Depth Estimation Using Blob Detection for Stereo Vision Images

Charles, R. Arokia Priya; Patil, Anupama V.

doi:10.1007/978-981-15-8354-4_22

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…R. Arokia Priya Charles and Anupama V. Patil proposed a methodology for object detection using a dual-camera system. The developed method processes images using thresholding and blob detection techniques to find the centers of objects and accurately determine their distances using stereographic depth values (Charles et al, 2021).…”

Section: Proposed Algorithmmentioning

confidence: 99%

Advancing defense capabilities through integration of electro-optical systems and computer vision technologies

Elri,

Ergüzen

2024

JCEEES

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This paper presents a comprehensive study on the integration of cutting-edge technologies to enhance defense capabilities, focusing on object detection, tracking, and distance measurement tasks. Also, the integration of IMX219-77 cameras and Nvidia Jetson Nano for object detection, tracking, and distance measurement in electro-optical systems is proposed. The decision to combine these components was based on their individual strengths: the high resolution and wide field of view of the IMX219-77 cameras, and the low-power, high-performance capabilities of the Jetson Nano. Software-wise, OpenCV and GStreamer were chosen for their widespread use and capabilities in computer vision and multimedia processing tasks, respectively. The MOSSE algorithm was selected for object tracking due to its speed, efficiency, and resilience to lighting changes. Additionally, Depth Estimation (Stereo Vision) was employed for distance measurement by analyzing images captured from a pair of cameras. The proposed algorithm involves transmitting images from the cameras to the Jetson Nano, where OpenCV and GStreamer are utilized to process the data. The MOSSE algorithm is employed for object detection and tracking, while Depth Estimation generates depth maps to measure distances between objects and the camera. The study's results demonstrate accurate distance measurements with a margin of error ranging from 0 to 2 mm, falling below the acceptable threshold mentioned in academic literature. This aligns with similar studies in the field, showcasing the efficacy of the proposed integration for object detection, tracking, and distance measurement in electro-optical systems.

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Section: Proposed Algorithmmentioning

confidence: 99%

Advancing defense capabilities through integration of electro-optical systems and computer vision technologies

Elri,

Ergüzen

2024

JCEEES

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“…This region's growing image segmentation method accurately analyzes textures [19]. Region growing is the process of grouping pixels to form a more extensive region based on predetermined criteria or parameters.…”

Section: Binary Large Object (Blob)mentioning

confidence: 99%

“…This segmentation method detects pixel dots that contain brightness from the background and unites them into a region. In other words, BLOB groups a pixel with other similar pixels using neighboring and labeling concepts and then separates them into image parts [19].…”

mentioning

confidence: 99%

Extraction of Chest Girth and Body Length Features to Estimate Goat Weight

Nahari,

Ulum,

Alfita

2023

Iraqi Journal of Science

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Measuring goat weight on a farm is carried out by two methods. The first method conventionally measures the chest girth (CG) and body length (BL), while the second is performed using a scale. This study aimed to facilitate the CG and BL measurements to estimate goat weight more efficiently through image processing. The endpoints of CG and BL were determined automatically using binary large object (BLOB) segmentation. The images of 120 goats were taken at three different shooting distances of 50 cm, 70 cm, and 90 cm. The statistical testing of the three scenarios in this study explained that the ideal distance to take pictures is the basis for determining whether or not the size of the circumference and BL is appropriate as a feature for estimating the weight of goats based on image processing. Statistical analysis showed that the features of CG and BL at a distance of 50 cm and 70 cm were not significantly different from the measurements of CG and BL of goats carried out directly (actual data). In comparison, the results of testing the feature prediction data at a distance of 90 cm are significantly different from the actual data.

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