Automatic Shadow Removal by Illuminance in HSV Color Space

Huang, W.; Kim, Kyoung Yeon; Yang, Yong; Kim, Yoo-Sung

doi:10.13189/csit.2015.030303

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…For example, in a study where 11 different color spaces (RGB, normalized rgb, XYZ, L*a*b*, L*u*v*, HSV, HLS, YCrCb, YUV, I1I2I3 and TSL) were compared to segment lettuce plants and soil from a set of images, the L*a*b* color space was shown to achieve superior classification, with 99.2% accuracy [34]. While there is no single optimum color space for any image classification, we chose to transform our RGB dataset into an HSV color model, as this model has been shown to be robust to illumination variations and removing shadow effects [35][36][37]. After performing HSV transformation, we observed high variation in the pixel values of the hue channel, especially for the soil class, since the soils have a reddish hue with values just above 0 and just below 240.…”

Section: Discussionmentioning

confidence: 99%

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

et al. 2018

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Aerial imaging provides a landscape view of crop fields that can be utilized to monitor plant diseases. Phymatotrichopsis root rot (PRR) is a serious root rot disease affecting several dicotyledonous hosts, including the perennial forage crop alfalfa. PRR disease causes stand loss by spreading as circular to irregular diseased areas that increase over time, but disease progression in alfalfa fields is poorly understood. The objectives of this study were to develop a workflow to produce PRR disease maps from sets of high-resolution red, green and blue (RGB) images acquired from two different platforms and to assess the feasibility of using these PRR disease maps to monitor disease progression in alfalfa fields. Aerial RGB images, two from unmanned aircraft systems (UAS) and four images from a manned aircraft platform were acquired at different time points during the 2014-2015 growing seasons from a center-pivot irrigated, PRR-infested alfalfa field near Burneyville, OK. Supervised classification of images acquired from both platforms were performed using three spectral signatures: image-specific, UAS-platform-specific and manned-aircraft platform-specific. Our results showed that the UAS-platform-specific spectral signature was most efficient for classifying images acquired with the UAS, with accuracy ranging from 90 to 96%. In contrast, manned-aircraft-acquired images classified using image-specific spectral signatures yielded 95 to 100% accuracy. The effect of hue, saturation and value color space transformations (HSV and Hrot60SV) on classification accuracy was determined, but the accuracy estimates showed no improvement in their efficiency compared to the RGB color space. Finally, the data showed that the classification of the bare ground increased by 74% during the study period, indicating the extent of alfalfa stand loss caused by PRR disease. Thus, this study showed the utility of high-resolution RGB aerial images for monitoring PRR disease spread in alfalfa.

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Section: Discussionmentioning

confidence: 99%

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

et al. 2018

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“…Figure 5 shows the flowchart for determining shadow for feature 1. As the first step of Figure 5 , the ROI of RGB color space is converted to that of HSV color space [ 35 ]. In the HSV color space, the V component is a direct measure of intensity.…”

Section: Proposed Methodsmentioning

confidence: 99%

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

Hoang

Baek

Cho

et al. 2017

Sensors

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Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

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“…Initially, we came up with different image processing algorithms. Starting with HSV [2] discrimination base tracking but it was light-dependent, then SURF a detection algorithm this works with feature extraction and matching process on each frame but due to irregular motion of object and change of perspective made our search for an alternative [3], [4]. Then we integrate the Lucas-Kunade method (KLT) [5] to track motion and store the tracking coordinates but when several objects came in the video feed it gets disturbed.…”

Section: Figure 2: Drag An Object To Trackmentioning

confidence: 99%

Vision-based autonomous object tracking using multi-rotor UAV

Hashmi

Abdullah

Khan

et al. 2022

NIJEC

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In this paper- we are presenting a real-time method to detect and track an autonomous objectusing visual processing on an unmanned aerial vehicle using an on-board companioncomputer (Jetson-TX1) for image processing. The profile of objects, frame rate of images,and unexpected motion make it hard to detect and track the object for a long period. To caterto this, we came up with an algorithm that was developed for long-term tracking which makesuse of Discriminative correlation filter with Channel and Spatial Reliability. The majorrestriction of our algorithm arises in the presence of occlusion, which was solved by creatinga region of interest in the center of the frame in which the object will always reside. If theobject exits the center region a command of left, right, top or bottom will be generatedfollowing the position of the object relative to our center position. These commands will becommunicated to the UAV via Mavlink protocol. Experimental results show that we haveachieved a long period of tracking with a good frame rate and eliminating spurious events andmisdetections.

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Automatic Shadow Removal by Illuminance in HSV Color Space

Cited by 11 publications

References 11 publications

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

Vision-based autonomous object tracking using multi-rotor UAV

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