Quantitative and Qualitative Evaluation of Performance and Robustness of Image Stitching Algorithms

Dissanayake, Vipula; Herath, Sachini; Rasnayaka, Sanka; Seneviratne, Sachith; Vidanaarachchi, Rajith; Gamage, Chandana

doi:10.1109/dicta.2015.7371297

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…As in Dissanayake V. et al study [68], a visual evaluation and similarity comparison [69] with the real state of the garden parcel was carried out. The final validation was obtained when CityVeg was tested as a whole and was able to precisely move above each one of the plants (see Section 3.3.…”

Section: Image Stitchingmentioning

confidence: 99%

Design and Implementation of an Urban Farming Robot

2022

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Urban agriculture can be shortly defined as the growing of plants and/or the livestock husbandry in and around cities. Although it has been a common occupation for the urban population all along, recently there is a growing interest in it both from public bodies and researchers, as well as from ordinary citizens who want to engage in self-cultivation. The modern citizen, though, will hardly find the free time to grow his own vegetables as it is a process that requires, in addition to knowledge and disposition, consistency. Given the above considerations, the purpose of this work was to develop an economic robotic system for the automatic monitoring and management of an urban garden. The robotic system was designed and built entirely from scratch. It had to have suitable dimensions so that it could be placed in a balcony or a terrace, and be able to scout vegetables from planting to harvest and primarily conduct precision irrigation based on the growth stage of each plant. Fertigation and weed control will also follow. For its development, a number of technologies were combined, such as Cartesian robots’ motion, machine vision, deep learning for the identification and detection of plants, irrigation dosage and scheduling based on plants’ growth stage, and cloud storage. The complete process of software and hardware development to a robust robotic platform is described in detail in the respective sections. The experimental procedure was performed for lettuce plants, with the robotic system providing precise movement of its actuator and applying precision irrigation based on the specific needs of the plants.

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Section: Image Stitchingmentioning

confidence: 99%

Design and Implementation of an Urban Farming Robot

2022

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“…A patch similarity metric has also been used to quantify misalignments, even for areas where points are not extracted. This metric is an indirect measurement of the misalignment error, e.g., by using structural similarity (SSIM) [5,13,14], normalized cross-correlation [10,11], or the peak signal-tonoise ratio [15]. The concept of the patch similarity metric is that the larger the misalignment, the less the similarity between the patches in the overlap area.…”

Section: Quality Assessment Of Stitched Imagesmentioning

confidence: 99%

“…Several studies have attempted to quantify the quality of output panoramic images for an objective evaluation and comparison of the performance [5,[10][11][12][13][14][15][16][17][18][19]. However, they only focused on the resulting panoramic image, without considering whether the parallax of the input image was objectively large or small.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment Method of Image Stitching Performance Based on Estimation of Planar Parallax

Jung

Hong

2021

IEEE Access

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While parallax-tolerant image stitching is a relatively mature field, the performances of image stitching methods have been assessed subjectively and qualitatively. These methods primarily provide the stitched image itself to demonstrate the performance, rather than quantitative data. Although several objective assessment methods have been proposed for quantifying the quality of stitched images, only the stitched output images have been analyzed, without considering the parallax level in each input image. We propose a method for quantifying the parallax level of the input images and clustering them accordingly. This facilitates a quantitative assessment of the various stitching methods for each parallax level. The parallax levels of the images are grouped based on the magnitude and variation in the planar parallax, as estimated with the proposed metric using matching errors and patch similarity. The existing image stitching methods are compared experimentally in terms of the residual misalignment errors, based on 73 pairs of different levels of parallax images originally classified in this study. Among the existing methods, the elastic local alignment method exhibits the least error. The shape-preserving half-projective method produces a larger misalignment error, but creates a natural panorama with less geometric distortion. We introduce a quantitative assessment method for considering the parallax of input images in image stitching methods. It can aid in specifying their performances, and in finding an appropriate method depending on the parallax level of the input images.INDEX TERMS Image alignment, image stitching, planar parallax, plane + parallax, quantitative assessment.

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“…Wang et al [34] proposed a noticeable quality metric named the structural similarity (SSIM) index. In the last several years, the SSIM index has been widely used to evaluate the qualities of color correction [35] and image mosaicking methods [36,37]. Here, we apply the SSIM index to evaluate the quality of the detected seamlines.…”

Section: Quality Assessmentmentioning

confidence: 99%

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

Yao

Liu

et al. 2017

Remote Sensing

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When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index.

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Quantitative and Qualitative Evaluation of Performance and Robustness of Image Stitching Algorithms

Cited by 13 publications

References 7 publications

Design and Implementation of an Urban Farming Robot

Design and Implementation of an Urban Farming Robot

Quantitative Assessment Method of Image Stitching Performance Based on Estimation of Planar Parallax

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

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