Anàlisi d'un sistema pel reconeixement d'objectes en imatges en temps real

En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismes En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismes En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismes En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismes En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismes En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismes En l'àmbit del reconeixement d'imatges pretenem analitzar i augmentar el rendiment d'un dels millors algorismesIn the field of image recognition, we aim to analyze and improve performance of one of the best algorithms known nowadays to detect all objects in an image in less than a second. Our main purpose is to understand how it works, analyze the algorithmic complexity involved and get some estimations on the possible improvements as regards the ability to detect objects as the speed with which it does. We will show the results from the entire analysis of algorithmic complexity and what this implies in terms of chances for improvement. We have also included the importance of the input parameters of the algorithm randomized Prim (RP) accompanied by a study what values can increase its performance

Benchmark evaluation of object segmentation proposal

Abstract. In this research, we provide an in depth analysis and evaluation of four recent segmentation proposals algorithms on PASCAL VOC benchmark. The principal goal of this study is to investigate these object detection proposal methods in an un-biased evaluation framework. Despite having a widespread application, the strengths and weaknesses of different segmentation proposal methods with respect to each other are mostly not completely clear in the previous works. This thesis provides additional insights to the segmentation proposal methods. In order to evaluate the quality of proposals we plot the recall as a function of average number of regions per image. PASCAL VOC 2012 Object categories, where the methodologies show high performance and instances where these algorithms suffer low recall is also discussed in this work. Experimental evaluation reveals that, despite being different in the operational nature, generally all segmentation proposal methods share similar strengths and weaknesses. The analysis also show how one could select a proposal generation method based on object attributes. Finally we show that, improvement in recall can be obtained by merging the proposals of different algorithms together. Experimental evaluation shows that this merging approach outperforms individual algorithms both in terms of precision and recall

Prime object proposals with randomized Prim's algorithm

Generic object detection is the challenging task of proposing windows that localize all the objects in an image, regardless of their classes. Such detectors have recently been shown to benefit many applications such as speeding-up class-specific object detection, weakly supervised learning of object detectors and object discovery. In this paper, we introduce a novel and very efficient method for generic object detection based on a randomized version of Prim's algorithm. Using the connectivity graph of an image's super pixels, with weights modelling the probability that neighbouring super pixels belong to the same object, the algorithm generates random partial spanning trees with large expected sum of edge weights. Object localizations are proposed as bounding-boxes of those partial trees. Our method has several benefits compared to the state-of-the-art. Thanks to the efficiency of Prim's algorithm, it samples proposals very quickly: 1000 proposals are obtained in about 0.7s. With proposals bound to super pixel boundaries yet diversified by randomization, it yields very high detection rates and windows that tightly fit objects. In extensive experiments on the challenging PASCAL VOC 2007 and 2012 and SUN2012 benchmark datasets, we show that our method improves over state-of-the-art competitors for a wide range of evaluation scenarios. © 2013 IEEE.Manen S., Guillaumin M., Van Gool L., ''Prime object proposals with randomized Prim's algorithm'', Proceedings 14th international conference on computer vision - ICCV 2013, pp. 2536-2543, December 3-6, 2013, Sydney, Australia.status: publishe