Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 4 Articles
This work demonstrates how a high throughput\nrobotic machine vision systems can quantify seedling\ndevelopment with high spatial and temporal resolution.The\nthroughput that the system provides is high enough to match\nthe needs of functional genomics research. Analyzing images\nof plant seedlings growing and responding to stimuli is a\nproven approach to finding the effects of an affected gene.\nHowever, with 104 genes in a typical plant genome, comprehensive\nstudies will require high throughput methodologies.\nTo increase throughput without sacrificing spatial or\ntemporal resolution, a 3 axis robotic gantry system utilizing\nvisual servoing was developed. The gantry consists of\ndirect drive linear servo motors that can move the cameras\nat a speed of 1 m/s with an accuracy of 1 ?m, and a repeatability\nof 0.1 ?m. Perpendicular to the optical axis of the\ncameras was a 1 m2 sample fixture holds 36 Petri plates in\nwhich 144 Arabidopsis thaliana seedlings (4 per Petri plate)\ngrew vertically along the surface of an agar gel. A probabilistic\nimage analysis algorithm was used to locate the root\nof seedlings and a normalized gray scale variance measure\nwas used to achieve focus by servoing along the optical axis.\nRotation of the sample holder induced a gravitropic bending\nresponse in the roots, which are approximately 45 ?m wide\nand several millimeter in length. The custom hardware and\nsoftware described here accurately quantified the gravitropic\nresponses of the seedlings in parallel at approximately 3 min\nintervals over an 8-h period. Here we present an overview of\nour system and describe some of the necessary capabilities\nand challenges to automating plant phenotype studies....
Multimedia event detection (MED) is a challenging\nproblem because of the heterogeneous content and variable\nquality found in large collections of Internet videos. To\nstudy the value of multimedia features and fusion for representing\nand learning events from a set of example video clips,\nwe created SESAME, a system for video SEarch with Speed\nand Accuracy for Multimedia Events. SESAME includes\nmultiple bag-of-words event classifiers based on single data\ntypes: low-level visual, motion, and audio features; highlevel\nsemantic visual concepts; and automatic speech recognition.\nEvent detection performance was evaluated for each\nevent classifier. The performance of low-level visual and\nmotion features was improved by the use of difference coding.\nThe accuracy of the visual concepts was nearly as strong\nas that of the low-level visual features. Experiments with a\nnumber of fusion methods for combining the event detection\nscores from these classifiers revealed that simple fusion\nmethods, such as arithmetic mean, perform as well as or better\nthan other, more complex fusion methods. SESAME�s performance in the 2012 TRECVID MED evaluation was\none of the best reported....
One important aspect of assessing the quality in\npulp and papermaking is dirt particle counting and classification.\nKnowing the number and types of dirt particles present\nin pulp is useful for detecting problems in the production\nprocess as early as possible and for fixing them. Since manual\nquality control is a time-consuming and laborious task,\nthe problem calls for an automated solution using machine\nvision techniques. However, the ground truth required to train\nan automated system is difficult to ascertain, since all of the\ndirt particles should be manually segmented and classified\nbased on image information. This paper proposes a framework\nfor developing and tuning dirt particle detection and\nclassification systems. To avoid manual annotation, dry pulp\nsheets with a single dirt type in each were exploited to generate\nsemisynthetic images with the ground truth information.\nTo classify the dirt particles, a set of features were com-puted for each image segment. Sequential feature selection\nwas employed to determine a close-to-optimal set of features\nto be used in classification. The framework was tested both\nwith semisynthetically generated images based on real pulp\nsheets and with independent original real pulp sheets without\nany generation. The results of the experiments show that\nthe semisynthetic procedure does not significantly change\nthe properties of images and has little effect on the particle\nsegmentation. The feature selection proved to be important\nwhen the number of dirt classes changes since it allows to\nimprove the classification results. Using the standard classification\nmethods, it is possible to obtain satisfactory results,\nalthough the methods modeling the data, such as the Bayesian\nclassifier using the Gaussian Mixture Model, show better\nperformance....
Model fitting is a fundamental component in\ncomputer vision for salient data selection, feature extraction\nand data parameterization. Conventional approaches such as\nthe RANSAC family show limitations when dealing with\ndata containing multiple models, high percentage of outliers\nor sample selection bias, commonly encountered in computer\nvision applications. In this paper, we present a novel model\nevaluation function based on Gaussian-weighted Jensenââ?¬â??\nShannon divergence, and integrate into a particle swarm optimization\n(PSO) framework using ring topology. We avoid\ntwo problems from which most regression algorithms suffer,\nnamely the requirements to specify inlier noise scale and the\nnumber of models. The novel evaluation method is generic\nand does not require any estimation of inlier noise. The continuous\nand meta-heuristic exploration facilitates estimation\nof each individual model while delivering the number of\nmodels automatically. Tests on datasets comprised of inlier\nnoise and a large percentage of outliers (more than 90 %\nof the data) demonstrate that the proposed framework can\nefficiently estimate multiple models without prior information.\nSuperior performance in terms of processing time and\nrobustness to inlier noise is also demonstrated with respect\nto state of the art methods....
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