Self-localization for an autonomous mobile robot based on an omni-directional vision system

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

With the rapid development of artificial intelligence and computer vision, feature point detection algorithms have emerged as a significant area of research, particularly in applications such as SLAM (Simultaneous Localization and Mapping) and image registration. However, the FAST (Features from Accelerated Segment Test) detection method employed in the ORB (Oriented FAST and Rotated BRIEF) algorithm exhibits limitations in detection accuracy. This paper proposes a feature point detection method based on sequential neural networks and knowledge distillation, aiming to enhance the performance of the FAST algorithm. Firstly, the sequential nature of the data points in the FAST algorithm is analyzed, leading to the development of a binary classification and regression model using a sequential neural network. Considering the significant computational overhead of the model, knowledge distillation techniques are introduced to optimize the efficiency of the neural network. Secondly, the constructed model undergoes performance testing and evaluation, with a detailed comparison against the traditional FAST method. Finally, this paper discusses the future prospects of feature point detection, suggesting potential directions for further optimization.

Detecting the changes within a single image is significant for applications like planogram compliance where objects of same type are arranged in a sequence. This article discusses on detection of such objects which differ in color, texture and shape from other objects in the given image. In this proposed algorithm, the given image is converted to HSV color space and it is partitioned into regions; Fast Discrete Curvelet Transform (FDCT) coefficients are extracted. In order to identify changed regions in the given image, salient points are detected using the Features from Accelerated Segment Test (FAST) corner detection algorithm. It has been observed that the value channel content of the HSV image is much significant in extracting useful features for change detection. Experimental results show an efficacy of 87% in detecting changes in a single row of a shelf image having same items arranged in a linear fashion.Keywords(FAST) corner detectionHSV color spaceChange detectionFDCTImage processing

Indonesia has a diversity of cultural arts, including art crafts. This craft art covers several fields including the arts of wood crafts, metal crafts, ceramic crafts, leather crafts, and textile crafts. In textile crafts, there are several types, such as weaving, knitting, embroidery, and batik. One area that has a characteristic of batik is Cirebon. Cirebon has a traditional batik center, namely Trusmi Batik. TrusmiBatik is a company that produces various kinds of batik, including mega mendungbatik motifs, paksinagaliman, patrankeris, patran kale, umbrella lion, barong lion, sunyaragian arum garden, banjarbalong, ayam alas, supiturang, nagaseba, sawat bride and groom cymbals, etc. The Trusmi batik company has problems on how to minimize artificial batik and how to distinguish original batik from artificial batik. One of technologiesthat can be used is Augmented Reality by using a QR Code (Quick Response) as a sign especially those which can find out whether the batik is original or imitation. The Feature From Accelerated Segment Test is a high-speed interest point detection algorithm based on pixel considerations in circular areas around interest points. Using the FAST algorithm can be used to detect markers so that they can distinguish original batik from artificial batik. Keywords: Trusmi Batik, QR Code, Augmented Reality, FAST.

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

Optical Coherence Tomography (OCT) is widely used for endoscopic imaging in endoluminal organs because of its high imaging accuracy and resolution. However, OCT endoscopic imaging suffers from Non-Uniform Rotational Distortion (NURD), which can be caused by many factors, such as irregular motor rotation and changes in friction between the probe and the sheath. Correcting this distortion is essential to obtaining high-quality Optical Coherence Tomography Angiography (OCTA) images. There are two main approaches for correcting NURD: hardware-based methods and algorithm-based methods. Hardware-based methods can be costly, challenging to implement, and may not eliminate NURD. Algorithm-based methods, such as image registration, can be effective for correcting NURD but can also be prone to the problem of NURD propagation. To address this issue, we process frames by coarse and fine registration, respectively. The new reference frame is generated by filtering out the A-scan that may have the NURD problem by coarse registration. And the fine registration uses this frame to achieve the final NURD correction. In addition, we have improved the Features from Accelerated Segment Test (FAST) algorithm and put it into coarse and fine registration process. Four evaluation functions were used for the experimental results, including signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). By comparing with Scale-invariant feature transform (SIFT), Speeded up robust features (SURF), Oriented FAST and Rotated BRIEF (ORB), intensity-based (Cross-correlation), and Optical Flow algorithms, our algorithm has a higher similarity between the corrected frames. Moreover, the noise in the OCTA data is better suppressed, and the vascular information is well preserved. Our image registration-based algorithm reduces the problem of NURD propagation between B-scan frames and improves the imaging quality of OCT endoscopic images.

The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.

For the extraction of paddy rice seedling centerline, this study proposed a method based on Fast-SCNN (Fast Segmentation Convolutional Neural Network) semantic segmentation network. By training the FAST-SCNN network, the optimal model was selected to separate the seedling from the picture. Feature points were extracted using the FAST (Features from Accelerated Segment Test) corner detection algorithm after the pre-processing of original images. All the outer contours of the segmentation results were extracted, and feature point classification was carried out based on the extracted outer contour. For each class of points, Hough transformation based on known points was used to fit the seedling row centerline. It has been verified by experiments that this algorithm has high robustness in each period within three weeks after transplanting. In a 1280×1024-pixel PNG format color image, the accuracy of this algorithm is 95.9% and the average time of each frame is 158ms, which meets the real-time requirement of visual navigation in paddy field.

Blindness and visual impairment are commonly associated with social and functional limitations, almost 45 million people in the world have blindness, and 135 million have any visual impairment. This condition has a significant impact on the quality of life and brings many challenges to the individual, one of which is the navigation and positioning tasks. Although there are already apps capable of helping visually impaired people (VIP) for mobility purposes, most of them focus on detecting obstacles and, therefore, on avoiding dangerous situations. However, mobility of VIP involves many more tasks, such as knowing their exact position and staying informed along an entire route. For this purpose, a standalone and customizable solution is proposed that uses traditional visual recognition of landmarks to process the surroundings of the current location of the visually impaired person using a smartphone and informing about the nearby places assuring the user a sense of the site. For feature detection, it used the oriented features from accelerated segment test (FAST) and rotated binary robust-independent elementary feature (BRIEF) (ORB) algorithm, and for feature matching, it used the brute-force method with the k-nearest neighbor (KNN) algorithm. Results show that the proposed solution can analyze pictures in fractions of a second with satisfactory accuracy.

The potential for the research of object tracking in computer vision has been well established, but previous object-tracking methods, which consider only continuous and smooth motion, are limited in handling abrupt motions. We introduce an efficient algorithm to tackle this limitation. A feature-driven (FD) motion model-based features from accelerated segment test (FAST) feature matching is proposed in the particle-filtering framework. Various evaluations have demonstrated that this motion model can improve existing methods’ performances to handle abrupt motion significantly. The proposed model can be applied to most existing particle-filter tracking methods.

Since the limitation on the onboard equipment, the sparse stereo vision is becoming a suitable choice for the deployment of micro air vehicles (MAV) and small robots. However, for the point feature‐based sparse stereo, most of the current stereo algorithms ignore the similarity between feature points, so it is hard to achieve high accuracy. In addition, the problem of clustered feature distribution will still affect the performance of point feature‐based algorithms in the application. To make up for these deficiencies, the authors propose an improved features from accelerated segment test (FAST) feature detector to suppress the point detection in complex texture regions. Most importantly, the authors present a novel census transform (CT)‐based algorithm that contains two encoders ‘texture orientation’ and ‘texture gradient’ to get a more efficient census bit string for the feature point. Instead of randomly selecting pixels to calculate the bit string, we combine the texture characteristics of the census windows where feature points are located. Compared with the original CT, the processing speed of our method is improved, and the average error of our method is reduced by 18.05%. The evaluation results show the presented improved point feature‐based sparse stereo algorithm has a great value in engineering applications.

Fast near-duplicate keyframe detection is the basis of similar video content and video topic analysis. Traditional solutions based on local features are time-consuming and unsuitable for real-time applications. Considering there are little changes on the angles between similar video keyframes, a fast near-duplicate keyframe detection method based on local features is proposed. Firstly, the feature points are detected in various scales of image pyramids by adopting FAST (Features from Accelerated Segment Test) detection method. Secondly, each feature point is described by BRIEF algorithm. Thirdly, the similar keyframes are recognized based on the pattern entropy. Experiments prove that the proposed method is accurate and efficient, and is suitable for real-time applications of similar video keyframes detection.

Blocked intersections have been a contributing factor in the city-wide traffic congestion, especially in metropolitan cities. This research study aims to develop a better traffic violations management system in city-road intersections by using a machine vision system that automatically identifies and tags traffic violations committed in an intersection. The proposed system have three main sub-systems which are the video capture, video analysis, and output sub-systems. This study presents the development and results of a vehicle detection and tracking system using corner feature point detection and artificial neural networks for the vision-based contactless traffic violations apprehension system. This detection and tracking system serves as the front-end processing in the video analysis sub-system. Experiments were conducted for different corner feature-points detection algorithm: Harris, Shi-Tomasi, and Features from Accelerated Segment Test (FAST). The results showed that in the testing phase Harris-ANN have 89.09% accuracy, Shi-Tomasi-ANN have 88.48%, and FAST-ANN have 90.30% accuracy.

If there are great illumination and contrast changes for multitemporal unmanned aerial vehicle (UA V) images, common image matching and registration methods may not work well on these images. To improve matching effects, this paper conduct the image registration using the structure consistency between the images to be matched, first, use phase congruency(PC) to describe images' structure characteristics and generate the PC images for both images to be matched; then, conduct the image matching by extracting the features from accelerated segment test (FAST) in the PC images; finally, use the method of randomized sample consensus (RANSAC) to eliminate the mismatches and obtain the affine transformation matrix between the two images. We use this method to pick out known control points on new UAV images on the basis of old image database of ground control points (GCP), the test proves that the method is independent of the radiation information and has a good effect on multitemporal UAV images registration.

It is general to use a fisheye camera for obtaining a full view image, but the images obtained by a fisheye camera are not rotation invariant, that is, the distortion of scenes on the image varies with their place of image. On the other hand, the content of a spherical image is invariant with the camera rotation and scenes do not vary. In this paper, we propose to detect corners on spherical images. We map a full view image to a discrete spherical image for obtaining a rotation invariant spherical image, and modify the conventional FAST (Features from Accelerated Segment Test) corner detector for directly applying it to the discrete spherical image.