The monitoring of vegetation near high-voltage transmission power lines and poles is tedious. Blackouts present a huge challenge to power distribution companies and often occur due to tree growth in hilly and rural areas. There are numerous methods of monitoring hazardous overgrowth that are expensive and time-consuming. Accurate estimation of tree and vegetation heights near power poles can prevent the disruption of power transmission in vulnerable zones. This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. The proposed CNN extracts and classifies features by employing convolutional pooling inputs to fully connected data layers that capture prominent features from stereo image patches. Unmanned aerial vehicle or satellite stereo image datasets can thus provide a feasible and cost-effective approach that identifies threat levels based on height and distance estimations of hazardous vegetation and other objects. Results were compared with extant disparity map estimation techniques, such as graph cut, dynamic programming, belief propagation, and area-based methods. The proposed method achieved an accuracy rate of 90%.
In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis (PCA) has been used to reduce the feature vector length. The reduction of feature length, further increases the performance of this technique. The proposed technique has been tested on self recorded dataset and gives accuracy of 97.87%. We believe that this method can help to reduce a significant number of motorcycle accidents.
The number of vehicle accidents is rapidly increasing and causing significant economic losses in many countries. According to the World Health Organization, road accidents will become the fifth major cause of death by the year 2030. To minimize these accidents different types of collision warning systems have been proposed for motor vehicle drivers. These systems can early detect and warn the drivers about the potential danger, up to a certain accuracy. Many researchers study the effectiveness of these systems by using different methods, including Electroencephalography (EEG). From the literature review, it has been observed that, these systems increase the drivers' response and can help to minimize the accidents that may occur due to drivers unconsciousness. For these collision warning systems, tactile early warnings are found more effective as compared to the auditory and visual early warnings. This review also highlights the areas, where further research can be performed to fully analyze the collision warning system. For example, some contradictions are found among researchers, about these systems' performance for drivers within different age groups. Similarly, most of the EEG studies focus on the front collision warning systems and only give beep sound to alert the drivers. Therefore, EEG study can be performed for the rear end collision warning systems, against proper auditory warning messages which indicate the types of hazards. This EEG study will help to design more friendly collision warning system and may save many lives.
Fractional pixel motion estimation (ME) is required to achieve more accurate motion vectors and higher compression efficiency. This results in an increase in the computational complexity of the ME process because of additional computational overheads such as interpolation and fractional pixel search. Fast algorithms for fractional ME in H.264/AVC are presented. To reduce the complexity of fractional pixel ME, unimodal error surface assumption is used to check only some points in the fractional pixel search window. The proposed algorithm employs motion prediction, directional quadrant and point-based search pattern and early termination to speed up the process. Hence, the proposed algorithm efficiently explores the neighborhood of integer pixel based on high correlation that exists between the neighboring fractional pixels and unimodal property of error surface. The proposed search pattern and early termination reduce computational time by almost 8% to 18% as compared to the hierarchical fractional pixel algorithm employed in the reference software with a negligible degradation in video quality and negligible increase in bit rate.
The objective of the 3D shape estimation from focus is to estimate depth map of the scene or object based on best focus
points from camera lens. In shape from focus (SFF), the measure of focus - sharpness - is the crucial part for final 3D
shape estimation. However the noise imposed during image acquisition process by imaging system prevents exact focus
measure. The traditional noise filters remove not only noise but also sharpness information. In this paper, mean shift
algorithm was applied to remove noise imposed by the imaging process while minimizing loss of informative edges.
Experimental results show that the mean shift algorithm can be applied before computing focus measure from image
sequence corrupted by Gaussian noise and Impulse noise. Applying mean shift filtering before computing focus measure
is promising in case the noise type during image acquisition is not known.
The objective of 3D shape recovery using focus is to estimate depth map of the scene or object based on best focus points
from camera lens. In Shape From Focus (SFF), the measure of
focus - sharpness - is the crucial part for final 3D shape
estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of
the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for
curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a
new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that
have the same initial depth as of the center pixel and then it
re-adjusts the center value accordingly. Experimental results
show that the proposed technique generates better shape and takes less computation time in comparison to previous SFF
methods based on Focused Image Surface (FIS) and dynamic programming.
Estimation of surface roughness is an important parameter for many applications including optics, polymers,
semiconductor etc. In this paper, we propose to estimate surface roughness using one of the 3D shape recovery optical
passive methods, i.e., shape from focus. Three-dimensional shape recovery from one or multiple observations is a
challenging problem of computer vision. The objective of shape from focus is to calculate the depth map. That depth
map can further be used in techniques and algorithms leading to recovery of three dimensional structure of object which
is required in many high level vision applications. The same depth map can also be used for surface roughness
estimation. One of the requirements, of researchers is to quickly compare the samples being fabricated based on various
measures including surface roughness. However, the high cost involved in estimation of surface roughness limits its
extensive and exhaustive usage. Therefore, we propose an inexpensive and fast method based on Shape From Focus
(SFF). We use two microscopic test objects, i.e., coin and TFT-LCD cell for estimating the surface roughness.
Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new focus measure for calculation of depth map. That depth map can further be used in techniques and algorithms leading to recovery of three dimensional structure of object which is required in many high level vision applications. The focus measure presented has shown robustness in presence of noise as compared to the earlier focus measures. This new focus measure is based on an optical transfer function using Discrete Cosine Transform and its results are compared with the earlier focus measures including Sum of Modified Laplacian (SML) and Tenenbaum focus measures. With this new focus measure, the results without any noise are almost similar in nature to the earlier focus measures however drastic improvement is observed with respect to others in the presence of noise. The proposed focus measure is applied on a test image, on a sequence of 97 simulated cone images and on a sequence of 97 real cone images. The images were added with the Gaussian noise which arises due to factors such as electronic circuit noise and sensor noise due to poor illumination and/or high temperature.