Speeded Up Robust Features Research Articles

Fish species classification using a collaborative technique of firefly algorithm and neural network

Monitoring various Fish Species and its distribution of the species obtains a primary significance in receiving the insights to marine ecological-system. After this, visual classification of those species would aid in tracing out the movement and yield the patterns and trends in fish activities, which provides in depth knowledge of the species. Unconstrained under-water images pose highly variations because of the fish orientation changes, Light-intensities, similarity in fish patterns and fish shapes. This would create the greater challenge for Image-processing techniques in accurate classification of Fish species or the Fish classes. Hence, for this reason, Underwater Image Enhancement is implemented in combination of Morphological-operations in pre-processing method. The pre-processed image is then subjected to feature extraction process by using Speed-up Robust Feature algorithm. This is followed by Firefly Algorithm, applied for optimization of Region of interest selection in the selected-features. For the categorization of Fish-species, PatternNet is a technique which is employed, in classifying 10,000 marine fish-images to five categories (Dascyllus reticulatus, Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus). The Efficiency of the proposed-framework is performed in terms of Classification accuracy, execution time, precision value, F-measure and recall factors with respect to various categories of fish species. The comparison of the proposed-framework is also assessed with the other existing methods. 98% of accuracy rate in classification was produced by the evaluation results of the proposed framework with a lesser average computation time of 3.64 s upon different tested images. Thus, the higher efficiency of the proposed framework is proved by the outcomes of the study.

DMCNet: Diversified model combination network for understanding engagement from video screengrabs

Engagement is an essential indicator of the Quality-of-Learning Experience (QoLE) and plays a major role in developing intelligent educational interfaces. The number of people learning through Massively Open Online Courses (MOOCs) and other online resources has been increasing rapidly because they provide us with the flexibility to learn from anywhere at any time. This provides a good learning experience for the students. However, such learning interface requires the ability to recognize the level of engagement of the students for a holistic learning experience. This is useful for both students and educators alike. However, understanding engagement is a challenging task, because of its subjectivity and ability to collect data. In this paper, we propose a variety of models that have been trained on an open-source dataset of video screengrabs. Our non-deep learning models are based on the combination of popular algorithms such as Histogram of Oriented Gradient (HOG), Support Vector Machine (SVM), Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). The deep learning methods include Densely Connected Convolutional Networks (DenseNet-121), Residual Network (ResNet-18) and MobileNetV1. We show the performance of each models using a variety of metrics such as the Gini Index, Adjusted F-Measure (AGF), and Area Under receiver operating characteristic Curve (AUC). We use various dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to understand the distribution of data in the feature sub-space. Our work will thereby assist the educators and students in obtaining a fruitful and efficient online learning experience.

Performance Evaluation of Feature Extraction Algorithms for Vehicle Shape Classification

Vehicle classification is a classic application of automotive image processing that is necessary for a variety of modern vehicle safety and comfort features. While machine learning-based solutions are effective in these fields and are currently employed extensively in various automotive applications. However, the most challenging aspect of automotive image processing with machine learning methods is gathering adequate quality and quantity datasets to develop such applications. Additionally, imbalanced datasets are common in multiclass automotive image processing, as is the case with the current topic of vehicle platoon management. The effectiveness of available handcrafted feature extractors and classifiers employed for vehicle class categorization varies greatly due to the effect of dataset imbalance. This study aims to examine how the performance of four prominent feature extractors alters when used with the imbalance dataset for vehicle shape classification. Also, the use of image augmentation techniques to increase the dataset size for three vehicle classes: car, bus, and truck, has been presented. Further, using the Support Vector Machine (SVM) classifier, experimental analysis was performed using feature extractors such as Histogram of Oriented Gradient (HOG), Scaled Invariant Feature Transform (SIFT), Speeded-Up Robust Feature (SURF), and Haar. Vehicle shape classification, which is an important characteristic in vehicle platoon management, has been evaluated using Receiver Operating Characteristic (ROC) for both the unbalanced dataset and the augmented dataset. The experimental results demonstrate that using the HOG feature extractor performs better when compared to SIFT, SURF, and HAAR feature extractors on the imbalance dataset. After using an image augmentation technique to add images, output performance improved significantly, with HOG output of 95%, SIFT output of 91%, SURF output of 91%, and HAAR output of 96%.

Displacement Measurement Based on UAV Images Using SURF-Enhanced Camera Calibration Algorithm

Displacement is an important parameter in the assessment of the integrity of infrastructure; thus, its measurement is required in a multitude of guidelines or codes for structural health monitoring in most countries. To develop a low-cost and remote displacement measurement technique, a novel method based on an unmanned aerial vehicle (UAV) and digital image correlation (DIC) is presented in this study. First, an auxiliary reference image that meets the requirements is fabricated using the selected first image. Then, the speeded-up robust features (SURF) algorithm is introduced to track the feature points in the fixed areas. The least square algorithm is then employed to resolve the homography matrix of the auxiliary reference image and target images; then, the acquired homography matrices are utilized to calibrate the deviation caused by the UAV wobble. Finally, the integral pixel and sub-pixel matching of the DIC algorithm is employed to calculate the displacement of the target object. The numerical simulation results show that the proposed method has higher calculation accuracy and stability. The outdoor experiment results show that the proposed method has definite practicability.

Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography

The mismatch of geometric parameters in a nanotomography system bears a significant impact on the reconstructed images. Moreover, projection image noise is increased due to limitations of the X-ray power source. The accuracy of the existing self-calibration method, which uses only the grayscale information of the projected image, is easily affected by noise and leads to reduced accuracy. This paper proposes a geometric parameter self-calibration method based on feature matching of mirror projection images. Firstly, the fast extraction and matching feature points in the mirror projection image are performed by speeded-up robust features (SURF). The feature triangle is then designed according to the stable position of the system’s rotation axis to further filter the feature points. In turn, the influence of the mismatched points on the calculation accuracy is reduced. Finally, the straight line where the rotation axis is located is fitted by the midpoint coordinates of the filtered feature points, thereby realizing geometric parameter calibration of the system. Simulation and actual data from the experimental results show that the proposed method effectively realizes the calibration of geometric parameters, and the blurring and ghosting caused by geometric artifacts are corrected. Compared with existing methods, the image clarity can be improved by up to 14.4%.

In-Motion Railroad Tie Deflection Measurement via Ultrasonic Airborne Sonar and Computer Vision Techniques

ABSTRACT It is known in the railroad maintenance engineering community that the deflection of railroad ties is an indicator of the quality of the tie–ballast interface, whose deterioration may cause dangerous train derailments. A new technology is proposed to reconstruct the full-field deflection profile of railroad ties in-motion by means of non-contact ultrasonic testing and computer vision techniques. The sensing layout consists of an array of air-coupled capacitive transducers (operated in pulse-echo sonar-based ranging mode) and a high frame-rate camera, rigidly connected to the main frame of a moving train car. The acquisition system is programmed such that the synchronized waveforms and images are collected and saved as train car moves. In the processing stage, a supervised machine learning-based image classification approach is developed to demarcate the tie boundaries. For this purpose, the Speeded-Up Robust Features (SURF) and Bag of Visual Words (BOVW) algorithms are employed to encode images into condensed feature vectors, which are subsequently fed into the Support Vector Machine (SVM) to train a classifier. The relative deflections of the identified ties are eventually computed by tracking the time-of-flight of the reflected waves from the surfaces flagged as tie. An image processing technique is also developed to estimate the spatial resolution of the tracking system, required to reconstruct the full-field deflection profile of the scanned ties. The importance of such a technique is stressed if the test run is performed without any dedicated positioning system. The proposed ‘tie sonar’ system was prototyped and used to reconstruct the deflection profile of the ties scanned during a series of test runs conducted at slow (walking) speed at the Rail Defect Testing Facility (RDTF) of UC San Diego as well as a BNSF yard in San Diego, CA, with a realistic train load. Further developments of this system should include a performance evaluation at higher speeds (e.g., revenue speed).

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

AbstractMagnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer‐assisted diagnosis tool. The fused Bag‐of‐Features (BoF) with Speeded‐Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1‐weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.

Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm.

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technologies. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change.

A steganography embedding method based on CDF-DWT technique for data hiding application using Elgamal algorithm

Steganography is a data hiding method used for security purposes. While hiding more data in the embedding process, the data may be lost and cause some security problems. The steganography embedding method is used to avoid this problem. In this manuscript, the steganography embedding method is proposed based on the Cohen-Daubechies-Feauveau Discrete Wavelet Transform (CDF-DWT) technique for data hiding applications using the Elgamal algorithm. The cover image is considered to hide the secret data and then the cover image edges are detected and filtered by using the Speeded-Up Robust Features (SURF) method. Elgamal algorithm is used to encrypt the input data. This method aims to safeguard the secret data using the steganography method with increase on embedding efficiency, embedding capacity, carrier capacity and reduce execution time. The proposed CDF-DWT technique with Elgamal algorithm shows better results with Peak to signal noise ratio (PSNR) of 45.78%, Mean Square Error (MSE) of 2.09%, and Bit Error Rate (BER) of4.56%, the execution time of 5.98%, Carrier Capacity of 85.90% and Embedding Capacity of 80.67%. The obtained values are compared with the existing state-of-the-art method in data hiding.

The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

Aim: Advancements in multimedia technology have facilitated the uploading and processing of videos with substantial content. Automated tools and techniques help to manage vast volumes of video content. Video shot segmentation is the basic symmetry step underlying video processing techniques such as video indexing, content-based video retrieval, video summarization, and intelligent surveillance. Video shot boundary detection segments a video into temporal segments called shots and identifies the video frame in which a shot change occurs. The two types of shot transitions are cut and gradual. Illumination changes, camera motion, and fast-moving objects in videos reduce the detection accuracy of cut and gradual transitions. Materials and Methods: In this paper, a novel symmetry shot boundary detection system is proposed to maximize detection accuracy by analysing the transition behaviour of a video, segmenting it initially into primary segments and candidate segments by using the colour feature and the local adaptive threshold of each segment. Thereafter, the cut and gradual transitions are fine-tuned from the candidate segment using Speeded-Up Robust Features (SURF) extracted from the boundary frames to reduce the algorithmic complexity. The proposed symmetry method is evaluated using the TRECVID 2001 video dataset, and the results show an increase in detection accuracy. Result: The F1 score obtained for the detection of cut and gradual transitions is 98.7% and 90.8%, respectively. Conclusions: The proposed symmetry method surpasses recent state-of-the-art SBD methods, demonstrating increased accuracy for both cut and gradual transitions in videos.

A study of multi-target image-based displacement measurement approach for field testing of bridges

ABSTRACT As the demand for field testing of bridges grows, so does the need to optimize field testing procedures to allow for a simplified testing strategy that can be employed more effectively. With the advent of computer vision, there has been limited research exploring Feature-Based Image Registration (FBIR) methods for structural testing of bridges. In this paper, the potential of a simple FBIR approach to accurately capture submillimeter displacements using consumer-grade cameras will be demonstrated through a field test on a reinforced concrete slab bridge and on a full-scale bridge deck specimen in the laboratory. The internal and external parameters that influence the results of this measurement strategy were investigated by using various camera positions during the laboratory tests and applying different threshold parameters to the Speeded-Up Robust Features algorithm used for the feature detection and matching. The FBIR method demonstrates great potential, producing an average measurement accuracy within 1.6% of conventional displacement sensors during the field test and 3.3% during the laboratory tests. Altogether, the advantages to this image-based measurement approach enhance the load testing strategy to be implemented by bridge owners at much lower costs and with minimal complication and field setup.

Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF.

In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.

Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images.

Prostate cancer can be diagnosed by prostate biopsy using transectal ultrasound guidance. The high number of pathology images from biopsy tissues is a burden on pathologists, and analysis is subjective and susceptible to inter-rater variability. The use of machine learning techniques could make prostate histopathology diagnostics more precise, consistent, and efficient overall. This paper presents a new classification fusion network model that was created by fusing eight advanced image features: seven hand-crafted features and one deep-learning feature. These features are the scale-invariant feature transform (SIFT), speeded up robust feature (SURF), oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) of local features, shape and texture features of the cell nuclei, the histogram of oriented gradients (HOG) feature of the cavities, a color feature, and a convolution deep-learning feature. Matching, integrated, and fusion networks are the three essential components of the proposed deep-learning network. The integrated network consists of both a backbone and an additional network. When classifying 1100 prostate pathology images using this fusion network with different backbones (ResNet-18/50, VGG-11/16, and DenseNet-121/201), we discovered that the proposed model with the ResNet-18 backbone achieved the best performance in terms of the accuracy (95.54%), specificity (93.64%), and sensitivity (97.27%) as well as the area under the receiver operating characteristic curve (98.34%). However, each of the assessment criteria for these separate features had a value lower than 90%, which demonstrates that the suggested model combines differently derived characteristics in an effective manner. Moreover, a Grad-CAM++ heatmap was used to observe the differences between the proposed model and ResNet-18 in terms of the regions of interest. This map showed that the proposed model was better at focusing on cancerous cells than ResNet-18. Hence, the proposed classification fusion network, which combines hand-crafted features and a deep-learning feature, is useful for computer-aided diagnoses based on pathology images of prostate cancer. Because of the similarities in the feature engineering and deep learning for different types of pathology images, the proposed method could be used for other pathology images, such as those of breast, thyroid cancer.

Stationary wavelet transformation based video stabilization and stitching

Video stitching has become popular due to recent advancements in technology to provide broad views and high-resolution displays. Comprehensive view or panoramic videos and high-resolution displays are created by stitching videos captured by multiple cameras or by a single camera at different points of time. This paper proposes a video stitching technique with stabilization for moving multi-camera videos adopting the wavelet decomposition technique. This method uses only those feature points that reduce the mismatching and increase the precision in estimating the transformation from among the feature points identified by the Speed-Up Robust Features detector. This work differs from the similar work of others in two directions. Instead of using all selected feature points for the matching purpose, only significant among them are used. Unlike others, the frames are stabilized before they are stitched.

Motion Video Recognition in Speeded-Up Robust Features Tracking

Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets.

Early detection of diabetic foot ulcers from thermal images using the bag of features technique

The most common diabetes complication, diabetic foot ulcer (DFU), can result in amputation if not managed effectively. Early diagnosis is thus critical, yet DFU symptoms cannot be reliably identified in the initial stages of the disease. This work develops an accurate, fast, reliable end-to-end traditional machine learning (ML) model that can be implemented in real-time computer-aided diagnosis (CAD) applications for DFU diagnosis. We selected the plantar thermogram database because it may help detect an increase in plantar temperature and thereby permit early diagnosis. This method is less affected by environmental conditions, such as smoke and weather. To extract features invariant to translation, rotation, and scaling transformation, we use the Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods combined with the Bag Of Features (BOF) technique. We used different pretrained deep learning (DL) networks to compare ML models trained with DL and handcrafted features. This research is novel since no previous studies have used traditional ML algorithms for the diagnosis of DFU from thermal images, nor have they used SIFT, SURF, or BOFtechniques. In addition, we use direct temperature files of each foot, later mapping them onto images to obtain accurate temperature distributions. Conventional ML classifiers are preferred in the final stage for binary classification between normal and DFU. The proposed model achieves 97.81% classification accuracy using a support vector machine (SVM) classifier trained with the SURF-BOF technique, along with 97.81% sensitivity, 97.9% precision, and a 0.9995 Area under the ROC curve (AUC) for DFU classification using thermal images.

Investigation of near-field jet stability of a single-hole injector based on fast X-ray phase contrast imaging and image feature matching

Fuel spray is very effective in controlling the combustion process to improve engine performance and reduce emissions. Understanding the spray unstability during the injection process is of importance to improve the control of spray characteristics and engine operation. In this study, near-field biodiesel jets were recorded using fast X-ray phase contrast imaging and the flow features inside the jet were extracted using Speeded Up Robust Features (SURF) method. The image similarity by feature matching was successfully used to represent the near-field jet stability. Based on the jet stability, an injection process can be divided into five stages: an unstable stage at needle opening, a partially stable transition stage at needle opening, a stable stage, a stable transition stage at needle closing and an unstable stage at needle closing. The ranges of needle lift for five stages were also determined. The jet unstability is highly related to the cavitation formation and gas purging process during needle opening. The variation of stable jet feature is dependent on the needle lift at needle closing. Higher needle lift for the similar jet feature at needle opening indicates a hydraulic delay compared to needle closing. Finally, the possible reasons of jet feature formation and feature detection used on the multi-hole injector are discussed.

Vision-Based Autonomous Landing on Unprepared Field With Rugged Surface

Landing on unprepared terrains is a challenging task for fully autonomous micro unmanned aerial vehicles (UAVs). Most of the existing methods mainly rely on manual control when the terrain of the target area is unknown in advance. In this article, we propose an autonomous landing method based on a learning-based multi-view stereo (MVS) system. UAV acquires multiple RGB pictures of the terrain after cruise, and then extracts speed-up robust features (SURF) and perform structure from motion (SFM) to obtain sparse feature point clouds. By utilizing the generated initial depth map, we further propose a novel 3-D reconstruction algorithm named PatchmatchNet-A, which can help to obtain precise and stable estimates of the depth information. In PatchmatchNet-A, we also use a new activation function, adjustable-arctangent linear units (ALU), to improve the accuracy and robustness. We have tested our algorithms in a DTU dataset and found that it can speed up the processing by around 25% while guaranteeing competitive performance. Flight experiments have also been conducted to verify the effectiveness of the whole landing system by using a commercial M210V2 quadcopter.

Improved speeded-up robust features registration method based on particle swarm and gray wolf algorithm for coal mine images

Image registration is an electronic imaging technology that matches two or more images acquired at different times or under different conditions. Here, we highlight the application of image registration in monitoring robots (e.g., autonomous vehicles) of coal mines. In image registration processing, the scale-invariant feature transform algorithm and its improved versions achieve poor extraction effects for feature points on smooth-edge curves. In comparison, the speeded-up robust features (SURF) algorithm achieves more stable registration results in various images. To improve the accuracy of the SURF algorithm, it is optimized using the particle swarm optimization (PSO) algorithm and several improved PSOs, but it achieves unstable accuracy. Therefore, we optimize the improved gray wolf algorithm (IGWO) using advanced mathematical models in several computing steps. We add the memory function of both experienced individual and group optimal locations in PSO to update the particle positions. The evaluations performed on different datasets show that the accuracy and range of registration are substantially improved by IGWO and IGWO-PSO; however, the running time is extended. Thus, our improved method is competitive with other state-of-the-art image registration methods.

Evaluation of Several Computer Vision Feature Detectors/Extractors on Ahuna Mons Region in Ceres and Its Implications for Technosignatures Search.

Ahuna Mons is a 4 km particular geologic feature on the surface of Ceres, of possibly cryovolcanic origin. The special characteristics of Ahuna Mons are also interesting in regard of its surrounding area, especially for the big crater beside it. This crater possesses similarities with Ahuna Mons including diameter, age, morphology, etc. Under the cognitive psychology perspective and using current computer vision models, we analyzed these two features on Ceres for comparison and pattern-recognition similarities. Speeded up robust features (SURF), oriented features from accelerated segment test (FAST), rotated binary robust independent elementary features (BRIEF), Canny edge detector, and scale invariant feature transform (SIFT) algorithms were employed as feature-detection algorithms, avoiding human cognitive bias. The 3D analysis of images of both features’ (Ahuna Mons and Crater B) characteristics is discussed. Results showed positive results for these algorithms about the similarities of both features. Canny edge resulted as the most efficient algorithm. The 3D objects of Ahuna Mons and Crater B showed good-fitting results. Discussion is provided about the results of this computer-vision-techniques experiment for Ahuna Mons. Results showed the potential for the computer vision models in combination with 3D imaging to be free of bias and to detect potential geoengineered formations in the future. This study also brings forward the potential problem of both human and cognitive bias in artificial-intelligence-based models and the risks for the task of searching for technosignatures.