https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Mukhopadhyay%20-%20Hough%20Transform%20-%202014.pdf

A survey of Hough Transform

Proceedings of world academy of science, engineering and technology, vol 8

With the marvelous increase in video and image database there is an incredible need of automatic understanding and examination of information by the intelligent systems as manually it is getting to be plainly distant. Face plays a major role in social intercourse for conveying identity and feelings of a person. Human beings have not tremendous ability to identify different faces than machines. So, automatic face detection system plays an important role in face recognition, facial expression recognition, head-pose estimation, human–computer interaction etc. Face detection is a computer technology that determines the location and size of a human face in a digital image. Face detection has been a standout amongst topics in the computer vision literature. This paper presents a comprehensive survey of various techniques explored for face detection in digital images. Different challenges and applications of face detection are also presented in this paper. At the end, different standard databases for face detection are also given with their features. Furthermore, we organize special discussions on the practical aspects towards the development of a robust face detection system and conclude this paper with several promising directions for future research.

Face detection techniques: a review

서브블록 히스토그램 등화기법을 이용한 개선된 콘트래스트 강화 알고리즘 ( An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization )

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

Lane detection can provide important information for safety driving. In this paper, a real time vision-based lane detection method is presented to find the position and type of lanes in each video frame. In the proposed lane detection method, lane hypothesis is generated and verified based on an effective combination of lane-mark edge-link features. First, lane-mark candidates are searched inside region of interest (ROI). During this searching process, an extended edge-linking algorithm with directional edge-gap closing is used to produce more complete edge-links, and features like lane-mark edge orientation and lane-mark width are used to select candidate lane-mark edge-link pairs. For the verification of lane-mark candidates, color is checked inside the region enclosed by candidate edge-link pairs in YUV color space. Additionally, the continuity of the lane is estimated employing a Bayesian probability model based on lane-mark color and edge-link length ratio. Finally, a simple lane departure model is built to detect lane departures based on lane locations in the image Experiment results show that the proposed lane detection method can work robustly in real-time, and can achieve an average speed of 30~50ms per frame for 180x120 image size, with a correct detection rate over 92%.

Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm

In order to enhance the contrast in the regions where the pixels have similar intensities, this paper presents a new histogram equalization scheme. Conventional global equalization schemes over-equalizes these regions so that too bright or dark pixels are resulted and local equalization schemes produce unexpected discontinuities at the boundaries of the blocks. The proposed algorithm segments the original histogram into sub-histograms with reference to brightness level and equalizes each sub-histogram with the limited extents of equalization considering its mean and variance. The final image is determined as the weighted sum of the equalized images obtained by using the sub-histogram equalizations. By limiting the maximum and minimum ranges of equalization operations on individual sub-histograms, the over-equalization effect is eliminated. Also the result image does not miss feature information in low density histogram region since the remaining these area is applied separating equalization. This paper includes how to determine the segmentation points in the histogram. The proposed algorithm has been tested with more than 100 images having various contrasts in the images and the results are compared to the conventional approaches to show its superiority.

/pdf/image-contrast-enhancement-based-sub-histogram-equalization-1xu87u5n79.pdf

Image Contrast Enhancement based Sub-histogram Equalization Technique without Over-equalization Noise

This paper presents a robust vehicle detection approach using Haar-like feature. It is possible to get a strong edge feature from this Haar-like feature. Therefore it is very effective to remove the shadow of a vehicle on the road. And we can detect the boundary of vehicles accurately. In the paper, the vehicle detection algorithm can be divided into two main steps. One is hypothesis generation, and the other is hypothesis verification. In the first step, it determines vehicle candidates using features such as a shadow, intensity, and vertical edge. And in the second step, it determines whether the candidate is a vehicle or not by using the symmetry of vehicle edge features. In this research, we can get the detection rate over 15 frames per second on our embedded system. Keywords—vehicle detection, haar-like feauture, single camera, real time

Vehicle Detection Method using Haar-like Feature on Real Time System

In driving assistance systems, lane detection can provide significant information for driving safety. In this paper, we proposed a novel real-time lane detection method to extract the location of lane marking lines based on inverse perspective mapping transform (top view) for the region of interest (ROI) of a video frame. The data were then filtered by a selective oriented Gaussian high pass filter, Hough transformation, and Kalman filter to give the initial regions to our optimized RANSAC (Random Sample Consensus) Bezier splines fitting algorithm, which is the main innovation in this paper. Our experimental results and accuracy evaluation indicated that the proposed lane detection algorithm could run robustly in real time, and could achieve an average speed of 32.32 ms per frame for a 320 x 240 pixel image and 41.64 ms for a 640 x 480 pixel image, with a correct detection rate of over 92.85%. Moreover, our method was found to be suitable for various conditions.

Youngjoon Han

Papers

Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm

Image Contrast Enhancement based Sub-histogram Equalization Technique without Over-equalization Noise

Vehicle Detection Method using Haar-like Feature on Real Time System

A real-time system of lane detection and tracking based on optimized RANSAC B-spline fitting

A new algorithm for ellipse detection by curve segments