A Cognitive Vision Approach to Image Segmentation

Martin, Vincent; Thonnat, Monique

doi:10.5772/6080

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…Sample supervised segment generation can be seen as an explicit example of more general approaches found at the intersection of evolutionary computation and image analysis/computer vision [19]. A distinction can be made [14] based on the granularity of the search process-whether the search method is used to construct a segmentation algorithm/image processing method [20][21][22][23][24], common with cellular automata, mathematical morphology and genetic programming approaches, or either for tuning the free parameters of an algorithm [14,[25][26][27][28].…”

Section: Sample Supervised Segment Generationmentioning

confidence: 99%

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Abstract:Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid-and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization.

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Section: Sample Supervised Segment Generationmentioning

confidence: 99%

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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“…Using YUV color space and fixed thresholds, they examined 600 DPI pictures to count whiteflies, aphids, and thrips. In the same year, Martin and Thonnan [25] used the adaptive learning technique to adjust optimal parameters segmenting whiteflies out of leaves. Simultaneously, a more complex multidisciplinary cognitive vision approach based on the knowledgebase technique was designed by Boissard et al [5] to count whiteflies on catted rose leaves in 2008.…”

Section: State Of the Artmentioning

confidence: 99%

Scouting of Whiteflies in Tomato Greenhouse Environment Using Deep Learning

Turecek

Vařacha

Turečková

et al. 2021

Smart Innovation, Systems and Technologies

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This study shows the possibilities of how to replace tedious human labor -scouting of yellow sticky traps (YST) for whitefliesusing artificial cognitive vision, specifically deep convolutional network (CNN), as a part of the more complex system -BERABOT. The used CNN is the Faster R-CNN trained by deep transfer learning to substitute human scouting when the low whiteflies infection phase was specifically targeted. The training was conducted on pictures taken inside the heated and lighted tomato production greenhouse of "Bezdínek Farm" in Dolni Lutyne, Czechia. Used pictures were collected in a way suitable for future fully automated robotic applications in the BERABOT system. The achieved results were compared to the scouting results of a professional phytopathologist. The trained employee's scouting results against the professional phytopathologist accomplished root-mean-square error (RMSE) equal to 4.23 while the developed CNN model was evaluated to be 5.83. The results presented here open up new frontiers for further CNN model tuning leading to the potential in substituting an employee(S) in the future and make tomato production less expensive and less human labor dependent.

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“…For example, the use of AI technologies, such as the K-nearest neighbors (KNN), logistic regression, decision tree, support vector machine (SVM), and deep convolutional neural network (CNN) models may accurately and precisely help farmers to detect, classify insect pests and suggest the appropriate pesticides [12]. Such AI techniques are mostly recommended as certain insect pests are tiny and hard to detect [13]. Additionally, AI techniques are essential because of the potential to identify pests in images using low-cost RGB cameras [14].…”

Section: Introductionmentioning

confidence: 99%

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton

Kiobia

Mwitta

Fue

et al. 2023

Sensors

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Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system’s detection accuracy.

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A Cognitive Vision Approach to Image Segmentation

Cited by 10 publications

References 36 publications

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Scouting of Whiteflies in Tomato Greenhouse Environment Using Deep Learning

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton

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