Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Colorization of Multispectral Image Fusion using Convolutional Neural Network approach

The proposed technique  offers a significant advantage in enhancing multiband nighttime imagery for surveillance and navigation purposes., The multi-band image data set comprises visual  and infrared  motion sequences with various military and civilian surveillance scenarios which include people that are stationary, walking or running, Vehicles and buildings or other man-made structures. Colorization method led to provide superior discrimination, identification of objects (Lesions), faster reaction times and an increased scene understanding than monochrome fused image. The guided filtering approach is used to decompose the source images hence they are divided into two parts: approximation part and detail content part further the weighted-averaging method is used to fuse the approximation part. The multi-layer features are extracted from the detail content part using the VGG-19 network. Finally, the approximation part and detail content part will be combined to reconstruct the fused image. The proposed approach has offers better outcomes equated to prevailing state-of-the-art techniques in terms of quantitative and qualitative parameters. In future, propose technique will help Battlefield monitoring, Defence for situation awareness, Surveillance, Target tracking and Person authentication

Evaluation and improvement of a face detection method on the basic of skin color

Este proyecto aborda el análisis y evaluación de un método de detección de rostros basado en el color de la piel, es decir, teniendo en cuenta principalmente las características de color de la imagen para llevar a cabo la detección de rostros. Este proceso se logra a través de tres pasos fundamentales: la transformación del espacio de color, la generación de imágenes en escala de grises y la estimación del valor umbral óptimo que nos lleva al último paso que es la segmentación. La detección de rostros es un paso crítico en cualquier aplicación de análisis facial, tal como el reconocimiento de rostros, la codificación de vídeo para videoconferencia, inteligencia artificial, etc. En general, este tipo de detección suele implicar problemas muy complejos, ya que los rostros tienen diferentes colores, expresiones, poses y tamaños relativos o se encuentran en diferentes condiciones de iluminación. _________________________________________________________________________________________________________________________This project deals with the analysis and evaluation of a face detection method based on skin color, ie, taking into account mainly the color characteristics of the image to perform face detection. This process is accomplished through three fundamental steps: transformation of color space, generating grayscale images and establishing optimal thresholding value to reach the last step that is segmentation. To evaluate the proposed algorithm we have decided to use two databases with different characteristics. The first works with images with a controlled background, specifically white and smooth, the only change are the characters of the images and the illumination. The second database works with a not controlled background, that is, each picture is taken in a different environment, with different luminosities, characters and objects, these changes hinder the detection process as discussed below. After analyzing the performance of the face detector we must determine whether the results we have are good for each of the situations that ariseIngeniería Técnica en Sonido e Image

Automaattinen syväoppimiseen perustuva puun vuosikasvun analysointi sahateollisuudessa

Analysis of wood growth is an important quality control step in a sawmill, as it predicts the structure and load-bearing capabilities of the wood. The annual growth of wood is determined by calculating the distances between the annual rings in a wood end-face. The wood is moving fast in a process line, and manual analysis of woodgrowthisalaborioustaskthatispronetoerrors. Havingtheprocessautomated increases the efficiency and throughput of the sawmill as well as reduces monotonic manual labor, thus providing better working conditions. Automatic counting of annual ring distances has been studied before, however, little research has been done on a sawmill setting which suffers from difficult imaging conditionsandroughwoodend-faceswithvariousdefects. Previousstudieshaveused traditional image processing methods which rely on handcrafted features and fail to generalize well on wood end-faces with varying conditions and arbitrary shaped annual rings. This thesis proposes a general solution to the problem by developing complete end-to-end software for detecting annual rings and analyzing wood growth using deep learning methods. The proposed system is described in detail and compared against traditional computer vision methods. Using data from a real sawmill, the deep learning based approach performs better than the traditional methods.Puun vuosikasvun analysointi on tärkeä osa laadunvarmistusta sahalla, sillä vuosikasvu määrittää puun rakenteen ja kestävyyden. Lankut kulkevat nopeasti tehdaslinjastolla, joten manuaalinen vuosikasvun analysointi on vaivalloista ja virhealtista työtä. Prosessin automatisointi lisää sahan suoritustehoa sekä vapauttaa työntekijän mielekkäämpiin tehtäviin. Puun vuosikasvu määritetään selvittämällä vuosirenkaiden väliset etäisyydet lankun päädystä. Automaattista vuosirenkaiden laskentaa on käsitelty kirjallisuudessa aiemmin, mutta vain muutama tutkimus on tehty sahaympäristössä, jossa kuvausolosuhteet ovat epäotolliset ja puupäädyt ovat karheita ja siistimättömiä. Aiemmat tutkimukset ovat käyttäneet perinteisiä konenäkömenetelmiä, jotka toimivat huonosti vaihtelevan laatuisiin ja muotoisiin puun päätyihin sekä vuosirenkaisiin. Tässä työssä kehitetään automaattinen syväoppimiseen perustuva tietokoneohjelmisto vuosirenkaiden tunnistamiseen ja vuosikasvun analysointiin. Ohjelmisto esitellään läpikotaisesti ja sitä verrataan perinteisiin konenäkömenetelmiin. Vertailussa käytettiin oikealta tehtaalta otettua dataa ja syväoppimiseen perustuva järjestelmä suoriutui perinteisiä menetelmiä paremmin

자율주행을 위한 카메라 기반 거리 측정 및 측위

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 서승우.Automated driving vehicles or advanced driver assistance systems (ADAS) have continued to be an important research topic in transportation area. They can promise to reduce road accidents and eliminate traffic congestions. Automated driving vehicles are composed of two parts. On-board sensors are used to observe the environments and then, the captured sensor data are processed to interpret the environments and to make appropriate driving decisions. Some sensors have already been widely used in existing driver-assistance systems, e.g., camera systems are used in lane-keeping systems to recognize lanes on roadsradars (Radio Detection And Ranging) are used in adaptive cruise systems to measure the distance to a vehicle ahead such that a safe distance can be guaranteedLIDAR (Light Detection And Ranging) sensors are used in the autonomous emergency braking system to detect other vehicles or pedestrians in the vehicle path to avoid collisionaccelerometers are used to measure vehicle speed changes, which are especially useful for air-bagswheel encoder sensors are used to measure wheel rotations in a vehicle anti-lock brake system and GPS sensors are embedded on vehicles to provide the global positions of the vehicle for path navigation. In this dissertation, we cover three important application for automated driving vehicles by using camera sensors in vehicular environments. Firstly, precise and robust distance measurement is one of the most important requirements for driving assistance systems and automated driving systems. We propose a new method for providing accurate distance measurements through a frequency-domain analysis based on a stereo camera by exploiting key information obtained from the analysis of captured images. Secondly, precise and robust localization is another important requirement for safe automated driving. We propose a method for robust localization in diverse driving situations that measures the vehicle positions using a camera with respect to a given map for vision based navigation. The proposed method includes technology for removing dynamic objects and preserving features in vehicular environments using a background model accumulated from previous frames and we improve image quality using illuminant invariance characteristics of the log-chromaticity. We also propose a vehicle localization method using structure tensor and mutual information theory. Finally, we propose a novel algorithm for estimating the drivable collision-free space for autonomous navigation of on-road vehicles. In contrast to previous approaches that use stereo cameras or LIDAR, we solve this problem using a sensor fusion of cameras and LIDAR.1 Introduction 1 1.1 Background and Motivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Accurate Object Distance Estimation based on Frequency-Domain Analysis with a Stereo Camera 3 1.2.2 Visual Map Matching based on Structural Tensor and Mutual Information using 3D High Resolution Digital Map 3 1.2.3 Free Space Computation using a Sensor Fusion of LIDAR and RGB camera in Vehicular Environment 4 2 Accurate Object Distance Estimation based on Frequency-Domain Analysis with a Stereo Camera 5 2.1 Introduction 5 2.2 Related Works 7 2.3 Algrorithm Description 10 2.3.1 Overall Procedure 10 2.3.2 Preliminaries 12 2.3.3 Pre-processing 12 2.4 Frequency-domain Analysis 15 2.4.1 Procedure 15 2.4.2 Contour-based Cost Computation 20 2.5 Cost Optimization and Distance Estimation 21 2.5.1 Disparity Optimization 21 2.5.2 Post-processing and Distance Estimation 23 2.6 Experimental Results 24 2.6.1 Test Environment 24 2.6.2 Experiment on KITTI Dataset 25 2.6.3 Performance Evaluation and Analysis 28 2.7 Conclusion 32 3 Visual Map Matching Based on Structural Tensor and Mutual Information using 3D High Resolution Digital Map 33 3.1 Introduction 33 3.2 Related Work 35 3.3 Methodology 37 3.3.1 Sensor Calibration 37 3.3.2 Digital Map Generation and Synthetic View Conversion 39 3.3.3 Dynamic Object Removal 41 3.3.4 Illuminant Invariance 43 3.3.5 Visual Map Matching using Structure Tensor and Mutual Information 43 3.4 Experiments and Result 49 3.4.1 Methodology 49 3.4.2 Quantitative Results 53 3.5 Conclusions and Future Works 54 4 Free Space Computation using a Sensor Fusion of LIDAR and RGB Camera in Vehicular Environments 55 4.1 Introduction 55 4.2 Methodology 57 4.2.1 Dense Depth Map Generation 57 4.2.2 Color Distribution Entropy 58 4.2.3 Edge Extraction 60 4.2.4 Temporal Smoothness 61 4.2.5 Spatial Smoothness 62 4.3 Experiment and Evaluation 63 4.3.1 Evaluated Methods 63 4.3.2 Experiment on KITTI Dataset 64 4.4 Conclusion 68 5 Conclusion 70 Abstract (In Korean) 87Docto

Optimization Methods for Image Thresholding: A review

Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated