Spatial and Temporal Enhancement of Depth Images Captured by a Time-of-Flight Depth Sensor

2010 ◽  
Author(s):  
Sung-Yeol Kim ◽  
Ji-Ho Cho ◽  
Andreas Koschan ◽  
Mongi A. Abidi
Keyword(s):  
Time Of Flight ◽  
Depth Sensor ◽  
Depth Images

scholarly journals Locomotion Strategy Selection for a Hybrid Mobile Robot Using Time of Flight Depth Sensor

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Artur Saudabayev ◽  
Farabi Kungozhin ◽  
Damir Nurseitov ◽  
Huseyin Atakan Varol
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Supervisory Control ◽  
Time Of Flight ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Strategy Selection ◽  
Depth Sensor ◽  
Depth Images ◽  
Locomotion Mode

The performance of a mobile robot can be improved by utilizing different locomotion modes in various terrain conditions. This creates the necessity of having a supervisory controller capable of recognizing different terrain types and changing the locomotion mode of the robot accordingly. This work focuses on the locomotion strategy selection problem for a hybrid legged wheeled mobile robot. Supervisory control of the robot is accomplished by the terrain recognizer, which classifies depth images obtained from a commercial time of flight depth sensor and selects different locomotion mode subcontrollers based on the recognized terrain type. For the terrain recognizer, a database is generated consisting of five terrain classes (Uneven, Level Ground, Stair Up, Stair Down, and Nontraversable). Depth images are enhanced using confidence map based filtering. The accuracy of the terrain classification using Support Vector Machine classifier for the testing database in five-class terrain recognition problem is 97%. Real-world experiments assess the locomotion abilities of the quadruped and the capability of the terrain recognizer in real-time settings. The results of these experiments show depth images processed in real time using machine learning algorithms can be used for the supervisory control of hybrid robots with legged and wheeled locomotion capabilities.

scholarly journals RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2815
Author(s):  
Shih-Hung Yang ◽  
Yao-Mao Cheng ◽  
Jyun-We Huang ◽  
Yon-Ping Chen
Keyword(s):  
Receptive Field ◽  
American Sign Language ◽  
Multiple Scales ◽  
Receptive Fields ◽  
Depth Image ◽  
Small Scale ◽  
Feature Maps ◽  
Depth Sensor ◽  
Depth Images ◽  
Effective Transfer

Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the discriminative details of these fingers, RFaNet reweights the low-level features of the hand depth image with those of the non-forearm image and improves finger localization, even when the wrist is occluded. RFaNet captures neighboring and inter-region dependencies between fingers in high-level features. An atrous convolution procedure enlarges the RFs at multiple scales and a non-local operation computes the interactions between multi-scale feature maps, thereby facilitating the building of inter-finger relations. Thus, the representation of a sign is invariant to viewpoint changes, which are primarily responsible for intra-class variability. On an American Sign Language fingerspelling dataset, RFaNet achieved 1.77% higher classification accuracy than state-of-the-art methods. RFaNet achieved effective transfer learning when the number of labeled depth images was insufficient. The fingerspelling representation of a depth image can be effectively transferred from large- to small-scale datasets via highlighting the finger regions and building inter-finger relations, thereby reducing the requirement for expensive fingerspelling annotations.

Dimensions of the Modern Pig

2018 ◽  
Vol 61 (5) ◽  
pp. 1729-1739 ◽  
Author(s):  
Isabella C. F. S. Condotta ◽  
Tami M. Brown-Brandl ◽  
John P. Stinn ◽  
Gary A. Rohrer ◽  
Jeremiah D. Davis ◽  
...  
Keyword(s):  
Animal Husbandry ◽  
Finishing Pigs ◽  
Depth Sensor ◽  
Livestock Farming ◽  
Body Depth ◽  
Depth Images ◽  
Engineering Standards ◽  
Feed Form ◽  
Current Engineering ◽  
Physical Dimensions

Abstract. It is important to know the physical dimensions of livestock to properly design confined animal housing facilities as well as feeding and drinking equipment. An engineering standard for the dimensions of livestock and poultry published by ASABE reports swine dimensions that were originally published in 1968. Changes in animal husbandry practices for swine, such as improved and new genetic lines, nutrition and feed form, and improved facility and equipment design, make it necessary to validate or update these dimensions for modern animals. The objective of this study was to evaluate dimension data for the grow-finish stages of modern pigs. A total of 150 growing-finishing pigs were sampled at five approximate ages: 4, 8, 12, 16, and 20 weeks old (30 animals at each age). The animals equally represented three commercial sire lines (Landrace, Duroc, and Yorkshire), and equal numbers of barrows and gilts were sampled. Dorsal and lateral color digital and depth images were collected using a Kinect sensor as the pigs were held individually in a stanchion or scale, and the images were analyzed by manual and automated methods. Measured physical dimensions included height from top of back to the floor, length from nose to base of the tail, width at shoulders, jowl length, front leg height, body depth from top of back to lowest point of the belly, and others. It was determined that the conformation of modern pigs has changed from the dimensions reported in current engineering standards such that modern pigs tend to be wider (15.1%) and shorter in height (-10.2%) and length (-4.9% on average) between 4 and 20 weeks of age. These updated pig dimensions will enable engineers to better design modern swine equipment and facilities. Keywords: Depth sensor, Dimensions, Image analysis, Precision livestock farming, Swine.

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