Minimum Enclosing Rectangle Research Articles

Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning

In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree.

A fast automatic identification method for seismic belts based on Delaunay triangulation

Earthquake prediction practice and a large number of earthquake cases show that there may be abnormal images of small earthquake belts near the epicenter before strong earthquakes occur. For a static small earthquakes spatial distribution, due to the complexity of exhaustive algorithm, the fast automatic identification method of seismic belts has not yet been realized. Visual identification is still the main method of seismic belt discrimination. Based on the Delaunay triangulation, this paper presents a fast automatic identification method of seismic belts. The effectiveness of this method is proved by a 1000 random points test and an actual example of the 4-magnitude belts before the 2005 Jiujiang M5.7 Earthquake. The results show that: (1) Using Delaunay triangulation method, we can fast get the spatial relationship between two neighboring points; (2) using the two neighboring relationships, it can automatically extend to cluster, which carries the key information of seismic belt; (3) using the technology of minimum enclosing rectangle (MER) for the identified cluster, we can get the shape and structural information of the MER, which can be called the “suspect seismic belt”; (4) after using the other restrictions to sort and filter the suspect seismic belt, we complete the identification of seismic belt; (5) the random and actual earthquakes trial calculation shows that the Delaunay triangulation method can realize a fast automatic identification of seismic belts; and (6) this automatic identification method may provide a research basis for earthquake prediction.

Change to movement and morphology of the median nerve resulting from steroid injection in patients with mild carpal tunnel syndrome

There are conflicting hypotheses regarding the initial pathogenesis of carpal tunnel syndrome (CTS). One hypothesis characterizes it as inflammation of the median nerve caused by compression, while another hypothesis characterizes CTS as non-inflammatory fibrosis of the subsynovial connective tissue (SSCT). This study aimed to investigate the differences in the ultrasonography parameters before and after a steroid injection, which is effective for CTS, to elucidate the initial pathogenesis of CTS and the mechanisms of action of the injected steroid. Fourteen hands from 14 healthy participants and 24 hands from 24 participants with mild CTS were examined. Dynamic movement and morphology of the median nerve before and after steroid injection were measured. There was no significant difference in the normalized maximal distance of the median nerve, which reflects the degree of fibrosis in the SSCT indirectly, during finger and wrist movements before and after the injection among patients with CTS (p > 0.05). Among the parameters that indirectly reflects the degree of median nerve compression, such as normalized maximal change in the aspect ratio of the minimum-enclosing rectangle (MER), maximal change in the median nerve perimeter, and maximal value of the median nerve cross-sectional area (CSA), statistically significant differences were not observed between values of the normalized maximal change in the aspect ratio of the MER and maximal change in the median nerve perimeter, during finger and wrist movements recorded before and after the injection in patients with CTS (p > 0.05). However, multivariate logistic regression analysis revealed that the change in the normalized maximal value of the median nerve CSA, according to finger and wrist movement was correlated with the administration of the steroid injection (p < 0.05). In conclusion, compared to that noted before steroid injection, the median nerve CSA noted during finger and wrist movements changed significantly after injection in patients with mild CTS. Given the improvement in median nerve swelling after steroid injection, but no improvement in the movement of the median nerve during finger and wrist movements, median nerve swelling due to compression (rather than fibrosis of the SSCT may be the initial pathogenesis of early-stage (mild) CTS, and the fibrous changes around the median nerves (SSCT) may be indicative of secondary pathology after median nerve compression. Further studies are required to validate the findings of our study and confirm the pathogenesis of CTS.

Infrared image-based identification method for the gradation of rock grains using heating characteristics

In earth-rock dam engineering, the gradation characteristics of filling materials have an important influence on the compactability, impermeability, and stress–strain characteristics of dams. A sound gradation is the key to guaranteeing dam safety and stability. Based on the principles of the heat conduction and heating characteristic tests on rocks, the power function equation between volume and temperature increase for pebble and gravel grains in a heating environment is established with correlation coefficients of 0.98. Aiming at the low span of gray-scale change of digital infrared images, the linear gray-scale transformation method with a Gamma factor of 1 to enhancing the digital infrared images, and the maximum between-cluster variance method (OSTU method) is adopted to perform binary grain target-context segmentation on enhanced images. With regard to the phenomenon of grain adhesion, it subjects flattened images after binary segmentation to peak processing using Dis operation, and introduces an improved watershed algorithm (i.e., local minimum with threshold) for image reconstruction, effectively segmenting adhesive grains while avoiding over-segmentation. According to maximum stability theory, the shortest side of the minimum enclosing rectangle (MER) of the grains in images is proposed as the judgment index for grain sieving. Five gradation detections were carried out on pebbles and gravels, respectively. For grain mass fraction identification results for various grain size intervals, the maximum absolute errors for pebbles and gravels do not exceed 6% and 7%, and the average absolute errors do not exceed 4% and 5%, respectively. Thus, the identified gradation curve fits well with the mechanical sieving gradation curve, and can meet engineering application requirements. This new method overcomes several deficiencies of traditional methods for gradation image detection, such as the susceptibility of image sampling to various interfering factors and difficulties in controlling errors in calculating grain mass, and therefore, provides a novel conceptual technology for particle grading image recognition.

Ultrasonography of the Transverse Movement and Deformation of the Median Nerve and Its Relationships With Electrophysiological Severity in the Early Stages of Carpal Tunnel Syndrome

BackgroundTo date, there have been conflicting suggestions regarding the initial pathogenesis of carpal tunnel syndrome (CTS). It has been characterized as either inflammation of the median nerve caused by compression or noninflammatory fibrosis of the subsynovial connective tissue (SSCT). ObjectiveTo investigate the initial pathogenesis of CTS, we compared the deformation and displacement of the median nerve in accordance with the electrophysiological severity between patients with CTS (via the Bland scale) and healthy controls. DesignCross-sectional, case-control study. SettingGeneral teaching hospital, rehabilitation unit. PatientsThirteen hands of 10 healthy participants and 39 hands of 27 patients with CTS were recruited and classified into 4 groups (stage 0−3) according to the severity of the electrodiagnostic test results. MethodsUltrasound images of the median nerve in response to the wrist and finger motions were analyzed. Main Outcome MeasureWe measured the deformation of the median nerve (the maximal change values of the median nerve area, and aspect ratio of the minimum-enclosing rectangle [MER]) and movement of the median nerve (the maximal change value of the median nerve displacement) in response to the motions of wrists and finger. The maximal change value of the median nerve displacement was normalized with respect to the width of the wrist. Moreover, the maximal change values of the median nerve area, and the aspect ratio of the MER, were also normalized to the median nerve area and the aspect ratio of the MER in wrist-neutral position with finger extension. ResultCTS patients in stage 3 showed a significantly lower normalized maximal change of the median nerve movement compared with CTS patients in stages 0, 1, and 2 (P <.001). Regarding the deformation of the median nerve, however, there were statistically significant differences among all groups (area, P < .001; MER, P <.001). According to multivariate logistic regression analysis, the normalized maximal change of the aspect ratio of the MER is a meaningful parameter in diagnosing the initial stage of CTS (P < .01). ConclusionThese results showed that the deformation of the median nerve may be associated with the initial pathogenesis; however, further studies are necessary to validate the findings of this study. Level of EvidenceIII

Head rice rate measurement based on concave point matching

Head rice rate is an important factor affecting rice quality. In this study, an inflection point detection-based technology was applied to measure the head rice rate by combining a vibrator and a conveyor belt for bulk grain image acquisition. The edge center mode proportion method (ECMP) was applied for concave points matching in which concave matching and separation was performed with collaborative constraint conditions followed by rice length calculation with a minimum enclosing rectangle (MER) to identify the head rice. Finally, the head rice rate was calculated using the sum area of head rice to the overall coverage of rice. Results showed that bulk grain image acquisition can be realized with test equipment, and the accuracy rate of separation of both indica rice and japonica rice exceeded 95%. An increase in the number of rice did not significantly affect ECMP and MER. High accuracy can be ensured with MER to calculate head rice rate by narrowing down its relative error between real values less than 3%. The test results show that the method is reliable as a reference for head rice rate calculation studies.

Image segmentation incorporating double-mask via graph cuts

This paper described a novel strategy to apply double-masking in image segmentation based on graph cuts. We provided a reasonable method for imposing seed information automatically at object regions where image labels are difficult to determine during complex underwater scene segmentation. Cr component pre-segmentation based on Mahalanobis distance played a role in YCrCb space. The pre-segmentation region was used as object mask for the graph model of the original image in Cr pre-segment binary image. The minimum-enclosing rectangle of the object mask could reduce the calculative area in graph model and the bounding box-provided graph model of the original image with the background mask. Our approach was easily realized and did not require specialized hardware, prior knowledge of underwater conditions, or scene structure. Experimental results demonstrated the robustness and accuracy of the performance of our proposed method.

Blob analyzation-based template matching algorithm for LED chip localization

During the testing and sorting of LED chips, traditional methods do not exclude the polycrystalline and fragmentary LED chips from the normal chips well. The purpose of this paper is to propose a new algorithm to solve this problem. The algorithm consists of three steps. Firstly, present a simple but efficient image segmentation method to get blobs. Secondly, analyze the blobs to exclude abnormal blobs and predict the pose (position and orientation) of the potential object based on the pose of the minimum enclosing rectangle (MER) of each remained blob. Finally, according to the predicted poses, locate the LED chips precisely in the originally captured image based on gradient orientation features. Experiments show that the algorithm is not only robust to illumination variation but also can locate the LED chips and exclude the polycrystalline and fragmentary chips efficiently.

Moving object classification using local shape and HOG features in wavelet-transformed space with hierarchical SVM classifiers

This paper proposes an integrated system for the segmentation and classification of four moving objects, including pedestrians, cars, motorcycles, and bicycles, from their side-views in a video sequence. Based on the use of an adaptive background in the red–green–blue (RGB) color model, each moving object is segmented with its minimum enclosing rectangle (MER) window by using a histogram-based projection approach or a tracking-based approach. Additionally, a shadow removal technique is applied to the segmented objects to improve the classification performance. For the MER windows with different sizes, a window scaling operation followed by an adaptive block-shifting operation is applied to obtain a fixed feature dimension. A weight mask, which is constructed according to the frequency of occurrence of an object in each position within a square window, is proposed to enhance the distinguishing pixels in the rescaled MER window. To extract classification features, a two-level Haar wavelet transform is applied to the rescaled MER window. The local shape features and the modified histogram of oriented gradients (HOG) are extracted from the level-two and level-one sub-bands, respectively, of the wavelet-transformed space. A hierarchical linear support vector machine classification configuration is proposed to classify the four classes of objects. Six video sequences are used to test the classification performance of the proposed method. The computer processing times of the object segmentation, object tracking, and feature extraction and classification approaches are 79ms, 211ms, and 0.01ms, respectively. Comparisons with different well-known classification approaches verify the superiority of the proposed classification method.

이웃 에지 탐색에 의한 개선된 객체 윤곽선 추출 알고리즘과 MER을 이용한 모의훈련에서의 폐색처리

영상처리 기술을 이용한 모의훈련에서 사용자는 영상에 전시된 가상객체를 통해 실세계와의 상호작용과 인식능력을 향상시킬 수 있다. 따라서 현실감 있는 모의훈련을 위해서는 가상객체와 실영상을 정합한 후 가상객체로 인해 생기는 폐색영역을 결정하는 것이 필수적이다. 본 논문에서는 실 영상위에서 지정된 경로에 따라 가상표적을 이동시킬 때 발생하는 폐색문제를 이웃에지 탐색을 이용한 개선된 윤곽선 추출 알고리즘과 MER(Minimum Enclosing Rectangle)을 이용하여 해결한다. 제안된 윤곽선 추출 알고리즘에 의해 복잡한 물체에 대한 세부적인 윤곽을 얻은 후 성능향상을 위해 객체의 MER을 이용하여 폐색이 일어나는 지점의 3차원 정보를 산출하였다. 실험에서는 부분적 폐색이 발생하는 환경에서 제안한 방법을 기존방법과 비교하고 유효성을 입증하였다. Trainee can enhance his perception of and interaction with the real world by displayed virtual objects in simulation using image processing technology. Therefore, it is essential for realistic simulation to determine the occlusion areas of the virtual object produces after registering real image and virtual object exactly. In this paper, we proposed the new method to solve occlusions which happens during virtual target moves according to the simulated route on real image using improved object contour extraction by neighboring edge search and picking algorithm. After we acquire the detailed contour of complex objects by proposed contour extraction algorithm, we extract the three dimensional information of the position happening occlusion by using MER for performance improvement. In the experiment, we compared proposed method with existed method and preyed the effectiveness in the environment which a partial occlusions happens.