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dissertacao_mestrado_jose-grimaldo.pdf: 19406681 bytes, checksum: d108855fa0fb0d44ee5d1cb59579a04c (MD5) / Accuracy in image object detection has been usually achieved at the expense of much computational load. Therefore a trade-o between detection performance and fast execution commonly represents the ultimate goal of an object detector in real life applications. Most images are composed of non-trivial amounts of background nformation, such as sky, ground and water. In this sense, using an object detector against a recurring
background pattern can require a signi cant amount of the total processing time. To
alleviate this problem, search space reduction methods can help focusing the detection procedure on more distinctive image regions. Among the several approaches for search space reduction, we explored saliency information to organize regions based on their probability of containing objects. Saliency detectors are capable of pinpointing regions which generate stronger visual stimuli based solely on information extracted from the image. The fact that saliency methods do not require prior training is an important bene t, which allows application of these techniques in a broad range of machine vision domains. We propose a novel method toward the goal of faster object detectors. The proposed method was grounded on a multi-scale spectral residue (MSR) analysis using saliency detection. For better search space reduction, our method enables ne control of search scale, more robustness to variations on saliency intensity along an object length and also a direct way to control the balance between search space reduction and false negatives caused by region selection. Compared to a regular sliding window search over the images, in our experiments, MSR was able to reduce by
75% (in average) the number of windows to be evaluated by an object detector while
improving or at least maintaining detector ROC performance. The proposed method was
thoroughly evaluated over a subset of LabelMe dataset (person images), improving detection
performance in most cases. This evaluation was done comparing object detection
performance against di erent object detectors, with and without MSR. Additionally, we
also provide evaluation of how di erent object classes interact with MSR, which was done
using Pascal VOC 2007 dataset. Finally, tests made showed that window selection performance
of MSR has a good scalability with regard to image size. From the obtained data,
our conclusion is that MSR can provide substantial bene ts to existing sliding window
detectors. / Salvador
| Identifer | oai:union.ndltd.org:IBICT/oai:192.168.11:11:ri/13203 |
| Date | 11 October 2013 |
| Creators | Silva Filho, José Grimaldo da |
| Contributors | Oliveira, Luciano Rebouças de |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFBA, instname:Universidade Federal da Bahia, instacron:UFBA |
| Rights | info:eu-repo/semantics/openAccess |
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