Robust Duplicate Detection of 2D and 3D Objects

In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters in a number of applications. Furthermore, effectiveness of object duplicate detection algorithm is measured over different object classes. The authors’ method is shown to be robust in detecting the same objects even when images with objects are taken from different viewpoints or distances.

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Geospatial 2D and 3D object-based classification and 3D reconstruction of ISO-containers depicted in a LiDAR data set and aerial imagery of a harbor

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2015.7326747 ◽

2015 ◽

Cited By ~ 1

Author(s):

Dirk Tiede ◽

Sebastian d'Oleire-Oltmanns ◽

Andrea Baraldi

Keyword(s):

3D Reconstruction ◽

Aerial Imagery ◽

Lidar Data ◽

Data Set ◽

3D Object ◽

Object Based ◽

2D And 3D

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AN OBJECT-BASED WORKFLOW DEVELOPED TO EXTRACT AQUACULTURE PONDS FROM AIRBORNE LIDAR DATA: A TEST CASE IN CENTRAL VISAYAS, PHILIPPINES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-1147-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1147-1152 ◽

Cited By ~ 4

Author(s):

R. A. Loberternos ◽

W. P. Porpetcho ◽

J. C. A. Graciosa ◽

R. R. Violanda ◽

A. G. Diola ◽

...

Keyword(s):

Detection Algorithm ◽

The Philippines ◽

Airborne Lidar ◽

Surface Model ◽

Lidar Data ◽

Flat Surfaces ◽

Water Classification ◽

Object Based ◽

Aquaculture Ponds ◽

Model Layer

Traditional remote sensing approach for mapping aquaculture ponds typically involves the use of aerial photography and high resolution images. The current study demonstrates the use of object-based image processing and analyses of LiDAR-data-generated derivative images with 1-meter resolution, namely: CHM (canopy height model) layer, DSM (digital surface model) layer, DTM (digital terrain model) layer, Hillshade layer, Intensity layer, NumRet (number of returns) layer, and Slope layer. A Canny edge detection algorithm was also performed on the Hillshade layer in order to create a new image (Canny layer) with more defined edges. These derivative images were then used as input layers to perform a multi-resolution segmentation algorithm best fit to delineate the aquaculture ponds. In order to extract the aquaculture pond feature, three major classes were identified for classification, including land, vegetation and water. Classification was first performed by using assign class algorithm to classify Flat Surfaces to segments with mean Slope values of 10 or lower. Out of these Flat Surfaces, assign class algorithm was then performed to determine Water feature by using a threshold value of 63.5. The segments identified as Water were then merged together to form larger bodies of water which comprises the aquaculture ponds. The present study shows that LiDAR data coupled with object-based classification can be an effective approach for mapping coastal aquaculture ponds. The workflow currently presented can be used as a model to map other areas in the Philippines where aquaculture ponds exist.

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Feature Based Face Detection Algorithm for Digital Still Images

Advances in Intelligent and Soft Computing - Image Processing and Communications Challenges 3 ◽

10.1007/978-3-642-23154-4_7 ◽

2011 ◽

pp. 59-64 ◽

Cited By ~ 2

Author(s):

Tomasz Orczyk ◽

Piotr Porwik

Keyword(s):

Face Detection ◽

Detection Algorithm ◽

Still Images ◽

Feature Based

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Conditioning 3D object-based models to dense well data

Computers & Geosciences ◽

10.1016/j.cageo.2018.02.006 ◽

2018 ◽

Vol 115 ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Yimin C. Wang ◽

Michael J. Pyrcz ◽

Octavian Catuneanu ◽

Jeff B. Boisvert

Keyword(s):

3D Object ◽

Object Based ◽

Well Data

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Haptic Rendering: Practical Modeling and Collision Detection

10.1115/imece1999-0011 ◽

1999 ◽

Author(s):

ZhuLiang Cai ◽

John Dill ◽

Shahram Payandeh

Keyword(s):

Surgical Training ◽

Collision Detection ◽

Object Representation ◽

Virtual Assembly ◽

Detection Algorithm ◽

Haptic Rendering ◽

3D Object ◽

Modeling Techniques ◽

Simulation Results ◽

Fast Collision

Abstract 3D collision detection and modeling techniques can be used in the development of haptic rendering schemes which can be used, for example, in surgical training, virtual assembly, or games. Based on a fast collision detection algorithm (RAPID) and 3D object representation, a practical haptic rendering system has been developed. A sub-system determines detailed collision information. Simulation results are presented to demonstrate the practicality of our results.

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CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

NED University Journal of Research ◽

10.35453/nedjr-ascn-2019-0100 ◽

2021 ◽

Author(s):

Shah bano ◽

Syed Adnan Shah ◽

Wakeel Ahmad ◽

Muhammad Ilyas

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Classifier ◽

Binary Code ◽

Spatial Information ◽

Detection Algorithm ◽

Support Vector ◽

Surveillance Systems ◽

Body Parts ◽

Public Places ◽

Surveillance Camera

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.

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