Sow Localization in Thermal Images Using Gabor Filters

Almadani, Iyad; Abuhussein, Mohammed; Robinson, Andrew

doi:10.1007/978-3-030-98012-2_44

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…CNNs are renowned for their effectiveness in various tasks, such as image classification (Moinuddin et al (2022) and image segmentation ( Almadani et al (2022)). CNNs' superior performance, compared to other techniques like Support Vector Machines (SVM), stems from their ability to extract high-and low-level features from an image using its spatial filter.…”

Section: Model Architecturementioning

confidence: 99%

EGAN: An Ensemble Adversarial Network for Topology-Preserving EEG Data Generation for Predicting Cognitive Load Levels

Havugimana,

Moinuddin,

Yeasin

2024

Preprint

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Learning representations and extracting meaningful patterns from Electroen- cephalogram (EEG) recordings is critical for analyzing cognitive events (e.g., predicting cognitive load). The primary challenges include individual variability, technical noise from unreliable sensor-skin contacts, and rapid temporal changes in the EEG recordings. Given the multi-factorial nature of the problems, deep learning models are natural choices for learning representations from the data. However, the extensive time required for data collection limits the number of subjects (samples) available, which is essential for building robust deep learn- ing models. We introduce an ensemble generative adversarial network (EGAN) to generate high-fidelity EEG data. The EGAN generates multi-channel EEG recordings and their spatial-spectral representation. Key design constraints were preserving topological structure and maintaining proper bias-variance trade-offs for building robust models. To ensure the quality of the synthetic data, we visually inspected the data generated by EGAN. We conducted spectral analyses to con- firm that the quality and spectral similarity were comparable to EEG recordings. To illustrate the efficacy of data generated by EGAN, we developed a convolu- tional neural network (CNN) model to predict four levels of cognitive load (CL). We used spatial-spectral representations (topomap) from three frequency bands (i.e., θ, α, and β). We trained our model on real data and a mixture of origi- nal and EGAN-generated data with varying proportions (e.g., 50%, 70%, 100%) of real data for training. We compared the performance of models trained solely on original data to those trained on mixed data. Our results indicate that CNN models trained on EGAN-supplemented data significantly outperformed those trained using only real data across all three frequency bands and all training set proportions. For example, the model trained on the mixed data with 50% real data achieved classification F1 and accuracy scores of approximately 71%, 69%, and 72% for θ, α, and β bands, respectively. The same model trained on only real data achieved approximately 52%, 57%, and 59% accuracy and F1 scores for the respective bands. These results demonstrate the utility of EGAN in gener- ating high-fidelity EEG data, significantly advancing cognitive load analysis and modeling other cognitive events.

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Section: Model Architecturementioning

confidence: 99%

EGAN: An Ensemble Adversarial Network for Topology-Preserving EEG Data Generation for Predicting Cognitive Load Levels

Havugimana,

Moinuddin,

Yeasin

2024

Preprint

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“…However, when it comes to thermal images, only a limited number of these algorithms produce satisfactory outcomes. One such effective combination is the utilization of Gabor with Histogram of Oriented Gradients (HOG) technique [18]. This algorithm analyzes the gradients and extracts features of the image to identify regions of rapid intensity changes, which are indicative of edges.…”

Section: B Edge-based Segmentationmentioning

confidence: 99%

Review of Unsupervised Segmentation Techniques on Long Wave Infrared Images

Abuhussein¹,

Robinson²,

Almadani³

2023

IJACSA

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This paper studies the different unsupervised segmentation algorithms that have been proposed and their efficacy on thermal images. The scope of this research is to develop a generalized approach to blindly segment urban thermal imagery to assist the system in identifying regions by shape instead of pixel values. Most methods can be classified as thresholding, edgebased, region-based, clustering, or texture analysis. We explained methods, worked before applying the methods of interest on thermal images of 8-bit and 16-bit resolution, and evaluated the performance. The evaluation section discusses where each method succeeded, where it failed, and how the performance can be enhanced. Finally, we study the time complexity of each method to assess the feasibility of implementing a fast, and generalized method of pixel labeling.

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“…However, despite its importance, research into the field of pig breeding remains limited. Previous studies have focused on various aspects such as pig face detection [18,19], posture analysis [20], interactive touch-based techniques [21], and machine learning approaches for sow identification [22]. Many recent research efforts used changes in posture as an indicator of heat in sows [23].…”

Section: Introductionmentioning

confidence: 99%

Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring

Almadani,

Ramos,

Abuhussein

et al. 2024

Digital

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Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52).

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Sow Localization in Thermal Images Using Gabor Filters

Cited by 4 publications

References 6 publications

EGAN: An Ensemble Adversarial Network for Topology-Preserving EEG Data Generation for Predicting Cognitive Load Levels

EGAN: An Ensemble Adversarial Network for Topology-Preserving EEG Data Generation for Predicting Cognitive Load Levels

Review of Unsupervised Segmentation Techniques on Long Wave Infrared Images

Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring

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