The challenge of large-scale image retrieval has been recently addressed by many promising approaches. In this work, we propose a new approach that jointly optimizes the search accuracy and time by using binary local image descriptors, such as BRIEF and BRISK, and binary hashing methods, such as Locality Sensitive Hashing (LSH) and Spherical Hashing. We propose a Multi-bin search method that highly improves the retrieval precision of binary hashing methods. Also, we introduce a reranking scheme that increases the retrieval precision, but with a slight increase in search time. Evaluations on the University of Kentucky Benchmark (UKB) dataset show that the proposed approach greatly improves the retrieval precision of recent binary hashing approaches.

Augmented Reality ‘‘AR’’ is a promising paradigm that can offer users with real-time, high- quality visualization of a wide variety of information. In AR, virtual objects are added to the real-world view in real time. The AR technology can offer a very realistic environment for enhancing drivers’ performance on the road and testing drivers’ ability to react to different road design and traffic operations scenarios. This can be achieved by adding virtual objects (people, vehicles, hazards, and other objects) to the normal view while driving an actual vehicle in a real environment. This paper explores a new Augmented Reality Vehicle ‘‘ARV’’ system and attempts to apply this new concept to a selected traffic engineering application namely the left-turn maneuver at two-way stop-controlled ‘‘TWSC’’ intersec- tion. This TWSC intersection experiment, in addition to testing the feasibility of the appli- cation, tries to quantify the size of gaps accepted by different driver’s characteristics (age and gender). The ARV system can be installed in any vehicle where the driver can see the surrounding environment through a Head Mounted Display ‘‘HMD’’ and virtual objects are generated through a computer and added to the scene. These different environments are generated using a well defined set of scenarios. The results from this study supported the feasibility and validity of the proposed ARV system and they showed promise for this system to be used in the field-testing for the safety and operation aspects of transportation research. Results of the left-turn maneuver study revealed that participants accepted gaps in the range of 4.0–9.0 s. This finding implies that all gaps below 4 s are rejected and all gaps above 9 s are likely to be accepted. The mean value of the left-turn time was 4.67 s which is a little bit higher than reported values in the literature (4.0–4.3 s). Older drivers were found to select larger gaps to make left turns than younger drivers. The conservative driving attitude of older drivers indicates the potential presence of reduced driving ability of elderly. Drivers’ characteristics (age and gender) did not significantly affect the left-turn time. Based on the survey questions that were handed to participants, most participants indicated good level of comfort with none or small level of risk while driving the vehicle with the ARV system. None of the participants felt any kind of motion sickness and the participants’ answers indicated a good visibility and realism of the scene with overall good system fidelity.

Abstract—Applying new technologies to traffic engineering studies has become more urgent due to the high cost and risk associated with ordinary in-the-field testing. Augmented reality (AR) is one of those technologies, in which virtual (computer- generated) objects are added to the real scene in a way that the user cannot distinguish between real and virtual objects in the final scene. Adding virtual objects (people, vehicles, hazards, and other objects) to the normal view can provide a safe realistic environ- ment for testing driving performance under different scenarios. This paper presents two systems, i.e., AR vehicle (ARV) and offline AR simulator (OARSim) systems, and uses them to study the left- turn driving behavior at an unsignalized intersection for drivers with different characteristics. Two experiments were performed: one using the ARV system installed in a vehicle and another using the OARSim system installed in the laboratory. Quantitative mea- surements of left-turn drivers’ behaviors were recorded. There was no significant gender effect on all measured parameters in both experiments. Older drivers selected larger gaps and used smaller acceleration rates to turn left than younger drivers in both experiments. The conservative driving attitude of older drivers indicates the potential presence of reduced driving ability of the elderly. While left-turn times using the ARV system were not sig- nificantly affected by drivers’ age, older drivers took longer time to complete the left-turn maneuver than younger drivers using the OARSim did. Results from this study supported the feasibility and validity of the proposed systems and showed promise for these systems to be used as surrogates to in-the-field testing for safety and operation aspects of transportation research.

Abstract Pattern Matching is a fundamental problem in computer vision, and image and video processing. Exhaustive-Search equivalent algorithms yield the same results as exhaustively searching all patterns in the image but significantly faster. In this paper, we propose a novel exhaustive-search equivalent algorithm that is combined with a number of state-of-art algorithms to provide a significantally faster alternative in the problem of finding nearest pattern according to a predefined distance measure. Our technique also shows high resilience to both blurring and JPEG compression types of noise. This is demonstrated in the paper with results from over 15 million runs for each compared algorithm.

Abstract Pattern Matching is a fundamental problem in computer vision, and image and video processing. Exhaustive-Search equivalent algorithms yield the same results as exhaustively searching all patterns in the image but significantly faster. In this paper, we propose a novel exhaustive-search equivalent algorithm that is combined with a number of state-of-art algorithms to provide a significantally faster alternative in the problem of finding nearest pattern according to a predefined distance measure. Our technique also shows high resilience to both blurring and JPEG compression types of noise. This is demonstrated in the paper with results from over 15 million runs for each compared algorithm.

Abstract Augmented Reality “AR” is a promising paradigm that can offer users with real-time, high-quality visualization of a wide variety of information. In AR, virtual objects are added to the real-world view in real time. The AR technology can offer a very realistic environment for enhancing drivers’ performance on the road and testing drivers’ ability to react to different road design and traffic operations scenarios. This can be achieved by adding virtual objects (people, vehicles, hazards, and other objects) to the normal view while driving an actual vehicle in a real environment. This paper explores a new Augmented Reality Vehicle “ARV” system and attempts to apply this new concept to a selected traffic engineering application namely the left-turn maneuver at two-way stop-controlled “TWSC” intersection. This TWSC intersection experiment, in addition to testing the feasibility of the application, tries to quantify the size of gaps accepted by different driver’s characteristics (age and gender). The ARV system can be installed in any vehicle where the driver can see the surrounding environment through a Head Mounted Display “HMD” and virtual objects are generated through a computer and added to the scene. These different environments are generated using a well defined set of scenarios. The results from this study supported the feasibility and validity of the proposed ARV system and they showed promise for this system to be used in the field-testing for the safety and operation aspects of transportation research. Results of the left-turn maneuver study revealed that participants accepted gaps in the range of 4.0–9.0 s. This finding implies that all gaps below 4 s are rejected and all gaps above 9 s are likely to be accepted. The mean value of the left-turn time was 4.67 s which is a little bit higher than reported values in the literature (4.0–4.3 s). Older drivers were found to select larger gaps to make left turns than younger drivers. The conservative driving attitude of older drivers indicates the potential presence of reduced driving ability of elderly. Drivers’ characteristics (age and gender) did not significantly affect the left-turn time. Based on the survey questions that were handed to participants, most participants indicated good level of comfort with none or small level of risk while driving the vehicle with the ARV system. None of the participants felt any kind of motion sickness and the participants’ answers indicated a good visibility and realism of the scene with overall good system fidelity.

Abstract Pattern Matching is a fundamental problem in computer vision, and image and video processing. Exhaustive-Search equivalent algorithms yield the same results as exhaustively searching all patterns in the image but significantly faster. Though much work have been done over the L1 and L2 distances, only small amount of work has been dedicated to the Chebyshev distance though its importance in many applications. In this paper, we provide an evaluation of available state-of-art exhaustive-search equivalent algorithm that targets the Chebyshev distance; we also provide detailed analysis of the performance characteristics of evaluated algorithms.

Abstract Pattern Matching is a fundamental problem in computer vision, and image and video processing. Exhaustive-Search equivalent algorithms yield the same results as exhaustively searching all patterns in the image but significantly faster. Though much work have been done over the L1 and L2 distances, only small amount of work has been dedicated to the Chebyshev distance though its importance in many applications. In this paper, we provide an evaluation of available state-of-art exhaustive-search equivalent algorithm that targets the Chebyshev distance; we also provide detailed analysis of the performance characteristics of evaluated algorithms.

ABSTRACT Digital Content Creation (DCC) Applications (e.g. Blender, Autodesk 3ds Max) have long been used for the creation and editing of digital content (e.g. Images, videos). Due to current advancement in the field, the need for controlled automated work forced these applications to add support for scripting languages that gave power to artists without diving into many details. With time these languages developed into more mature languages and were used for more complex tasks (driving physics simulations, controlling particle systems, or even game engines).For long, these languages have been interpreted, embedded within the applications, lagging the UIs or incomparable with real programming languages (regarding Completeness, Expressiveness, Extensibility and Abstractions). In this paper, we present a high level scripting language (Zlang) and a DCC Engine that addresses those problems. The language can be interpreted, compiled, extended in C/C++ and has a number of constructs, and optimizations dedicated to DCC domain. The engine provides geometric primitives, mesh modifiers, key-framed animation and Physics Simulations (Rigid Body, and Cloth Simulations). The engine is designed and implemented as a library so it can be used alone or embedded.

ABSTRACT Digital Content Creation (DCC) Applications (e.g. Blender, Autodesk 3ds Max) have long been used for the creation and editing of digital content (e.g. Images, videos). Due to current advancement in the field, the need for controlled automated work forced these applications to add support for scripting languages that gave power to artists without diving into many details. With time these languages developed into more mature languages and were used for more complex tasks (driving physics simulations, controlling particle systems, or even game engines).For long, these languages have been interpreted, embedded within the applications, lagging the UIs or incomparable with real programming languages (regarding Completeness, Expressiveness, Extensibility and Abstractions). In this paper, we present a high level scripting language (Zlang) and a DCC Engine that addresses those problems. The language can be interpreted, compiled, extended in C/C++ and has a number of constructs, and optimizations dedicated to DCC domain. The engine provides geometric primitives, mesh modifiers, key-framed animation and Physics Simulations (Rigid Body, and Cloth Simulations). The engine is designed and implemented as a library so it can be used alone or embedded.