthis flavirostris)雌雄亲鸟的育幼行为数据收集于1999～2001年的3个繁殖季节(5～8月). 观察使用了一个闭路电视系统, 包括放在鸟巢附近5～10处的摄像头和35～40m外的监视器. 数据分析表明: 亲鸟的递食率无年间变化; 雌、雄鸟递食率没有差异; 总递食率不随雏鸟日龄和日间不同时段改变, 但雌鸟在傍晚以前的递食率低于雄鸟并在傍晚高于雄鸟. 在雏鸟6日龄前, 雌鸟总递食率低于雄鸟, 且二者逐步逼近, 然后稳定在同一水平. 此种性别差异与雌鸟在递食以外所承担的, 为雏鸟保温、遮阳以及清除雏鸟粪便等事务有关, 这些结果与单配制鸟类亲本投资理论一致。

Purification, characterization and primary structure of a chymotrypsin inhibitor from Naja atra venom

A chymotrypsin inhibitor, designated NA-CI, was isolated from the venom of the Chinese cobra Naja atra by three-step chromatography. It inhibited bovine (x-chymotrypsin with a K-i of 25 nM. The molecular mass of NA-CI was determined to be 6403.8 Da by matrix-assisted laser-desorption ionization time-of-flight (MALDI-TOF) analysis. The complete amino acid sequence was determined after digestion of S-carboxymethylated inhibitor with Staphylococcus aureus V8 protease and porcine trypsin. NA-CI was a single polypeptide chain composed of 57 amino acid residues. The main contact site with the protease (PI) has a Phe, showing the specificity of the inhibitor. NA-CI shared great similarity with the chymotrypsin inhibitor from Naja naja venom (identities = 89.5%) and other snake venom protease inhibitors. (C) 2003 Elsevier Inc. All rights reserved.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.