Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa

The Use Of Gabor Filter And Back-Propagation Neural Network For The Automobile Types Recognition

Type of automobile is the general factor that makes automobile different from each other. But conventional sensor cannot detect the automobile and recognize its type. Because of those reasons, we made an experiment application that can count the number and recognize automobile based on its type. This application uses gabor filter for feature extraction and back-propagation neural network for training and recognizing type of automobile. The experiment was done using various parameters for back-propagation neural network and gabor filters. The experimental result shows that the best error rate of recognition results is 16%. It’s done with the brightness condition not too low or high

Human Automotive Interaction: Affect Recognition for Motor Trend Magazine\u27s Best Driver Car of the Year

Observation analysis of vehicle operators has the potential to address the growing trend of motor vehicle accidents. Methods are needed to automatically detect heavy cognitive load and distraction to warn drivers in poor psychophysiological state. Existing methods to monitor a driver have included prediction from steering behavior, smart phone warning systems, gaze detection, and electroencephalogram. We build upon these approaches by detecting cues that indicate inattention and stress from video. The system is tested and developed on data from Motor Trend Magazine\u27s Best Driver Car of the Year 2014 and 2015. It was found that face detection and facial feature encoding posed the most difficult challenges to automatic facial emotion recognition in practice. The chapter focuses on two important parts of the facial emotion recognition pipeline: (1) face detection and (2) facial appearance features. We propose a face detector that unifies state‐of‐the‐art approaches and provides quality control for face detection results, called reference‐based face detection. We also propose a novel method for facial feature extraction that compactly encodes the spatiotemporal behavior of the face and removes background texture, called local anisotropic‐inhibited binary patterns in three orthogonal planes. Real‐world results show promise for the automatic observation of driver inattention and stress

Machine Analysis of Facial Expressions

No abstract