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Hand activity is a critical monitoring component in understanding a driver's behavior within the car. Current vision-based hand detection algorithms perform poorly in naturalistic settings, due to various challenges such as global illumination changes and constant hand deformation and occlusion. To achieve a more accurate and robust hand detection system, this paper presents a hierarchical context-aware...
Hand detection is an important issue in the analysis of drivers activities, assessment of drivers alertness, and subsequent development of driver safety monitoring system. In this work, the hand detection problem is addressed in the deep Convolutional Neural Network (CNN) framework. Hypothesis of hand regions are first generated with high recall rate by AdaBoost detector associated with Aggregated...
In this paper, we introduce the application of generic multi-level Convolutional Neural Networks (CNN) approach into the scene understanding or image parsing task. Given an input image, first, a set of similar images from the training set are retrieved based on global-level CNN feature matching similarities. Then, the input test image and the similar images are overseg-mented into superpixels. Next,...
Extracting hand regions and their grasp information from images robustly in real-time is critical for occupants' safety and in-vehicular infotainment applications. It must however, be noted that naturalistic driving scenes suffer from rapidly changing illumination and occlusion. This is aggravated by the fact that hands are highly deformable objects, and change in appearance frequently. This work...
SIMPLE (Searching Images with MPEG-7 (& MPEG-7-like) Powered Localized dEscriptors) is a model that proposes the reuse of well-established global descriptors by localizing their description mechanism on image patches located by local features' detectors. Having displayed impressive retrieval results on two different databases, in this paper we extend the family by replacing the originally picked...
In this paper, we present a method that combines a sparse appearance model into the Bayesian inference framework for tracking pedestrians in video sequences captured by a fixed camera. We formulate sparse appearance model as a linear combination of a set of 4D smoothed colour histograms for each pedestrian. These colour histograms are computed for all detection windows with different confidence values...
In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity...
Effect of frame size and color histogram bins on shot boundary detection performance in terms of precision and recall is experimentally analyzed in this paper. An HS histogram-based shot boundary detector is used for Korean soap operas with eight kinds of frame sizes an bins, respectively, applying various thresholds. The biggest image size and bins are more than one hundred higher than the smallest...
The paper describes a new approach to the analysis of lighting systems, namely the lighting environment in general, with focus on luminance contrast mapping. Using the modern measuring equipment based on luminance analyzer units it is possible to transmit the obtained data from luminance maps to contrast maps. The current methods of determining contrast are too simple and can be applied only to a...
Seagoing vessels have to undergo regular visual inspections in order to detect defects such as cracks and corrosion before they result into catastrophic consequences. These inspections are currently performed manually by ship surveyors at a great cost, so that any level of assistance during the inspection process would significatively decrease the inspection cost. In this paper, we describe a novel...
Automatic detection of human in a video sequence is a canonical instance of object detection. It's considered as a nonrigid object; it has many appearances at different perspectives. Different approaches are used by several methods to combine what is specific to the pedestrian detection, and what is common to the object recognition. A robust solution to this problem would have numerous applications...
Human detection in RGB-D images is an important yet very challenging task in computer vision. In this paper, we propose a novel human detection approach in RGB-D images, which integrates ROI (region-of-interest) generation, depth-size relationship estimation and a human detector. Our approach has the following advantages: 1) ROI generation and depth-size relationship estimation take full advantage...
The recent popularity of smartphones causes significant increase of upskirt filming cases in many countries and regions. To search upskirt images in suspects' IT devices and block them on social media, an effective detector is demanded, but it is neglected by both academic and commercial communities. Three commercial pornographic image detectors and one detector distributed by the U.S. National Institute...
This work introduces a novel feature detection algorithm for the decoding of a binary encoded structured light pattern. To make the structure light pattern insensitive to surface color and texture, some geometrical shapes are used as the pattern elements. Grid-point between each two adjacent rhombic pattern element is defined as the feature points. Affected by the inner structure of pattern element,...
Object proposal, typically served as preprocessing of various multimedia applications, aims to detect the bounding boxes of possible objects in an image. In this paper, we propose a novel object proposal method for RGB-D images based on layered edges, which can effectively eliminate the influence of the mixture of edges from objects and background and improve the accuracy of proposals. Firstly, we...
Real-time human detection in crowded and dynamic environments poses a significant challenge, due to complex background, occlusion and different human poses. In this paper, we propose a two-staged approach using color and depth data taken by an RGB-D camera. The first stage is to find plausible head-top locations quickly in depth image. The second stage is to extract effective discrimination features...
A robust algorithm that detects text from natural scene images and extracts them regardless of the orientation is proposed. All existing methods are designed to operate under a certain constraint, like detecting text only in one direction. Maximally Stable Extremal Regions (MSER) detector is chosen to extract binary regions since it has proven to be robust to lighting conditions. An enhancement technique...
Effectively finding and removing the captions embedded in video images and restoring the covered background have great significance on video reuse. In this paper, considering the captions color and edge features, we first present a method of caption extraction based on the color edge detector and connected domain features analysis, which can reduce the false detection rate when the gray scale of text...
This research aims to study about developing and demonstrating the low-cost and portable light absorbance measurement device for using and learning in analytical experiments. The spectrophotometer is important for analytical chemistry to measure the concentration of solution. In chemical analysis, the UV-spectrophotometer is used to measure the solution for collecting the light absorbance from the...
Currency duplication also known as counterfeit currency is a vulnerable threat on economy. It is now a common phenomenon due to advanced printing and scanning technology. Bangladesh has been facing serious problem by the increasing rate of fake notes in the market. To get rid of this problem various fake note detection methods are available around the world and most of these are hardware based and...
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