Skip to:Content
Localization of certain animal species in images via training neural networks with image patches için kapak resmi
Localization of certain animal species in images via training neural networks with image patches
Orhan, Semih, author.
Yazar Ek Girişi:
Fiziksel Tanımlama:
xiii, 42 leaves: illustrarions, charts;+ 1 computer laser optical disc.
Object detection is one of the most important tasks for computer vision systems. Varying object size, varying view angle, illumination conditions, occlusion etc. effect the success rate. In recent years, convolutional neural networks (CNNs) have shown great performance in different problems of computer vision including object detection and localization. In this work, we propose a novel training approach for CNNs to localize some animal species whose bodies have distinctive pattern, such as speckles of leopards, black-white lines of zebras, etc. To learn characteristic patterns, small patches are taken from different body parts of animals and they are used to train models. To find object location, in a test image, all locations are visited in a sliding window fashion. Crops are fed to CNN, then classification scores of all patches are recorded. To illustrate object location, heat map is generated by the classification scores of the patches. Afterwards, heat maps are converted to binary images and end up with bounding box estimates of objects. The localization performance of our Patch-based training is compared with Faster R-CNN – a state-of-the-art CNN-based object detection and localization algorithm. While evaluating the performances, in addition to the standard precision-recall metric, we use area-precision and area-recall which represent the potential of Patch-based Model better. Experiment results show that the proposed training method has better performance than Faster R-CNN for most of the evaluated classes. We also showed that Patch-based Model can be used with Faster R-CNN to increase its localization performance.
Yazar Ek Girişi:
Tek Biçim Eser Adı:
Thesis (Master)--İzmir Institute of Technology: Computer Engineering.

İzmir Institute of Technology: Computer Engineering--Thesis (Master).
Elektronik Erişim:
Access to Electronic Versiyon.


Materyal Türü
Demirbaş Numarası
Yer Numarası
Durumu/İade Tarihi
Tez T001695 QA76.87 .O68 2017

On Order

Go to:Top of Page