Eye Detection using Helmholtz Principle

            كشف العين استخدم في تطبيقات متعددة مثل تمييز الأنماط , البصمة, وأنظمة المراقبة والعديد من الأنظمة الأخرى. في هذه المقالة ,تم تقديم  طريقة جديدة لتحديد العين واستخلاص الشكل الخارجي لعين واحدة من الصورة بالأعتماد على مبدئين هما Helmholtz و Gestalt. وفقا لميدأ الأدراك ل Helmholtz  أنه أي شكل هندسي ملاحظ يكون ذو معنى أدراكيا أذا كان عدد مرات تكراره ضئيل جدا في صورة ذات توزيع عشوائي. لتحقيق هذا الهدف مبدأ Gestalt الذى ينص على أن الأنسان يلاحظ الأشياء أما عن طريق تجميع عناصره المتماثلة أو تمييز الأنماط . بصورة عامة وفقا  لمبدأ Gestalt ان الانسان يدرك الأشياء من خلال الوصف العام لهذه الاشياء . في هذه المقالة تم الأستفادة من هذين المبدئين لتمييز وأستخلاص جزء العين من الصورة. اللغة البرمجية جافا مع مكتبة opencv المتخصصة في معالجة الصور تم استخدامهما معا لهذا الغرض. نتائج جيدة تم الحصول عليها من هذه الطريقة المقترحة , حيث تم الحصول على 88.89% كنسبة الدقة أما بالنسبة لمعدل وقت التنفيذ يبلغ 0.23 من الثواني.            Eye Detection is used in many applications like pattern recognition, biometric, surveillance system and many other systems. In this paper, a new method is presented to detect and extract the overall shape of one eye from image depending on two principles Helmholtz & Gestalt. According to the principle of perception by Helmholz, any observed geometric shape is perceptually "meaningful" if its repetition number is very small in image with random distribution. To achieve this goal, Gestalt Principle states that humans see things either through grouping its similar elements or recognize patterns. In general, according to Gestalt Principle, humans see things through general description of these things. This paper utilizes these two principles to recognize and extract eye part from image. Java programming language and OpenCV library for image processing are used for this purpose. Good results are obtained from this proposed method, where 88.89% was obtained as a detection rate taking into account that the average execution time is about 0.23 in seconds

An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

With the development of radar system, the problem of enormous raw data has drawn much attention. A plot fusion method based on contour tracking algorithm is proposed to detect extended targets in a radar image. Firstly, the characteristic of radar image in complex environment is revealed. Then, the steps of traditional method, region growing method, and the proposed method are introduced. Meanwhile, the algorithm of tracking the contour of an extended target is illustrated in detail. It is not necessary to scan all the plots in the image, because the size of target is considered in the proposed method. Therefore, the proposed method is much more efficient than several existing methods. Lastly, the performance of several methods is tested using the raw data of two scenarios in real world. The experiment results show that the proposed method is practical and most likely to satisfy the real-time requirement in various complex environment

Maritime Object Detection, Tracking, and Classification Using Lidar and Vision-Based Sensor Fusion

Autonomous Surface Vehicles have the capability of replacing dull, dirty, and dangerous jobs in the maritime field. However, few successful ASV systems exist today, as there is a need for greater sensing capabilities. Furthermore, a successful ASV system requires object detection and recognition capabilities to enable autonomous navigation and situational awareness. This thesis demonstrates an application of LiDAR sensors in maritime environments for object detection, classification, and camera sensor fusion. This is accomplished through the integration of a high-fidelity GPS/INS system, 3D LiDAR sensors, and a pair of cameras. After rotating LiDAR returns into a global reference frame, they are reduced to a 3D occupancy grid. Objects are then extracted and classified with a Support Vector Machine (SVM) classifier. The LiDAR returns, when converted from a global frame to a camera frame, then allow the cameras to process a region of their imaging frame to assist in the classification of objects using color-based features. The SVM implementation results in an overall accuracy 98.7% for 6 classes. The transformation into pixel coordinates is shown here to be successful, with an angular error of 2 degrees, attributed to measurement error propagated through rotations

Fast Contour-Tracing Algorithm Based on a Pixel-Following Method for Image Sensors

Contour pixels distinguish objects from the background. Tracing and extracting contour pixels are widely used for smart/wearable image sensor devices, because these are simple and useful for detecting objects. In this paper, we present a novel contour-tracing algorithm for fast and accurate contour following. The proposed algorithm classifies the type of contour pixel, based on its local pattern. Then, it traces the next contour using the previous pixel’s type. Therefore, it can classify the type of contour pixels as a straight line, inner corner, outer corner and inner-outer corner, and it can extract pixels of a specific contour type. Moreover, it can trace contour pixels rapidly because it can determine the local minimal path using the contour case. In addition, the proposed algorithm is capable of the compressing data of contour pixels using the representative points and inner-outer corner points, and it can accurately restore the contour image from the data. To compare the performance of the proposed algorithm to that of conventional techniques, we measure their processing time and accuracy. In the experimental results, the proposed algorithm shows better performance compared to the others. Furthermore, it can provide the compressed data of contour pixels and restore them accurately, including the inner-outer corner, which cannot be restored using conventional algorithms

Understanding Learning Progressions via Automatic Scoring of Visual Models

The modern reliance on technological advances has spurred a focus on improving scientific education. Fueled by this interest, novel methods of testing students’ understanding of scientific concepts have been developed. One of these is visual modeling, an assessment method which allows for non-textual evaluation that incorporates previously difficult factors to test,such as complexity and creativity. Although visual models have been shown to effectively measure conceptual understanding, there has been a logistical barrier of scaling due to the infeasibility of grading large amounts of them by hand. This thesis proposes a system that can solve this issue by automatically grading visual models. A host of unsupervised and supervised computer vision techniques are utilized in order to classify shapes in visual models, extract relevant features, and, ultimately, assign a Learning Progression score to each model. Examples of the techniques used are a novel way to determine the orientation of Arrows and a Cascaded Voting System for shape classification. The results of the automatic grading system proposed in this thesis outperform previous methods and lay the foundation for future improvements. The resulting findings show great promise for directly solving the scaling issue, thereby making visual model assessments a practical tool for widespread use.Master of ScienceData Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154796/1/Ari Sagherian Final Thesis.pdfDescription of Ari Sagherian Final Thesis.pdf : Thesi

Vision-Based Automated Hole Assembly System with Quality Inspection

Automated manufacturing, driven by rising demands for mass-produced products, calls for efficient systems such as the peg-in-hole assembly. Traditional industrial robots perform these tasks but often fall short in speed during pick-and-place processes. This study presents an innovative mechatronic system for peg-in-hole assembly, integrating a novel peg insertion tool, assembly mechanism and control algorithm. This combination achieves peg insertion with a 200 µm tolerance without the need for pick-and-place, meeting the requirements for high precision and rapidity in modern manufacturing. Dual cameras and computer vision techniques, both traditional and machine learning (ML)-based, are employed to detect workpiece features essential for assembly. Traditional methods focus on image enhancement, edge detection and circular feature recognition, whereas ML verifies workpiece positions. This research also introduces a novel statistical quality inspection, offering an alternative to standard ML inspections. Through rigorous testing on varied workpiece surfaces, the robustness of the methods is affirmed. The assembly system demonstrates a 99.00% success rate, while the quality inspection method attains a 97.02% accuracy across diverse conditions, underscoring the potential of these techniques in automated assembly, defect detection and product quality assurance

Experimental studies on shock wave interactions with flexible surfaces and development of flow diagnostic tools

Nowadays, light-weight composite materials have increasingly used for high-speed flight vehicles to improve their performance and efficiency. At supersonic speed, sonic fatigue, panel flutter, severe instabilities, and even catastrophic structural failure would occur due to the shock wave impingement on several flexible components of a given structural system either internally or externally. Therefore, investigation on shock wave interaction with flexible surfaces is crucial for the safety and performance of high-speed flight vehicles. This work aims to investigate the mechanism of shock wave interaction with flexible surfaces with and without the presence of the boundary layer. The first part involves the shock wave generated by supersonic starting jets interaction with flexible surfaces and the other one focuses on shock wave and boundary layer interaction (SBLI) over flexible surfaces. A novel miniature and cost-effective shock tube driven by detonation transmission tubing was designed and manufactured to simulate the supersonic starting jet and investigate the interaction of a supersonic starting jet with flexible surfaces. To investigate the characterization of this novel type shock tube, the pressure-time measurement in the driven section and the time-resolved shadowgraph were performed. The result shows that the flow structure from the open end of the shock tube driven by detonation transmission tubing agrees with that of conventional compressed-gas driven shock tubes. Moreover, this novel type of shock tube has good repeatability of less than 3% with a Mach number range of 1.29-1.58 when the weight of the NONEL explosive mixture varies from 3.6mg to 12.6mg. An unsteady background oriented schlieren (BOS) measurement system and a sprayable Polymer-Ceramic unsteady pressure sensitive paint (PC-PSP) system were developed. The preliminary BOS result in a supersonic wind tunnel shows that the sensitivity of the BOS system is good enough to visualize weak density variations caused by expansion waves, boundary layer, and weak oblique shocks. Additionally, compared with the commercial PC-PSP from Innovative Scientific Solutions Incorporated (ISSI), the in-house developed unsteady PSP system has higher pressure sensitivity, lower temperature sensitivity, and photo-degradation rate. To identify the shock movement, distortion and unsteadiness during the processes of the supersonic starting jet impingement and shock wave boundary layer interaction (SBLI) over flexible surfaces, an image processing scheme involving background subtraction in the frequency domain, filtering, resampling, edge detection, adaptive threshold, contour detection, feature extraction, and fitting was proposed and applied to process shadowgraph and schlieren sequences automatically. A large shadowgraph data set characterized by low signal to noise ratio (SNR) and small spatial resolution (312×260-pixel), was used to validate the proposed scheme. The result proves that the aforementioned image processing scheme can detect, track, localize, and fit shock waves in a subpixel accuracy. The mechanism of the interaction between the initial shock wave from a supersonic starting jet and flexible surfaces was investigated based on a square shock tube driven by detonation transmitting tube. Compared with that of the solid plate case, flexible surfaces can delay the shock reflection process because of the flexible panel deformation generated by the pressure difference between the top and the bottom. The delay time is around 8µs in the case of 0.1mm thick flexible surface, whereas it declines to around 4µs in the case of 0.3mm thick flexible surface because of the lower flexibility and deformation magnitude. However, interestingly, the propagation velocity of the reflected shock wave is basically the same for the solid plate and flexible panels, which means the flexible surface doesn’t reduce the strength of the reflection wave, although it delays its propagation. Also, there is not an apparent difference in the velocity of the reflected shock wave in the case of different incident shock Mach numbers when Ms varying from 1.22 to 1.54. These experimental results from this study are useful for validating numerical codes that are used for understanding fluid-structure interaction processes

Virtual aids to navigation

There are many examples of master, bridge crew and pilot errors in navigation causing grounding under adverse circumstances that were known and published in official notices and records. Also dangerous are hazards to navigation resulting from dynamic changes within the marine environment, inadequate surveys and charts. This research attempts to reduce grounding and allision incidents and increase safety of navigation by expanding mariner situational awareness at and below the waterline using new technology and developing methods for the creation, implementation and display of Virtual Aids to Navigation (AtoN) and related navigation information. This approach has widespread significance beyond commonly encountered navigation situations. Increased vessel navigation activity in the Arctic and sub-Arctic regions engenders risk due, in part, to the inability to place navigational aids and buoys in constantly changing ice conditions. Similar conditions exist in tropical regions where sinker placement to moor buoys in sensitive environmental areas with coral reefs is problematic. Underdeveloped regions also lack assets and infrastructure needed to provide adequate navigation services, and infrastructure can also rapidly perish in developed regions during times of war and natural disaster. This research exploits rapidly developing advances in environmental sensing technology, evolving capabilities and improved methods for reporting real time environmental data that can substantially expand electronic navigation aid availability and improve knowledge of undersea terrain and imminent hazards to navigation that may adversely affect ship operations. This is most needed in areas where physical aids to navigation are scarce or non-existent as well as in areas where vessel traffic is congested. Research to expand related vessel capabilities is accomplished to overcome limitations in existing and planned electronic aids, expanding global capabilities and resources at relatively low-cost. New methods for sensor fusion are also explored to vi reduce overall complexity and improve integration with other navigation systems with the goal of simplifying navigation tasks. An additional goal is to supplement training program content by expanding technical resources and capabilities within the confines of existing International Convention on Standards for Training, Certification and Watchkeeping for Seafarers (STCW) requirements, while improving safety by providing new techniques to enhance situational awareness

A Novel Online Any-Angle Path Planning Algorithm

Any-angle path planning algorithms are a popular topic of research in the fields of robotics and video games with a key focus in finding true shortest paths. Most online grid-constrained path-planning algorithms find suboptimal solutions that present as unrealistic paths, a shortcoming which the any-angle class of algorithms attempt to address. While they do provide improvements in finding shorter paths, it generally comes in the form of a trade-off, by sacrificing runtime performance. The lack of a robust solution, that does not compromise on any of the desirable properties – online, reduced search-space, low runtime, short paths – of an any-angle path-planning algorithm, is a prime motivator for the current research. A novel any-angle algorithm for 2-dimensional uniform-cost octile grids is introduced that operates purely online and reduces the search-space and runtime without sacrificing path-length. The methodology presents an atypical any-angle path-planning algorithm which employs a best first search that races individual paths towards a target with a free-space assumption. The paths exhibit bug-like properties in that they either move towards a target or wall-follow, but are allowed to terminate early. Wall following determines points on the boundary that are candidate heading changes in the path. At each step, the path is analysed and pruned in order to maintain its tautness at all times. Together with a purely heuristic cost based on the assumption of free-space between heading changes, the algorithm drives the search towards expanding the most promising path first. Once a path has reached the goal, it checks the free-space assumption between its heading changes and updates its cost accordingly. The shortest-path is determined when the cost estimate of any remaining paths is longer than the solution path. The proposed algorithm is shown experimentally to be competitive on a number of performance metrics with state-of-the-art any-angle algorithms. It also presents desirable properties that allow it to have a reduced search space and make it suitable for providing multiple solutions