Real-time automated road, lane and car detection for autonomous driving

In this paper, we discuss a vision based system for autonomous guidance of vehicles. An autonomous intelligent vehicle has to perform a number of functionalities. Segmentation of the road, determining the boundaries to drive in and recognizing the vehicles and obstacles around are the main tasks for vision guided vehicle navigation. In this article we propose a set of algorithms which lead to the solution of road and vehicle segmentation using data from a color camera. The algorithms described here combine gray value difference and texture analysis techniques to segment the road from the image, several geometric transformations and contour processing algorithms are used to segment lanes, and moving cars are extracted with the help of background modeling and estimation. The techniques developed have been tested in real road images and the results are presented

Moments of elliptic fourier descriptors

This paper develops a recursive method for computing moments of 2D objects described by elliptic Fourier descriptors (EFD). Green’s theorem is utilized to transform 2D surface integrals into 1D line integrals and EFD description is employed to derive recursions for moments computations. Experiments are performed to quantify the accuracy of our proposed method. Comparison with Bernstein-B´ezier representations is also provided

Shape and data-driven texture segmentation using local binary patterns

We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this “filtered” domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem as an optimization problem in a Bayesian framework. The cost functional contains a data-driven term, as well as a term that brings in information about the shapes of the objects to be segmented. We solve the optimization problem using level set-based active contours. Our experimental results on synthetic and real textures demonstrate the effectiveness of our approach in segmenting challenging textures as well as its robustness to missing data and occlusions

A texture based approach to reconstruction of archaeological finds

Reconstruction of archaeological finds from fragments, is a tedious task requiring many hours of work from the archaeologists and restoration personnel. In this paper we present a framework for the full reconstruction of the original objects using texture and surface design information on the sherd. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The confidence of this process is also defined. Feature values are derived from these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. The optimization of total affinity gives the best assembly of the piece. Experimental results are presented on real and artificial data

3D head tracking using normal flow constraints in a vehicle environment

Head tracking is a key component in applications such as human computer interaction, person monitoring, driver monitoring, video conferencing, and object-based compression. The motion of a driver’s head can tell us a lot about his/her mental state; e.g. whether he/she is drowsy, alert, aggressive, comfortable, tense, distracted, etc. This paper reviews an optical flow based method to track the head pose, both orientation and position, of a person and presents results from real world data recorded in a car environment

Graphical model based facial feature point tracking in a vehicle environment

Facial feature point tracking is a research area that can be used in human-computer interaction (HCI), facial expression analysis, fatigue detection, etc. In this paper, a statistical method for facial feature point tracking is proposed. Feature point tracking is a challenging topic in case of uncertain data because of noise and/or occlusions. With this motivation, a graphical model that incorporates not only temporal information about feature point movements, but also information about the spatial relationships between such points is built. Based on this model, an algorithm that achieves feature point tracking through a video observation sequence is implemented. The proposed method is applied on 2D gray scale real video sequences taken in a vehicle environment and the superiority of this approach over existing techniques is demonstrated

The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş

A Comparison on Features Efficiency in Automatic Reconstruction of Archeological Broken Objects

Automatic reconstruction of archeological broken objects is an invaluable tool for restoration purposes and personnel. In this paper, we assume that broken pieces have similar characteristics on their common boundaries, when they are correctly combined. In this paper we work in a framework for the full reconstruction of the original objects using texture and surface design information on the sherd. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. We present a quantitative and qualitative comparison over a large set of features and over a large set of synthetic and real archeological broken objects

Ölçekten bağımsız öznitelik dönüşümü kullanarak stereo kamera ile üç boyutlu kafa takibi = Stereo based 3D head pose tracking using the scale invariant feature transform

Bu makalede üç boyutlu kafa takibi için ölçekten ba˘gımsız öznitelik dönüşümünne (SIFT) dayalı bir yöntem önerilmektedir. 3B kafa takibi bir c¸ok bilgisayarla görme uygulaması için önemli bir ön işlemdir. Önerilen yöntemin, düzlem dışı öteleme ve dönmelere karşı gürbüz olduğu belirlenmis¸ aynı zamanda görüntüdeki ani değişen aydınlanma farklarından da etkilenmediği gözlenmiştir. Kafa takibi ile elde edilebilecek dönmeye göre düzeltilmiş bir imge ile yüz tanıması, ifade analizi, dudak okuması gibi problemleri çözmek daha kolay olacaktır. Önerdiğimiz SIFT tabanlı yöntemin başarısını sentetik ve stereo kamera ile çekilmiş gerçek görüntüler üzerinde deneyip var olan diğer yöntemlerle karşılaştırmasını yaptık. In this paper a new stereo-based 3D head tracking technique, based on scale-invariant feature transform (SIFT) features is proposed. A 3D head tracker is very important preprocessing for many vision applications. The proposed method is robust to out of plane rotations and translations and also invariant to sudden changes in time varying illumination. We present experiments to test the accuracy of our SIFT based 3D tracker on sequences of synthetic and real stereo images

A Local binary patterns and shape priors based texture segmentation method

We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this “filtered” domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem as an optimization problem in a Bayesian framework. The cost functional contains a data-driven term, as well as a term that brings in information about the shapes of the objects to be segmented. We solve the optimization problem using level set-based active contours. Our experimental results on synthetic and real textures demonstrate the effectiveness of our approach in segmenting challenging textures as well as its robustness to missing data and occlusions