Representation Learning for Contextual Object and Region Detection in Remote Sensing

Date

2014-08-28

Author

Firat, Orhan
Can, Gulcan
Yarman Vural, Fatoş Tunay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

196
views

0
downloads

The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is further represented under the Conditional Markov Random Fields framework. As a second task, end-to-end target detection using convolutional sparse auto-encoders (CSA) using large amount of unlabelled data is analysed. Proposed methodologies are tested on complex airfield detection problem using Conditional Random Fields and recognition of dispersal areas, park areas, taxi routes, airplanes using CSA. The method is also tested on the detection of the dry docks in harbours. Performance of the proposed method is compared with standard feature engineering methods and found competitive with currently used rule-based and supervised methods.

Subject Keywords

Feature extraction, Vectors, Remote sensing, Object detection, Image resolution, Airplanes, Training

URI

https://hdl.handle.net/11511/43878

DOI

https://doi.org/10.1109/icpr.2014.637

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Scale invariant representation of 2 5D data AKAGUNDUZ, Erdem; ULUSOY PARNAS, İLKAY; BOZKURT, Nesli; Halıcı, Uğur (2007-06-13) In this paper, a scale and orientation invariant feature representation for 2.5D objects is introduced, which may be used to classify, detect and recognize objects even under the cases of cluttering and/or occlusion. With this representation a 2.5D object is defined by an attributed graph structure, in which the nodes are the pit and peak regions on the surface. The attributes of the graph are the scales, positions and the normals of these pits and peaks. In order to detect these regions a "peakness" (or pi...
Segmentation Driven Object Detection with Fisher Vectors Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2013-01-01) We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks...
Training object detectors by directly optimizing lrp metric Çam, Barış Can; Akbaş, Emre; Kalkan, Sinan; Department of Computer Engineering (2020-9) This thesis focuses on training deep object detection networks by directly optimizing the localisation-recall-precision (LRP) performance metric that can evaluate classification and localisation performance of an object detector in a unified manner (Oksuz et al., 2018). To achieve this goal, unlike the commonly used linear weighting approach, we aim to implicitly optimize the LRP metric first by using a bounded localisation loss from previous works and proposing a loss function that can bound the range ...
Object recognition and segmentation via shape models Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016) In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...
OBJECT RECOGNITION AND LOCALIZATION WITH ULTRASONIC-SCANNING KIRAGI, H; Ersak, Aydın (1994-04-14) In this paper an object recognition and localization system based on ultrasonic range imaging to be used in optically opaque environments is introduced. The system is especially designed for robotics applications. The ultrasonic image is acquired by scanning ultrasonic transducers in two dimensions above the area where objects are located. The features that are used for recognition and localization processes are extracted from the outermost boundaries of the objects present in the input scene. Experimental ...

Citation Formats

O. Firat, G. Can, and F. T. Yarman Vural, “Representation Learning for Contextual Object and Region Detection in Remote Sensing,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43878.