From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Foot Progression Angle (FPA) detection is an important measurement in clinical gait analysis. Currently, the FPA can only be computed, while walking in a laboratory with a marker-based or Initial Measure Unit (IMU) based motion capture systems. A novel Visual Feature Matching (VFM) method is presented here, measuring the FPA by comparing the shoe orientation with the progression, i.e. the walking direction. Both the foot orientation and progression direction are detected by image processing methods in rectified digital images. Differential FPA (DFPA) algorithm is developed to provide accurate FPA measurement. The hardware of this system combines only one wearable sensor, a chest or torso mounted smart phone camera, and a laptop on the same Wi-Fi network. There is no other prerequisite hardware installation or other specialized set up. This method is a solution for long-term gait self-monitoring in a home or community like environments. Our novel approach leads to simple and persistent, real time remote gait FPA monitoring, and it is a core of new bio-feedback medical procedure.

A Novel Foot Progression Angle Detection Method

Brain activities initiate motion in the human body. In our research we try to detect brain electrical activities and generate control signals for robotic devices like prosthetic legs. Human legs are associated with a small representation area in the sensorimotor (SMR) cortex, which is located deep inside the inter hemispheric fissure. It is difficult to observe any electroencephalographic activity related to the legs. Detection of sensorimotor signals, based on leg imagery, could potentially be useful in medical applications, i.e. for systems that are using brain-computer interface for lower limbs assistance. We investigate reactivity of sensorimotor rhythm i.e., mu rhythm, as a result of given tasks, such as, motor execution (ME) and motor imagery (MI) of the leg. Resulting SMR was analyzed, for each task state and evaluated in terms of eventrelated de synchronization and event-related synchronization patterns. Higher power concentration was observed in the foot representation and peripheral areas, during both ME and MI tasks. No contralateral dominance was detected during left or right discrimination tasks. Results provide a foundation for leg imagery based, interfacing and control signals creation. This could be used for locomotion functions' restoration in a lower limb wearable rehabilitation system. Spinal cord injury patients could, also, be potential users of this type of biomechanical systems.

https://medcrave.org.medcraveonline.com/index.php/IRATJ/article/download/7367/13890

Event-related changes detection in sensorimotor rhythm

This study underscores the reactivity of sensorimotor rhythms (SMR) in EEG such as mu rhythm in connection with the imagination and voluntary movement of left and right knee extension with able-bodied subjects using the standard Graz brain-computer interface (BCI) paradigm. As the knee motor execution (ME) and motor imagery (MI) reflect the user's voluntary physical and imagination state of knee extension respectively, they have been analyzed in order to be used as control signals to restore motor function with neuro-plasticity for BCI applications. This reactivity has been evaluated using the quantification of event-related desynchronization (ERD) and event-related synchronization (ERS) patterns. Investigations on the cortical lateralization of ERD/ERS during the left and right knee MI and ME for the extension task was done. During left and right knee MI, the foot area mu rhythm was desynchronized in all subjects, whereas no enhancement of the hand area mu rhythm was observed during any task. The frequency of the most reactive components for mu rhythm was 8.8±0.5 Hz. Interestingly a contralateral dominance was visible at a central electrode position, during left knee extension MI task. This lead to the establishment of an understanding of the left-right knee MI and its associated cognitive behavior.

Detection of knee motor imagery by Mu ERD/ERS quantification for BCI based neurorehabilitation applications

A simplified mathematical tire model is introduced for combined longitudinal and lateral slip conditions in tire forces. The model includes the saturation of tire forces in both, longitudinal and lateral directions, considering the drop in longitudinal and lateral forces in the presence of lateral and longitudinal slips. The empirical Magic Formula is compared with the model in combined-slip condition. The presented model is found to provide good approximation in fitting experimental data, while being a simple analytical model for combined-slip condition with the minimum number of parameters needed.

Elliptical Combined-Slip Tire Model in Vehicle Dynamics

This paper presents application of multi-attribute decision making applied in a large public institution, to make strategic choice about new mobility technology. Electrical vehicles are becoming important parts of all vehicles product portfolios. Based on their advantages, many users are making transitions and replacing internal combustion engine vehicles with electrical vehicles. Considering a large number of vehicles, with various features, which are currently available on the market, and specific users’ requirements, it is often required to apply a comprehensive decision making model in order to select the best solution. In the project presented here, decision making process and methodology used by the Institution, is highlighted. Final decision is based on the knowledge of the previous solution, already executed by one organization with similar operational requirements, and on inputs from internal departments which placed their preferences on various features of several models of electrical vehicles.

Milan Simic

Papers

A Novel Foot Progression Angle Detection Method

Event-related changes detection in sensorimotor rhythm

Detection of knee motor imagery by Mu ERD/ERS quantification for BCI based neurorehabilitation applications

Elliptical Combined-Slip Tire Model in Vehicle Dynamics

Transition to Electrical Vehicles Based on Multi-Attribute Decision Making